CN116198522B - Unmanned mining card transverse and vertical coupling hierarchical control method for complex mining area working conditions - Google Patents
Unmanned mining card transverse and vertical coupling hierarchical control method for complex mining area working conditions Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
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- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
- B60W30/02—Control of vehicle driving stability
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W2050/0001—Details of the control system
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- B60W2520/00—Input parameters relating to overall vehicle dynamics
- B60W2520/26—Wheel slip
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2552/00—Input parameters relating to infrastructure
- B60W2552/35—Road bumpiness, e.g. potholes
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
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Abstract
The invention discloses an unmanned mining card transverse and vertical coupling hierarchical control method for complex mining area working conditions, which comprises the following steps: step 1, establishing an unmanned mine card fourteen-degree-of-freedom vehicle dynamics model as a reference model; step 2, dynamically identifying dynamic parameters of the unmanned mine truck under the complex road working condition of the mine area by using a vehicle-mounted sensor; step 3, carrying out mode division on the driving working conditions according to the road parameters obtained in the step 2 and the real-time dynamic state parameters of the unmanned mining cards; step 4, designing an unmanned mining card transverse and vertical coupling intelligent hierarchical controller aiming at a generalized control target of transverse movement and vertical movement; and 5, updating the vehicle state, evaluating the control effect, and further performing corresponding intervention operation on the unmanned mine card until the path tracking is finished. The unmanned mining card path tracking control method provided by the invention effectively improves the path tracking precision of the unmanned mining card under the working condition of a complex mining area, and improves the efficiency and the safety of the unmanned mining card.
Description
Technical Field
The invention relates to the technical field of intelligent vehicle motion control, in particular to an unmanned mining card transverse and vertical coupling hierarchical control method for complex mining area working conditions.
Background
The unmanned mining truck is an important transportation device for modern mines, and has important significance for improving the production efficiency and the safety of the mine industry. Path tracking control is a key technology for realizing unmanned mining card. The path tracking research aims at ensuring the running safety and riding comfort, and the vehicle is driven according to a pre-planned path by controlling a steering method. The aim of path tracking is to accurately track the path by eliminating the angular deviation and the transverse deviation of the actual position and the expected position of the vehicle in the running process. The road environment of the mining area is complex, random road surface excitation is more, the road surface adhesion coefficient is easy to mutate, uncertainty is caused, and therefore, the conventional path tracking control method is not suitable for unmanned mining cards, and improvement is required.
At present, related research on unmanned mine card path tracking is mainly performed aiming at working conditions of a fixed route and a single environment, and unmanned mine card research aiming at working conditions of a complex mining area is less. The order of the mining card dynamics model adopted by the existing research is low, and the mining card dynamics model is not applicable because of larger errors under the complex working condition of a mining area.
Disclosure of Invention
In view of the above, in order to solve the technical problems that the unmanned mining card research aiming at the complex mining area working condition in the prior art is less, the adopted mining card dynamics model has lower order, and under the complex mining area working condition, a larger error exists, and the unmanned mining card transversal and vertical coupling hierarchical control method for the complex mining area working condition is provided, the unmanned mining card path tracking precision and the unmanned mining card path tracking stability can be improved, and therefore the unmanned mining card efficiency and the unmanned mining card safety are improved.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a transverse and vertical coupling hierarchical control method for unmanned mining cards under complex mining area working conditions comprises the following steps:
step 1), establishing an unmanned mining card fourteen-degree-of-freedom whole vehicle dynamics reference model;
step 2), dynamically identifying dynamic parameters of the unmanned mine truck under the complex road working condition of the mine area by using a vehicle-mounted sensor;
step 3), carrying out mode division on the driving working conditions according to the road parameters in the complex road working conditions and the real-time dynamic state parameters in the dynamic parameters of the unmanned mining cards obtained in the step 2;
step 4), designing an unmanned mining card transverse coupling intelligent hierarchical controller and a vertical coupling intelligent hierarchical controller aiming at generalized control targets of transverse movement and vertical movement;
and 5) updating the vehicle state, evaluating the control effect, and performing corresponding intervention operation on the unmanned mine card until the path tracking is finished.
Preferably, step 1) establishes the unmanned mining card fourteen degrees of freedom whole vehicle dynamics reference model specifically as follows:
preferably, in step 2), a kalman filter observer is used to observe the tire slip rate in the dynamic parameters of the unmanned mine truck and the road surface unevenness power spectral density in the complex road conditions.
Preferably, the estimation of the tire slip rate is as follows:
from the slip definition it follows that: in (1) the->For each wheel effective radius, obtained from the tire parameter approximation,for each tire rotational angular velocity, obtained by a wheel speed sensor, +.>The longitudinal speed of each wheel center is obtained by a speed sensor; selecting the sliding rate as the state variable of the system, namely +.>Selecting longitudinal acceleration, lateral acceleration and yaw acceleration of the vehicle body, i.e.)>Road adhesion coefficient input is ∈>If the system process noise (w) and the measurement noise (v) are considered, the continuous random state equation can be expressed as: />In the method, in the process of the invention,is a system matrix; x (t) is a state variable, U (t) is a known external inputA variable is entered; discretizing a continuous random system with a sampling period of Ts, the discrete control process of the system can be expressed as follows:where k=t/Ts, a (k) = (i+tsa (t)), B (k) =tsb (t), and I is a unitary matrix. Let w (k), v (k) be gaussian white noise independent of each other and subject to normal distribution, i.e.:
w(k)~N(0，Q(k))
v(k)~N(0，R(k))
wherein Q (k) is the covariance of the process noise, and R (k) is the covariance of the measurement noise;
the workflow of the Kalman filter mainly comprises time update (prediction) and measurement update (correction)
The specific state observation process is as follows:
first, the system state update, i.e. predicting the system state at the next moment by using the system process model, can be expressed as:in (1) the->Predictive value representing the current time (k),>representing an optimal estimate of the last time instant (k-1); subsequently, the update corresponds toCan be expressed as: />Wherein P (k|k-1) is the one corresponding to +.>P (k-1|k-1) is corresponding to +.>Is a covariance of (c).
Then, the predicted value is combinedObservation value +.>The obtaining of the optimal estimated value at the current time can be expressed as: />Wherein K is g For the Kalman gain, this can be expressed as: />Finally, in order to keep the system state updated, the current time state also needs to be updated +.>Can be expressed as:wherein (1)>Is an identity matrix. When the system enters (k+1), P (k|k) isCovariance P (k-1|k-1), so that the filtering algorithm can proceed autoregressively;
classical power spectrum estimation is adopted for estimating the power spectrum density of the road surface unevenness, and random signals are inputIs->Point sample value +.>Is regarded as an energy-limited signal and fourier transformed to give +.>On the basis of which the square of the amplitude is taken and +.>As->Real power spectrum->Is an estimate of (1), namely: />The method comprises the steps of carrying out a first treatment on the surface of the Selecting the vehicle body displacement, the tyre dynamic deflection, the vehicle body vertical speed and the wheel axle vertical speed as the state variables of the system, namelyThe acceleration of the vehicle body and the vertical jumping acceleration of the tyre are selected as measurement variables, namelyRoad surface input is +.>If the system process noise (w) and the measurement noise (v) are considered, the continuous random state equation can be expressed as: />In (1) the->Is a system matrix; x (t) is a state variable, U (t) is a known external input variable; discretizing a continuous random system with a sampling period of Ts, the discrete control process of the system can be expressed as follows:where k=t/Ts, a (k) = (i+tsa (t)), B (k) =tsb (t), I is a unitary matrix;
let w (k), v (k) be gaussian white noise independent of each other and subject to normal distribution, i.e.: w (k) to N (0, Q (k))
v(k)~N(0，R(k))
Wherein Q (k) is the covariance of the process noise, and R (k) is the covariance of the measurement noise;
the workflow of the Kalman filter mainly comprises two parts of time update (prediction) and measurement update (correction), and the specific state observation process is as follows: first, the system state update, i.e. predicting the system state at the next moment by using the system process model, can be expressed as:in the method, in the process of the invention,predictive value representing the current time (k),>representing an optimal estimate of the last time instant (k-1); subsequently, the update corresponds to +.>Can be expressed as:wherein P (k|k-1) is the one corresponding to +.>P (k-1|k-1) is corresponding to +.>Is a covariance of (2); then, combine the predictive value +.>Observation value +.>The obtaining of the optimal estimated value at the current time can be expressed as:wherein K is g For the Kalman gain, this can be expressed as:finally, in order to keep the system state updated, the current time state also needs to be updated +.>Can be expressed as: />The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Is an identity matrix. When the system enters (k+1), P (k|k) is +.>Covariance P (k-1|k-1) so that the filtering algorithm continues autoregressively.
Preferably, the current wheel slip rate and the road surface unevenness power spectrum density are obtained by the step 2), and the slip rate coefficient is defined after dimensionless treatmentRoad surface unevenness coefficient->Comparing the control modes with a built-in road working condition parameter identification module to activate different control modes; />，/>Wherein (1)>For the maximum slip rate to be reached,for minimum slip, +.>The slip rate at the current moment;/>for maximum road power spectral density, +.>For minimum road power spectral density, < >>The road power spectral density at the current moment. Preferably, the driving scenario of the unmanned mining card road recognition module is divided into: common road surface, dry-wet mixed road surface, convex road surface and subsided road surface.
Preferably, three basic control stages in the hierarchical control method are respectively an organization stage, a coordination stage and an execution stage; wherein the tissue level is mainly based on the relative position information of the vehicle and the expected track, and the lateral acceleration of the vehicleAnd yaw rateRoad curvature->And preset the driving speed of the unmanned mine card +.>Obtaining a front wheel corner expected by a generalized control target of the unmanned mining card transverse and vertical coupling intelligent hierarchical control system>And control force of active suspension->The method comprises the steps of carrying out a first treatment on the surface of the The coordination level controller is mainly used for distributing tasks to all subsystems according to a control target of the path tracking control system, the running state of the unmanned mining card and the chassis cooperative control logic; the execution stage receives task targets distributed by the coordination stage, and realizes the requirement of the coordination stage according to the control strategy and the system characteristics of each subsystem, and finally realizesIntelligent car path tracking.
Preferably, the pre-aiming error model of the unmanned mining card established by the relative position of the unmanned mining card and the expected tracking path receives the transverse speed output by the unmanned mining card dynamics model in the step 1)And yaw rate>External input road reference curvature +.>And unmanned mining truck driving speed->As input to the pre-aiming error model, the lateral displacement deviation +.>And lateral orientation deviation->To direct the control of lateral movement, expressed as follows: />Wherein: />Is the lateral displacement deviation; />Is the transverse speed; />Is the longitudinal vehicle speed; />Is the transverse azimuth deviation; />Is yaw rate; />Is the curvature of the road; />For pretightening distance, & gt>For the rate of change of the lateral displacement deviation +.>Is the transverse azimuth deviation change rate; and carrying out dimensionless treatment on the transverse displacement deviation and the azimuth deviation, wherein the dimensionless treatment comprises the following formula: />；/>In order to be a deviation of the maximum displacement in the lateral direction,is the transverse minimum displacement deviation; />For the maximum lateral deviation, +.>Is the lateral minimum azimuth deviation.
Definition of integrated bias：/>In the method, in the process of the invention,λis a weight coefficient; preferably, the lateral movement controller in the tissue stage outputs the desired front wheel angle +.>The fuzzy sliding film controller is designed, and the design of the fuzzy sliding film controller comprises three parts in total: an equivalent controller, a switching controller and a fuzzy controller; an equivalent controller; defining a switching function: />Wherein S is a switching function, +.>Is a sliding mode surface coefficient->Is the error change rate; in order to meet the corresponding speed of the controller and reduce the influence of buffeting on the fuzzy synovial membrane controller, the comprehensive consideration selects the index approach rate: />Wherein s is the above-mentioned switching function, sgn(s) is a sign function,/->，/>Is approach rate parameter->For->Derivation to obtain equivalent control->The method comprises the steps of carrying out a first treatment on the surface of the The handover controller defines a handover controller: /> For switching the control coefficient function, +.>For approach rate parameter, ++>Is a sign function; substituting the vehicle transverse dynamics model to obtain ∈>The final fuzzy synovial membrane controller is as follows: />In the fuzzy sliding mode controller, the input of the fuzzy controller is a sliding mode surface +.>In the control process, the fuzzy control adjusts the equivalent control part and the switching control part in the sliding mode controller according to the sliding mode surface state, and accordingly, the control rule is obtained as follows: />After defuzzification, the fuzzy controller is: finally, the fuzzy sliding film controller outputs the front wheel angle +.>。
A vertical motion controller in a tissue stage outputs a desired control force of a force generator of an active suspensionThe fuzzy sliding film controller is designed, and the design of the fuzzy sliding film controller comprises three parts in total: an equivalent controller, a switching controller and a fuzzy controller; the equivalent controller defines a switching function: />In->Is a sliding mode surface coefficient->For the roll angle error of the car body,/>Is the roll angle error change rate; in order to meet the corresponding speed of the controller and reduce the influence of buffeting on the controller, the comprehensive consideration selects the index approach rate: />In (1) the->，/>Is approach rate parameter->For->Deriving, taking equivalent control in dynamics model>The method comprises the steps of carrying out a first treatment on the surface of the The handover controller defines a handover controller: />Substituting the vehicle transverse dynamics model to obtain ∈>The final sliding mode controller is as follows: />Fuzzy controller in the fuzzy sliding mode controller, the input of the fuzzy controller is sliding mode surface +.>In the control process, the fuzzy control adjusts the equivalent control part and the switching control part in the sliding mode controller according to the sliding mode surface state, namely when the system state is far away from the sliding mode surface, the switching control is required to be added through the fuzzy control, and when the system is close to the sliding mode surface, the original equivalent part is maintained, so that the control rule is obtained as follows: />After defuzzification, the fuzzy controller is: /> The coordination level cooperatively controls the suspension system and the steering system according to the transverse and vertical coordination control logic of the unmanned mine truck chassis, wherein the control logic is as follows:
in the table of the present invention,、/>desired front wheel steering angle and desired suspension effort for tissue level output; steering-by-wire system AFS in the execution stage for controlling steering receives the steering angle output of the coordination stage>The active suspension system CDC controlling the attitude of the vehicle body receives the control force of the force generator of the coordination level +.>。
Preferably, step 5) is specifically: the curvature is optimized based on a genetic algorithm, the optimization process is to find a target path closest to an expected path for tracking, and the minimum problem is solved, so that the curvature is required to be subjected to scale conversion:in (1) the->Evaluation of the index as a fitness functionFThe design process of (2) is as follows: />Wherein ρ is 2 ，ρ 1 D for optimizing path curvature and desired tracking path curvature, respectively e2 In order to correspond to the lateral deviation of the path, the optimized curvature in the fitness function should also meet the boundary condition requirements of the vehicle body stability: />Vehicle body roll angle +.>Lateral acceleration->Dynamic vertical load of wheel->Calculating roll moment, reversely outputting control force through active suspension actuator to enable roll angle of vehicle body>Decay to approximately 0; wherein the roll moment mainly comprises: roll moment due to centrifugal force of suspended mass>Roll moment due to gravity of suspended mass>The vertical load is transferred at the left and right wheel loads at the time of rolling, and a load transfer moment is generated +.>:In->Is sprung mass, < >>Is centroid lateral acceleration>Is the height of the mass centerFor roll angle of car body->For the track, ->Gravitational acceleration; wherein (1)>The method comprises the following steps: 。
in the middle ofFor the quality of the whole car, the weight of the whole car is increased>For centroid to front axis distance +.>Distance from center of mass to rear axle +.>Is centroid longitudinal acceleration->Is centroid lateral acceleration>Is the centroid height; compared with the prior art, the invention has the following beneficial effects:
1) The invention establishes a high-order mine card dynamics model, and the Kalman filter observer performs parameter identification to improve the model precision, thereby improving the path tracking precision of the complex environment of the mining area;
2) The invention adopts the transverse and vertical coupling intelligent hierarchical controller to realize the transverse and vertical cooperative control, thereby greatly improving the path tracking control effect.
Drawings
FIG. 1 is a flowchart of a Kalman filtering algorithm; FIG. 2 is a pre-aiming error model diagram; FIG. 3 is a logic diagram of a curvature optimization algorithm; fig. 4 is an unmanned mining card path tracking control logic roadmap.
Description of the embodiments
Based on the related research of the existing unmanned mine card path tracking in the prior art, the unmanned mine card path tracking method is mainly used for researching working conditions of a fixed route and a single environment, and the unmanned mine card research on the working conditions of a complex mining area is less. The invention provides an unmanned mining card transverse and vertical coupling hierarchical control method for a complex mining area working condition (the following path tracking is an action process, and an expected path is a target to be approximated in the path tracking process), which specifically comprises the following steps: step 1), establishing an unmanned mining card fourteen-degree-of-freedom whole vehicle dynamics reference model, which is specifically as follows:
longitudinal movement:
lateral movement:
yaw motion:
roll motion:
pitching motion:
vertical movement of the vehicle body:
wheel vertical movement:
wheel rolling:
wherein:、/>、/>、/>、/>、/>、/>the longitudinal speed, lateral speed, body roll angle, yaw angle, body pitch angle, front wheel steering angle and yaw rate of the vehicle, respectively. />、/>The longitudinal and transverse forces of each tire are respectively determined.Suspension forces that are the points of attachment of the body to the suspension; />、/>、/>、/>、/>、/>、/>、/>、/>Respectively, vehicles are wound aroundAxle moment of inertia, vehicle winding->Axle moment of inertia, vehicle winding->Axle moment of inertia, roll center height, center of mass to roll center height, pitch center height, center of mass to front axle distance, center of mass to rear axle distance, 1/2 of front and rear wheel track.
Step 2), dynamically identifying the complex road working condition of the mining area and the dynamic parameters of the unmanned mining card by using a vehicle-mounted sensor, and preferably observing the tire slip rate in the dynamic parameters of the unmanned mining card and the road surface unevenness power spectrum density in the complex road working condition by using a Kalman filter observer, wherein the steps are as follows: the tire slip rate is estimated as follows: from the slip definition it follows that: in (1) the->For each wheel effective radius, approximately obtained from the tire parameters, ->For each tire rotational angular velocity, obtained by a wheel speed sensor, +.>The longitudinal speed of each wheel center is obtained by a speed sensor; selecting the slip rate as a state variable of the system, i.eSelecting longitudinal acceleration, lateral acceleration and yaw acceleration of the vehicle body, i.e.)>Road adhesion coefficient input is ∈>If the system process noise (w) and the measurement noise (v) are considered, the continuous random state equation can be expressed as:/>in (1) the->Is a system matrix; x (t) is a state variable, U (t) is a known external input variable; discretizing a continuous random system with a sampling period of Ts, the discrete control process of the system can be expressed as follows: />Where k=t/Ts, a (k) = (i+tsa (t)), B (k) =tsb (t), and I is a unitary matrix. Let w (k), v (k) be gaussian white noise independent of each other and subject to normal distribution, i.e.: w (k) -N (0, Q (k)) v (k) -N (0, R (k)) wherein Q (k) is the covariance of process noise and R (k) is the covariance of measurement noise; the workflow of the Kalman filter mainly comprises two parts of time update (prediction) and measurement update (correction), and the specific state observation process is as follows: first, the system state update, i.e. predicting the system state at the next moment by using the system process model, can be expressed as:in (1) the->Predictive value representing the current time (k),>representing an optimal estimate of the last time instant (k-1); subsequently, the update corresponds toCan be expressed as: />Wherein P (k|k-1) is the one corresponding to +.>P (k-1|k-1) is corresponding to +.>Is a covariance of (c).
Then, the predicted value is combinedObservation value +.>The obtaining of the optimal estimated value at the current time can be expressed as: />Wherein K is g For the Kalman gain, this can be expressed as: />Finally, in order to keep the system state updated, the current time state also needs to be updated +.>Can be expressed as:wherein (1)>Is an identity matrix. When the system enters (k+1), P (k|k) isCovariance P (k-1|k-1), so that the filtering algorithm can proceed autoregressively; the road surface unevenness power spectrum density estimation adopts classical power spectrum estimation, and random signals are input +.>Is->Point sample value +.>Seen as an energy limited signal, and fourier transformed,obtain->On the basis of which the square of the amplitude is taken and +.>As->Real power spectrum->Is an estimate of (1), namely: />Selecting the vehicle body displacement, the tyre dynamic deflection, the vehicle body vertical speed and the wheel axle vertical speed as the state variables of the system, namely +.>The acceleration of the vehicle body and the vertical jumping acceleration of the tyre are selected as measurement variables, namely +.>Road surface input is +.>If the system process noise (w) and the measurement noise (v) are considered, the continuous random state equation can be expressed as:in (1) the->Is a system matrix; x (t) is a state variable, U (t) is a known external input variable; discretizing a continuous random system with a sampling period of Ts, the discrete control process of the system can be expressed as follows: />Where k=t/Ts, a (k) = (i+tsa (t)), B (k) =tsb (t), I is a unitary matrix; assuming w (k), v (k) are independent of each other and obey normalDistributed gaussian white noise, namely: w (k) -N (0, Q (k)) v (k) -N (0, R (k)) wherein Q (k) is the covariance of process noise and R (k) is the covariance of measurement noise; the workflow of the Kalman filter mainly comprises two parts of time update (prediction) and measurement update (correction), and the specific state observation process is as follows: first, the system state update, i.e. predicting the system state at the next moment by using the system process model, can be expressed as:in (1) the->Predictive value representing the current time (k),>representing an optimal estimate of the last time instant (k-1); subsequently, the update corresponds toCan be expressed as: />Wherein P (k|k-1) is the one corresponding to +.>P (k-1|k-1) is corresponding to +.>Is a covariance of (2); then, combine the predictive value +.>Observation value +.>The obtaining of the optimal estimated value at the current time can be expressed as:wherein K is g For the Kalman gain, this can be expressed as:finally, in order to keep the system state updated, the current time state also needs to be updated +.>Can be expressed as: />Wherein (1)>Is a unit matrix; when the system enters (k+1), P (k|k) is +.>Covariance P (k-1|k-1) so that the filtering algorithm continues autoregressively.
And 3) carrying out mode division on the driving working conditions according to the road parameters obtained in the step 2) and the real-time dynamic state parameters of the unmanned mining cards. Obtaining the current wheel slip rate and road surface unevenness power spectrum density by the step 2), and defining a slip rate coefficient after dimensionless processingRoad surface unevenness coefficient->And comparing the control modes with the built-in road working condition parameter identification module, thereby activating different control modes. The driving situations of the unmanned mining card road recognition module are divided into four major categories: 1. ordinary road surfaces; 2. dry-wet mixed road surface; 3. a bump road surface; 4. sedimentation road surface-> Wherein,,for maximum slip, +.>For minimum slip, +.>For the slip rate at the present moment>For maximum road power spectral density, +.>For minimum road power spectral density, < >>The road power spectral density at the current moment.
Step 4), designing an unmanned mining card transverse and vertical coupling intelligent hierarchical controller aiming at a generalized control target of transverse movement and vertical movement, wherein the intelligent hierarchical controller comprises the following specific steps: the three basic control stages of intelligent hierarchical control are respectively an organization stage, a coordination stage and an execution stage. Wherein the tissue level is based on the relative position information of the vehicle and the expected track, and the lateral acceleration of the vehicleAnd yaw rateRoad curvature->And preset the driving speed of the unmanned mine card +.>Obtaining a front wheel corner expected by a generalized control target of the unmanned mining card transverse and vertical coupling intelligent hierarchical control system>And control force of active suspension->The method comprises the steps of carrying out a first treatment on the surface of the The coordinator controller is controlled by the path tracking control systemThe target, the unmanned mining card running state and the chassis cooperative control logic are used for distributing tasks to all subsystems; the execution stage receives task targets distributed by the coordination stage, and achieves the requirements of the coordination stage according to the control strategy and the system characteristics of each subsystem, and finally achieves efficient and stable path tracking of the intelligent automobile.
Receiving the transverse speed output by the unmanned mining card dynamics model in the step 1) according to the pre-aiming error model of the unmanned mining card established according to the relative position of the unmanned mining card and the expected tracking pathAnd yaw rate>External input road reference curvature +.>And unmanned mining truck driving speed->As input to the pre-aiming error model, the lateral displacement deviation +.>And lateral orientation deviation->To guide the control of the lateral movement. The expression is as follows: />Wherein: />Is the lateral displacement deviation; />Is the transverse speed; />Is the longitudinal vehicle speed; />Is the transverse azimuth deviation; />Is yaw rate; />Is the curvature of the road; />For pretightening distance, & gt>For the rate of change of the lateral displacement deviation +.>Is the transverse azimuth deviation change rate; and carrying out dimensionless treatment on the transverse displacement deviation and the azimuth deviation, wherein the dimensionless treatment comprises the following formula:
definition of integrated bias：/>In the method, in the process of the invention,λis a weight coefficient.
To reduce the integrated offset, the traversing motion controller in the tissue stage outputs a desired front wheel steering angleThe fuzzy sliding film controller is designed, and the design of the fuzzy sliding film controller comprises three parts in total: an equivalent controller, a switching controller and a fuzzy controller.
Equivalent controller
Defining a switching function:wherein S is a switching function, +.>Is a sliding mode surface coefficient->Is the error change rate; in order to meet the corresponding speed of the controller and reduce the influence of buffeting on the controller, the comprehensive consideration selects the index approach rate: />Wherein s is the above-mentioned switching function, sgn(s) is a sign function,/->,/>Is an approach rate parameterFor->Derivation to obtain equivalent control->The method comprises the steps of carrying out a first treatment on the surface of the The handover controller defines a handover controller: for switching the control coefficient function, +.>For approach rate parameter, ++>Is a sign function; substituting the vehicle transverse dynamics model to obtain ∈>The final sliding mode controller is as follows: />In the fuzzy sliding mode controller, the input of the fuzzy controller is a sliding mode surface +.>In the control process, the fuzzy control can adjust the equivalent control part and the switching control part in the sliding mode controller according to the sliding mode surface state, namely when the system state is far away from the sliding mode surface, the switching control is required to be added through the fuzzy control, and when the system is close to the sliding mode surface, the original equivalent part is maintained, so that the control rule is obtained as follows: />After defuzzification, the fuzzy controller is: /> To achieve control of the body position, the vertical motion controller outputs the desired control force of the force generator of the active suspension in the tissue level +.>The fuzzy sliding film controller is designed, and the design of the fuzzy sliding film controller comprises three parts in total: an equivalent controller, a switching controller and a fuzzy controller;
the equivalent controller defines a switching function:in->Is a sliding mode surface coefficient->Is the roll angle error of the vehicle body,is the roll angle error change rate; in order to not only meet the corresponding speed of the controller, but also reduce the influence of buffeting on the controller,the comprehensive consideration selects the index approach rate: />In (1) the->,/>Is approach rate parameter->For->Deriving, taking equivalent control in dynamics model>The method comprises the steps of carrying out a first treatment on the surface of the The handover controller defines a handover controller:substituting the vehicle transverse dynamics model to obtain ∈>The final sliding mode controller is as follows:in the fuzzy sliding mode controller, the input of the fuzzy controller is a sliding mode surface +.>In the control process, the fuzzy control can adjust the equivalent control part and the switching control part in the sliding mode controller according to the sliding mode surface state, namely when the system state is far away from the sliding mode surface, the switching control is required to be added through the fuzzy control, and when the system is close to the sliding mode surface, the original equivalent part is maintained, so that the control rule is obtained as follows: />After defuzzification, the fuzzy controller is: /> The coordination level cooperatively controls the suspension system and the steering system according to the transverse and vertical coordination control logic of the unmanned mine truck chassis. Wherein the control logic is as follows:
in the table of the present invention,、/>desired front wheel steering angle and desired suspension effort for tissue level output; steering-by-wire system AFS in the execution stage for controlling steering receives the steering angle output of the coordination stage>The active suspension system CDC controlling the attitude of the vehicle body receives the control force of the force generator of the coordination level +.>。
And 5) updating the vehicle state, evaluating the control effect, and further performing corresponding intervention operation on the unmanned mine card until the path tracking is finished, wherein the method comprises the following steps of: the curvature optimizing system is realized through analyzing the stability of vehicle body under different conditions based on completed path tracking control system, determining the stability constraint boundary condition in optimizing process via the yaw rate, lateral acceleration and camber angle of the vehicle, and optimizing algorithm based on the known expected path curvatureAnd +.>Finding the real path nearest to the original expected path in the constraint rangeInter-driving route->The corresponding optimized desired path +.>The tracking error is input to a path tracking controller, which performs tracking control again by taking the path as a tracking path>Reduced to->The tracking precision is improved, and meanwhile stability constraint ensures that the stability of the vehicle body is changed within a reasonable range, so that the comprehensive improvement of the tracking effect is realized.
The invention adopts a method for optimizing the curvature based on a genetic algorithm. The optimization process is to find the target path closest to the expected path for tracking, which is the minimum problem, so that the target path needs to be subjected to scale conversion:in (1) the->Evaluation of the index as a fitness functionFThe design process of (2) is as follows: />Where ρ2, ρ1 are the optimized path curvature and the desired tracking path curvature, d, respectively e2 In order to correspond to the lateral deviation of the path, the optimized curvature in the fitness function should also meet the boundary condition requirements of the vehicle body stability: />Vehicle body roll angle +.>Lateral acceleration->Dynamic vertical load of wheel->Calculating the roll moment and further reversely outputting the control force through the active suspension actuator to enable the roll angle of the vehicle body to be +.>Decay to approximately 0; wherein the roll moment mainly comprises: roll moment due to centrifugal force of suspended mass>Roll moment due to gravity of suspended mass>The vertical load is transferred at the left and right wheel loads at the time of rolling, and a load transfer moment is generated +.>：/>In->Is sprung mass, < >>Is centroid lateral acceleration>Is centroid height->For roll angle of car body->For the track, ->Gravitational acceleration; wherein (1)>The method comprises the following steps: /> The control objective of the vertical control of the invention is to enable the body to leanAs infinitely approaching zero as possible, the control forces of the force generators of the individual active suspensions are inversely determined by the relationship of the load transfer and the roll angle of the vehicle body>。
The above is only a preferred embodiment of the present invention; the scope of the invention is not limited in this respect. Any person skilled in the art, within the technical scope of the present disclosure, may apply to the present invention, and the technical solution and the improvement thereof are all covered by the protection scope of the present invention.
Claims (8)
1. The unmanned mining card transverse and vertical coupling hierarchical control method for the complex mining area working conditions is characterized by comprising the following steps of:
step 1), establishing an unmanned mining card fourteen-degree-of-freedom whole vehicle dynamics reference model;
step 2), dynamically identifying dynamic parameters of the unmanned mine truck under the complex road working condition of the mine area by using a vehicle-mounted sensor;
step 3), carrying out mode division on the driving working conditions according to the road parameters in the complex road working conditions and the real-time dynamic state parameters in the dynamic parameters of the unmanned mining cards obtained in the step 2;
step 4), designing an unmanned mining card transverse coupling intelligent hierarchical controller and a vertical coupling intelligent hierarchical controller aiming at generalized control targets of transverse movement and vertical movement;
step 5), updating the vehicle state, evaluating the control effect, and performing corresponding intervention operation on the unmanned mine card until the path tracking is finished;
the three basic control stages in the hierarchical control method are respectively an organization stage, a coordination stage and an execution stage;
wherein the tissue level is mainly based on the relative position information of the vehicle and the expected track, and the lateral acceleration a of the vehicle y And yaw rate ω, road curvature ρ and preset unmanned mining truck travel speed V x Obtaining the front wheel rotation angle delta expected by the generalized control target of the unmanned mining card transverse and vertical coupling intelligent hierarchical control system f And control force F of active suspension ufl 、F url 、F ufr 、F urr ；
The coordination level controller is mainly used for distributing tasks to all subsystems according to a control target of the path tracking control system, the running state of the unmanned mining card and the chassis cooperative control logic;
the execution stage receives task targets distributed by the coordination stage, and realizes the requirement of the coordination stage according to the control strategy and the system characteristics of each subsystem, and finally realizes the path tracking of the intelligent automobile.
2. The unmanned mining truck transverse and vertical coupling hierarchical control method for the complex mining area working conditions according to claim 1, wherein in the step 2), a Kalman filter observer is used for observing the tire slip rate in the unmanned mining truck dynamics parameters and the road surface unevenness power spectral density in the complex road working conditions.
3. The unmanned mining truck transverse and vertical coupling hierarchical control method for complex mining area working conditions according to claim 2, wherein the estimation of the tire slip rate is as follows:
from the slip definition it follows that:
when in driving: r is R i ω wi ＞v i
When braking, the following steps: r is R i ω wi ＜v i
Wherein R is i For each wheel effective radius, ω is approximated by tire parameters wi For each tyre rotation angular velocity, obtained by wheel speed sensor, v i The longitudinal speed of each wheel center is obtained by a speed sensor;
the slip rate is selected as a state variable of the system, namely X (t) = [ s ] i ] T Selecting longitudinal acceleration, lateral acceleration and yaw acceleration of the vehicle body, namelyRoad adhesion coefficient input is->If the system process noise (w) and the measurement noise (v) are considered, the continuous random state equation can be expressed as:
Y(t)＝C(t)X(t)+D(t)U(t)+v(t)
wherein A (t), B (t), C (t), D (t) are system matrixes; x (t) is a state variable, U (t) is a known external input variable;
discretizing a continuous random system with a sampling period of Ts, the discrete control process of the system can be expressed as follows:
X(k)＝A(k-1)X(k-1)+B(k-1)U(k-1)+B * (k-1)U * (k-1)+w(k-1)
Y(k-1)＝C(k-1)X(k-1)+D(k-1)U(k-1)+D * (k-1)U * (k-1)+v(k-1)
where k=t/Ts, a (k) = (i+tsa (t)), B (k) =tsb (t), I is a unitary matrix;
let w (k), v (k) be gaussian white noise independent of each other and subject to normal distribution, i.e.:
w(k)～N(0，Q(k))
v(k)～N(0，R(k))
wherein Q (k) is the covariance of the process noise, and R (k) is the covariance of the measurement noise;
the work flow of the Kalman filter mainly comprises two parts of time updating and measurement updating, and the specific state observation process is as follows:
first, the system state update, i.e. predicting the system state at the next moment by using the system process model, can be expressed as:
in the method, in the process of the invention,predictive value representing the current time (k),>representing an optimal estimate of the last time instant (k-1);
subsequently, the update corresponds toCan be expressed as:
P(k|k-1)＝A(k-1)P(k-1|k-1)A(k-1) T +Q(k-1)
wherein P (k|k-1) corresponds toP (k-1|k-1) is corresponding to +.>Is a covariance of (2);
then, the predicted value is combinedObservation value +.>The obtaining of the optimal estimated value at the current time can be expressed as:
wherein K is g For the Kalman gain, this can be expressed as:
K g (k)＝P(k|k-1)C(K) T /(C(K)P(k|k-1)C(K) T +R(k))
finally, in order to continuously update the system state, the current state also needs to be updatedCan be expressed as:
P(k|k)＝(I-K g (k)C(k))P(k|k-1)
wherein I is an identity matrix;
when the system enters (k+1), P (k|k) isCovariance P (k-1|k-1), so that the filtering algorithm can proceed autoregressively;
the road surface unevenness power spectrum density estimation adopts classical power spectrum estimation, and N point sample value x of input random signal x (N) is calculated N (n) treating as an energy-limited signal and performing a Fourier transform to obtain X N (e jw ) Taking the square of the amplitude and taking N as x (N) real power spectrum P (e jw ) Is an estimate of (1), namely:
selecting the vehicle body displacement, the tyre dynamic deflection, the vehicle body vertical speed and the wheel axle vertical speed as the state variables of the system, namelyThe acceleration of the vehicle body and the vertical jumping acceleration of the tyre are selected as measurement variables, namelyRoad surface input is U (t) = [ z r (t)]If the system process noise (w) and the measurement noise (v) are considered, the continuous random state equation can be expressed as:
Y(t)＝C(t)X(t)+D(t)U(t)+v(t)
wherein A (t), B (t), C (t), D (t) are system matrixes; x (t) is a state variable, U (t) is a known external input variable;
discretizing a continuous random system with a sampling period of Ts, the discrete control process of the system can be expressed as follows:
X(k)＝A(k-1)X(k-1)+B(k-1)U(k-1)+B * (k-1)U * (k-1)+w(k-1)
Y(k-1)＝C(k-1)X(k-1)+D(k-1)U(k-1)+D * (k-1)U * (k-1)+v(k-1)
where k=t/Ts, a (k) = (i+tsa (t)), B (k) =tsb (t), I is a unitary matrix;
let w (k), v (k) be gaussian white noise independent of each other and subject to normal distribution, i.e.:
w(k)～N(0，Q(k))
v(k)～N(0，R(k))
wherein Q (k) is the covariance of the process noise, and R (k) is the covariance of the measurement noise;
the work flow of the Kalman filter mainly comprises two parts of time updating and measurement updating, and the specific state observation process is as follows:
first, the system state update, i.e. predicting the system state at the next moment by using the system process model, can be expressed as:
in the method, in the process of the invention,predictive value representing the current time (k),>representing an optimal estimate of the last time instant (k-1);
subsequently, the update corresponds toCan be expressed as:
P(k|k-1)＝A(k-1)P(k-1|k-1)A(k-1) T +Q(k-1)
wherein P (k|k-1) corresponds toP (k-1|k-1) is corresponding to +.>Is a covariance of (2);
then, the predicted value is combinedObservation value +.>Acquiring currentThe optimal estimate of time of day can be expressed as:
wherein K is g For the Kalman gain, this can be expressed as:
K g (k)＝P(k|k-1)C(K) T /(C(K)P(k|k-1)C(K) T +R(k))
finally, in order to continuously update the system state, the current state also needs to be updatedCan be expressed as:
P(k|k)＝(I-K g (k)C(k))P(k|k-1)
wherein I is an identity matrix;
when the system enters (k+1), P (k|k) isCovariance P (k-1|k-1) so that the filtering algorithm continues autoregressively.
4. The unmanned mining truck transverse and vertical coupling hierarchical control method for complex mining area working conditions according to claim 3, wherein the current wheel slip rate and road surface unevenness power spectrum density are obtained in the step 2), and a slip rate coefficient K is defined after dimensionless processing 1 Road surface unevenness coefficient K 2 Comparing the control modes with a built-in road working condition parameter identification module to activate different control modes;
wherein S is max For maximum slip ratio, S min S is the sliding rate at the current moment; g max G is the maximum road surface power spectral density min And G is the road power spectral density at the current moment.
5. The unmanned mining truck transverse and vertical coupling hierarchical control method for the complex mining area working condition according to claim 4, wherein the driving scene of the unmanned mining truck road identification module is divided into: common road surface, dry-wet mixed road surface, convex road surface and subsidence road surface.
6. The unmanned mining card transverse and vertical coupling hierarchical control method for complex mining area working conditions according to claim 1, wherein the unmanned mining card pre-aiming error model established by the relative positions of the unmanned mining card and the expected tracking path receives the transverse speed v output by the unmanned mining card dynamics model in the step 1) y And yaw rate ω, and externally input road reference curvature ρ and unmanned mining truck travel vehicle speed v x As an input of the pre-aiming error model, outputting the transverse displacement deviation y e And lateral azimuth deviation epsilon to guide the control of lateral motion, the expression is as follows:
wherein: y is e Is the lateral displacement deviation; v y Is the transverse speed; v x Is the longitudinal vehicle speed; epsilon is the transverse azimuth deviation; w (w) c Is yaw rate; ρ is the road curvature; x is x e For the pre-aiming distance,for the rate of change of the lateral displacement deviation +.>Is the transverse azimuth deviation change rate;
and carrying out dimensionless treatment on the transverse displacement deviation and the azimuth deviation, wherein the dimensionless treatment comprises the following formula:
y emax for the maximum displacement deviation in the transverse direction, y emin Is the transverse minimum displacement deviation; epsilon max Epsilon as the transverse maximum azimuth deviation min Is the lateral minimum azimuth deviation;
defining a comprehensive deviation E:
where λ is a weight coefficient.
7. The unmanned mining truck transverse and vertical coupling hierarchical control method for the complex mining area working condition according to claim 1, which is characterized in that,
the lateral motion controller in the tissue stage outputs a desired front wheel steering angle delta f The fuzzy sliding film controller is designed, and the design of the fuzzy sliding film controller comprises three parts in total: an equivalent controller, a switching controller and a fuzzy controller;
equivalent controller
Defining a switching function:
wherein S is a switching function, c is a sliding mode surface coefficient,is the error change rate;
in order to meet the corresponding speed of the controller and reduce the influence of buffeting on the fuzzy synovial membrane controller, the comprehensive consideration selects the index approach rate:
sl＝-ηsgn(s)-ks
wherein S is the above-mentioned switching function, sgn (S) is a sign function, eta, k is an approach rate parameter (eta > 0, k > 0), and S is derived to obtain an equivalent control delta eq ；
Switching controller
Defining a switching controller:
g (x, t) is a switching control coefficient function, eta is an approach rate parameter, and sgn(s) is a sign function;
substituting the model of the transverse dynamics of the vehicle to obtain delta sw The final fuzzy synovial membrane controller is as follows:
δ＝δ eq +δ sw
in the fuzzy sliding mode controller, the input of the fuzzy controller is a sliding mode surface s (t), and in the control process, the fuzzy control adjusts an equivalent control part and a switching control part in the sliding mode controller according to the sliding mode surface state, so that the control rule is obtained as follows:
If s(t)is ZO thenδisδ eq
If s(t)is NZ thenδisδ eq +δ sw
after defuzzification, the fuzzy controller is:
μ ZO (s)+μ NZ (s)＝1
finally, the fuzzy sliding film controller outputs the front wheel rotation angle delta fL ；
The vertical motion controller in the tissue stage outputs the desired control force F of the force generator of the active suspension ufl 、F url 、F ufr 、F urr The fuzzy sliding film controller is designed, and the design of the fuzzy sliding film controller comprises three parts in total:an equivalent controller, a switching controller and a fuzzy controller;
equivalent controller
Defining a switching function:
wherein c is the coefficient of the sliding mode surface,for the roll angle error of the car body,/>Is the roll angle error change rate;
in order to meet the corresponding speed of the controller and reduce the influence of buffeting on the controller, the comprehensive consideration selects the index approach rate:
sl＝-ηsgn(s)-ks
wherein, eta, k is the approach rate parameter (eta > 0, k > 0), S is derived and is brought into a dynamics model to obtain equivalent control F ueq ；
Switching controller
Defining a switching controller:
substituting the model of the transverse dynamics of the vehicle to obtain F usw The final sliding mode controller is as follows:
F u ＝F ueq +F usw ；
fuzzy controller
In the fuzzy sliding mode controller, the input of the fuzzy controller is a sliding mode surface s (t), in the control process, the fuzzy control adjusts an equivalent control part and a switching control part in the sliding mode controller according to the state of the sliding mode surface, namely when the state of a system is far away from the sliding mode surface, the switching control is required to be added through the fuzzy control, and when the system is close to the sliding mode surface, the original equivalent part is maintained, so that the control rule is obtained as follows:
If s(t)is ZO then F u is F ueq
If s(t)is NZ then F u is F ueq +F usw
after defuzzification, the fuzzy controller is:
μ ZO (s)+μ NZ (s)＝1
the coordination level cooperatively controls the suspension system and the steering system according to the transverse and vertical coordination control logic of the unmanned mine truck chassis, wherein the control logic is as follows:
in the table, delta fL 、Desired front wheel steering angle and desired suspension effort for tissue level output;
steering-by-wire system AFS in an execution stage for controlling steering receives a steering angle output delta of a coordination stage f The active suspension system CDC controlling the attitude of the vehicle body receives the control force F of the force generator of the coordination level ufl 、F ufr 、F url 、F urr 。
8. The unmanned mining truck transverse and vertical coupling hierarchical control method for the complex mining area working condition according to claim 7, wherein the step 5) is specifically as follows:
the curvature is optimized based on a genetic algorithm, the optimization process is to find a target path closest to an expected path for tracking, and the minimum problem is solved, so that the curvature is required to be subjected to scale conversion:
wherein F (x) is an fitness function, and the design process of the evaluation index F is as follows:
where ρ2, ρ 1 D for optimizing path curvature and desired tracking path curvature, respectively e2 In order to correspond to the lateral deviation of the path, the optimized curvature in the fitness function should also meet the boundary condition requirements of the vehicle body stability:
vehicle body roll angle output according to unmanned mining card dynamics modelLateral acceleration a y Dynamic vertical load F of wheel zii Calculating roll moment, reversely outputting control force through active suspension actuator to enable roll angle of vehicle body>Decay to approximately 0;
wherein the roll moment mainly comprises: roll moment due to centrifugal force of suspended massRoll moment due to gravity of suspended mass>When rolling, the vertical load is transferred to the left and right wheel loads to generate load transfer moment/>
M is in s A is sprung mass y For centroid lateral acceleration, h g Is the height of the mass center,the roll angle of the vehicle body is B, the track is B, and g is gravity acceleration;
wherein F is zfl 、F zrl 、F zfr 、F zrr The method comprises the following steps:
wherein m is the mass of the whole vehicle, a is the distance from the mass center to the front axle, b is the distance from the mass center to the rear axle, a x For centroid longitudinal acceleration, a y For centroid lateral acceleration, h g Is the centroid height.
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