CN117930665B - Multi-automatic driving mine card synchronous control method and system considering vehicle heterogeneity - Google Patents
Multi-automatic driving mine card synchronous control method and system considering vehicle heterogeneity Download PDFInfo
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Abstract
The invention discloses a synchronous control method and a synchronous control system for a plurality of automatic driving mining cards in consideration of vehicle heterogeneity, which relate to the technical field of automatic mining card fleet control and comprise the steps of adopting a fixed spacing strategy form among vehicles to calculate vehicle spacing errors; constructing vehicle dynamics models with different inertia time constants by combining the controller; and constructing an adaptive synchronous controller to perform heterogeneous conversion matching, and constructing an adaptive control law by a Lyapunov stability method. The synchronous control method of the multi-autopilot mining card considering the vehicle heterogeneity provided by the invention considers the heterogeneity characteristics of the mining card, establishes the vehicle dynamics model aiming at different inertial time constants, is more in line with the actual situation, and ensures that the fleet operates more stably. The self-adaptive controller is designed by adopting a model reference self-adaptive control method, and the heterogeneous vehicle model is compensated by the self-adaptive components, so that the influence of the difference on the system is reduced as much as possible, and the synchronous control effect is improved.
Description
Technical Field
The invention relates to the technical field of automatic mining truck fleet control, in particular to a multi-automatic driving mining truck synchronous control method and system considering vehicle heterogeneity.
Background
With the rapid development of advanced technologies such as artificial intelligence, mobile communication and cloud computing, intelligent traffic is gaining more and more widespread attention. Advanced communication technology and autopilot technology have facilitated the creation of multiple vehicle fleets. By controlling multiple vehicles to be arranged together in a certain preset shape and to control their behavior simultaneously, it has proven to have several technical advantages: the driving distance and time are reduced, and the driving efficiency is improved; the fuel consumption is reduced, and the environmental protection is facilitated; the safe distance and speed between vehicles are controlled, and the traffic safety performance is improved.
The multi-vehicle formation control originates from conventional adaptive cruise control (Adaptive Cruise Control, ACC). The rear vehicle is controlled to automatically adjust the vehicle speed to follow the front vehicle based on the distance and speed of the front vehicle measured by the on-board radar and the camera of the rear vehicle, but such control does not consider communication and cooperation between vehicles, and thus, a cooperative adaptive cruise control (Cooperative Adaptive Cruise Control, CACC) is introduced. The following distance is further reduced through wireless communication information sharing among vehicles, so that more accurate vehicle distance control is achieved. The current research is mainly focused on the control of linear formations, and common formation control technologies mainly comprise a leader-follower method, a virtual structure method, a behavior-based method, a graph theory-based method and an artificial potential field method. Among them, the "leader-follower" approach is most widely used. The mining area has severe environment and high risk, is urgent in operation requirement on the automatic driving vehicles, and is a technology with wide development prospect due to the development potential in the aspect of business.
Autopilot fleet is a special form of multi-agent system, so multi-vehicle fleet control is affiliated with multi-agent cooperative control, which can then be divided into lead following compliance issues for multi-agent systems that include a leader. Different from the consistency problem, the state of the final realization of the multi-agent is time-varying, and is a synchronization problem; when the final implementation state of the multi-agent is fixed, the multi-agent is a consistency problem. And the synchronization problem focuses on the convergence of the systems to a common trajectory under dynamic differences.
For heterogeneous multi-agent systems, two general classes of methods are employed to achieve agreed behavior: firstly, constructing a homogenization model, for example, incorporating a model containing target states into all agents; and secondly, matching a reference model, and adopting a model reference self-adaption or distributed feedforward method. The distributed internal model method solves the difference of heterogeneous models by constructing consistency dynamics, has good interference suppression effect, but needs to meet the transmission zero condition; when the distributed feedforward method is adopted to match the reference model, the control input of the reference model is used as feedforward of the controller, so that the convergence rate of the system can be improved, the heterogeneous components among the models can be eliminated through the self-adaptive components, the reference state can be well matched, and the transmission zero condition is not required to be met. For example Baldi S et al in 2019 proposed an adaptive virtual model reference method that was applicable to the problem of adaptive synchronization of heterogeneous agents with arbitrarily large matching uncertainties.
For mining area scenes, liu Hui et al propose an unmanned mine car path planning method based on an improved ant colony algorithm in 2021, and the mine car tracking capacity is improved by introducing obstacle exploration and a speed-gradient model. Wang Pengfei et al in 2022 propose a mining card whole-course conflict-free collaborative passing planning method, which is based on time-distance constraint to resolve workshop conflict, and is based on a self-adaptive trapezoidal speed planning method to plan speed, and experiments prove the effectiveness of the method. These studies have only made path planning and motion control for single vehicles and have not yet studied the content of multi-vehicle formation control.
In summary, current studies mainly focus on communication delays and instabilities between vehicles, and cannot be conducted in depth on the synchronization problem focusing on individual dynamics. Therefore, the invention focuses on the synchronism research, namely on the basis of certain communication, pay attention to the individual dynamics of the vehicle, and under the fixed interval strategy, establishes the vehicle dynamics model with different inertia time constants, and then designs the self-adaptive synchronous controller. There is a lack of research on the problem of multi-car formation in mining area scenes.
The invention provides a multi-automatic driving mining card synchronous control method considering vehicle heterogeneity, which aims at vehicle dynamics models with different inertia time constants under a fixed interval strategy, designs a self-adaptive synchronous controller, compensates heterogeneous vehicle models through self-adaptive components, and further improves the synchronous effect between a following vehicle and a guiding vehicle.
Disclosure of Invention
The present invention has been made in view of the above-described problems.
Therefore, the technical problems solved by the invention are as follows: the existing automatic mining truck fleet control method has the problems that communication is delayed, the method is immature, and the problem that the operation of the whole system is affected due to different operation results possibly caused by large and small differences of the inertia of mining trucks is solved.
In order to solve the technical problems, the invention provides the following technical scheme: a synchronous control method of a multi-autopilot mining card considering vehicle heterogeneity comprises the steps of adopting a fixed spacing strategy form between vehicles to calculate vehicle spacing errors; constructing vehicle dynamics models with different inertia time constants by combining the controller; and constructing an adaptive synchronous controller to perform heterogeneous conversion matching, and constructing an adaptive control law by a Lyapunov stability method.
As a preferable scheme of the multi-autopilot mining card synchronous control method considering vehicle heterogeneity, the invention comprises the following steps: the calculating the inter-vehicle distance error comprises the steps that a fixed distance strategy is adopted among vehicles, and the inter-vehicle distance is expressed as:
；
Wherein, For a fixed distance,/>For car/>With the target vehicle/>At time/>Is a pitch of (2); the inter-vehicle distance error is expressed as:
；
Wherein, For vehicle/>Location of/(I)For vehicle/>Location of/(I)For vehicle/>Is a length of (c).
As a preferable scheme of the multi-autopilot mining card synchronous control method considering vehicle heterogeneity, the invention comprises the following steps: the vehicle dynamics model comprises a control quantity for responding to a first-order inertia link and inputting acceleration, wherein the control quantity is expressed as follows:
；
In the formula, in the formula (I), For vehicle/>Acceleration of/>For control input,/>For vehicle/>Inertia time constant of/>Indicating the acceleration change rate of the vehicle i; constructing a vehicle distance error and vehicle dynamics model, which is expressed as follows:
；
Wherein, Representing the rate of change of the position error of vehicle i,/>Representing vehicle/>Rate of speed change,/>For vehiclesPosition error of/>Representing vehicle/>Speed of/(I)Representing vehicle/>Front speed of (v)/(v)Vehicle/>Driving force attenuation coefficient,/>For vehicle/>Is set to the target driving force; inertia time constant of different vehicles satisfies/>Substituted intoThe output acceleration rate of change is expressed as:
；
Wherein, Reference value representing inertial time constant,/>For vehicle/>Differential value of inertia constant according to vehicleDifference value of inertia constant and vehicle/>Determining vehicle/>, inertial time constant relationshipIs expressed as:
；
a vehicle model with heterogeneous inertial time constants is expressed as:
；
After the vehicle model of heterogeneous inertia time constant is built, the self-adaptive synchronous controller is built.
As a preferable scheme of the multi-autopilot mining card synchronous control method considering vehicle heterogeneity, the invention comprises the following steps: the constructing the adaptive synchronous controller includes performing a controller input, expressed as:
；
Wherein, Representing the position error of vehicle j,/>Representing the speed of vehicle j,/>Target acceleration of guided vehicle for queue,/>For controller gain for adjacent vehicle distance deviation,/>For controller gain for adjacent vehicle speed deviations,For controller gain for head car speed bias,/>Control inputs for the lead vehicle; the final controller input is expressed as:
；
And after the controller is determined, constructing a state space vehicle model.
As a preferable scheme of the multi-autopilot mining card synchronous control method considering vehicle heterogeneity, the invention comprises the following steps: the building a vehicle state space model includes building a state space vehicle model in combination with a vehicle model having heterogeneous inertial time constants and final controller inputs, expressed as:
；
The input guide vehicle data obtains a dynamic state space model of the head car.
As a preferable scheme of the multi-autopilot mining card synchronous control method considering vehicle heterogeneity, the invention comprises the following steps: the constructing of the adaptive control law comprises the step of constructing the adaptive control law through a Lyapunov stability method, and dividing the control input of each vehicle into two parts, wherein the two parts are expressed as follows:
；
Wherein, As reference control quantity,/>Before control, the vehicle control quantity/>, which is transmitted in a vehicle team, is used as the self-adaptive control quantityIs practically equivalent to the reference control quantity/>Will/>Substituting the vehicle state space model to construct a state space model of the heterogeneous vehicle and performing compact conversion, wherein the state space model is expressed as:
；
Wherein, For vehicle/>Time derivative of state vector of/(Representing vehicle/>Is used for the internal dynamic characteristics of the (c),Representing vehicle/>State vector of/>Representing control input versus vehicle/>Influence of the state/>Representing input vector,/>Representing vehicle/>Heterogeneity parameter,/>Representing an adaptive control speed vector,/>Representing vehicle/>Is used for the control of the non-uniformity parameters of (a),Representing a vehicle state error vector, when/>Time,/>；
Performing the difference elimination of the actuator to make the adaptive control quantity of heterogeneous parameters be as followsThe state space model compact form of a heterogeneous vehicle is expressed as:
；
Wherein, For vehicle/>Estimate of the heterogeneity parameter of/>For vehicle/>An estimation error of the heterogeneity parameter of (2);
The onboard reference model is expressed as:
；
Wherein, Representing the time derivative of the state vector on board the vehicle,/>Representing a state vector on board,/>Representing an onboard input vector;
Definition following vehicle The state following error with the reference model is:
；
The adaptive control law is expressed as:
；
Wherein, Representing a positive definite symmetric matrix, T representing a transpose;
The self-adaptive control quantity is as follows:
；
Adaptive parameter satisfaction R is an adaptive adjustment parameter.
As a preferable scheme of the multi-autopilot mining card synchronous control method considering vehicle heterogeneity, the invention comprises the following steps: the construction of the adaptive control law further comprises stability feedback detection, expressed as:
；
Wherein, And the monitoring value meets the requirement, and the monitoring value indicates that the motorcade is in a stable running state.
The invention further aims to provide a multi-automatic driving mine card synchronous control system considering vehicle heterogeneity, which can establish vehicle dynamics models aiming at different inertial time constants by considering the heterogeneity characteristics of the mine cards, is more in line with actual conditions, is more stable in operation of a vehicle team, and solves the problems that the self-inertia of the mine cards is not considered in the current cooperative driving of the vehicle team, and the operation deviation is easy to cause.
As a preferable scheme of the multi-autopilot mining card synchronous control system considering vehicle heterogeneity, the invention comprises the following steps: the system comprises a driving module, a vehicle dynamics model building module and a self-adaptive control module; the driving module is used for calculating vehicle spacing errors by adopting a fixed spacing strategy form between vehicles; the vehicle dynamics model building module is used for building vehicle dynamics models with different inertia time constants by combining the controller; the self-adaptive control module is used for constructing a self-adaptive synchronous controller to carry out heterogeneous conversion matching, and constructing a self-adaptive control law through a Lyapunov stability method.
A computer device comprising a memory storing a computer program and a processor executing the computer program is the step of implementing a multi-autopilot mining card synchronous control method that takes vehicle heterogeneity into account.
A computer readable storage medium having stored thereon a computer program which when executed by a processor implements the steps of a multi-autopilot mining card synchronous control method that takes into account vehicle heterogeneity.
The invention has the beneficial effects that: the synchronous control method of the multi-autopilot mining card considering the vehicle heterogeneity provided by the invention considers the heterogeneity characteristics of the mining card, establishes the vehicle dynamics model aiming at different inertial time constants, is more in line with the actual situation, and ensures that the fleet operates more stably. The self-adaptive controller is designed by adopting a model reference self-adaptive control method, and the heterogeneous vehicle model is compensated by the self-adaptive components, so that the influence of the difference on the system is reduced as much as possible, and the synchronous control effect is improved. The automatic driving technology of multi-vehicle formation is adopted in the mining area, so that the situation that no person is applied to the mining area driver post can be improved, a large amount of manpower resources are reduced, the production efficiency is improved, traffic accidents are reduced, and the energy-saving and environment-friendly effects are achieved. The invention has better effects in the aspects of stability, safety and synchronization rate.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is an overall flowchart of a method for controlling synchronization of multiple autopilot mining cards in consideration of vehicle heterogeneity according to a first embodiment of the present invention.
Fig. 2 is a diagram of simulation results of a multi-autopilot mining card synchronous control method according to a second embodiment of the present invention, which considers vehicle heterogeneity, without adaptive control amounts at a fixed pitch.
Fig. 3 is a diagram of simulation results when the adaptive control amount is provided at a fixed pitch of the synchronous control method for multiple autopilot mining cards according to the second embodiment of the present invention, which takes vehicle heterogeneity into consideration.
Fig. 4 is a diagram of simulation results of a multi-autopilot mining card synchronous control method according to a second embodiment of the present invention, which considers vehicle heterogeneity, without adaptive control amounts at a fixed time interval.
Fig. 5 is a diagram of simulation results of a multi-autopilot mining card synchronous control method with adaptive control under a fixed time interval, which is provided by a second embodiment of the present invention and considers vehicle heterogeneity.
Fig. 6 is an overall flowchart of a multi-autopilot mining card synchronous control system considering vehicle heterogeneity according to a third embodiment of the present invention.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
Example 1
Referring to fig. 1, for one embodiment of the present invention, there is provided a multi-autopilot mining card synchronization control method considering vehicle heterogeneity, including:
S1: the vehicles adopt a fixed spacing strategy form, and the vehicle spacing error is calculated.
Further, since the vehicular heterogeneity mainly manifests itself as an inertia time constant of the vehicle and a driving capability of the vehicle, only the inertia time constant and the driving capability are considered. Calculating the inter-vehicle distance error includes employing a fixed-distance strategy between vehicles, the inter-vehicle distance being expressed as:
；
Wherein, For a fixed distance,/>For car/>With the target vehicle/>At time/>Is a pitch of (c).
The inter-vehicle distance error is expressed as:
；
Wherein, For vehicle/>Location of/(I)For vehicle/>Location of/(I)For vehicle/>Is a length of (c).
S2: vehicle dynamics models with different inertial time constants are built in conjunction with the controller.
Further, the vehicle dynamics model includes a control amount of input acceleration in response to the first-order inertia element, expressed as:
；
In the formula, in the formula (I), For vehicle/>Acceleration of/>For control input,/>For vehicle/>Is used for the inertia time constant of the (c),The acceleration change rate of the vehicle i is shown.
Constructing a vehicle distance error and vehicle dynamics model, which is expressed as follows:
；
Wherein, Representing the rate of change of the position error of vehicle i,/>Representing vehicle/>Rate of speed change,/>For vehiclesPosition error of/>Representing vehicle/>Speed of/(I)Representing vehicle/>Front speed of (v)/(v)Vehicle/>Driving force attenuation coefficient,/>For vehicle/>Is set to the target driving force of (a).
The inertia time constant of different vehicles meetsSubstituted/>The output acceleration rate of change is expressed as:
；
Wherein, Reference value representing inertial time constant,/>For vehicle/>Differential value of inertia constant according to vehicleDifference value of inertia constant and vehicle/>Determining vehicle/>, inertial time constant relationshipIs expressed as:
；
a vehicle model with heterogeneous inertial time constants is expressed as:
；
After the vehicle model of heterogeneous inertia time constant is built, the self-adaptive synchronous controller is built.
S3: and constructing an adaptive synchronous controller to perform heterogeneous conversion matching, and constructing an adaptive control law by a Lyapunov stability method.
Further, in the case of a following vehicle acquiring a lead vehicle, since the fleet is composed of heterogeneous vehicles of different dynamics models, the control information of the lead vehicle cannot be directly used for the control input of the following vehicle. To synchronize the fleet, a Model Reference Adaptive Control (MRAC) method is used to match the lead vehicle Model. Constructing the adaptive synchronous controller includes performing a controller input, expressed as:
；
Wherein, Representing the position error of vehicle j,/>Representing the speed of vehicle j,/>Target acceleration of guided vehicle for queue,/>For controller gain for adjacent vehicle distance deviation,/>For controller gain for adjacent vehicle speed deviations,For controller gain for head car speed bias,/>To guide the control inputs of the vehicle.
The final controller input is expressed as:
；
And after the controller is determined, constructing a state space vehicle model.
It should be noted that constructing the vehicle state space model includes constructing the state space vehicle model in combination with the vehicle model having heterogeneous inertial time constants and the final controller inputs, expressed as:
；
The input guide vehicle data obtains a dynamic state space model of the head car.
It should also be noted that constructing an adaptive control law includes constructing an adaptive control law by the lyapunov stability method, dividing the control input of each vehicle into two parts, denoted:
；
Wherein, As reference control quantity,/>Before control, the vehicle control quantity/>, which is transmitted in a vehicle team, is used as the self-adaptive control quantityIs practically equivalent to the reference control quantity/>Will/>Substituting the vehicle state space model to construct a state space model of the heterogeneous vehicle and performing compact conversion, wherein the state space model is expressed as:
；
Wherein, For vehicle/>Time derivative of state vector of/(Representing vehicle/>Internal dynamics of,/>Representing vehicle/>State vector of/>Representing control input versus vehicle/>Influence of the state/>Representing input vector,/>Representing vehicle/>Heterogeneity parameter,/>Representing an adaptive control speed vector,/>Representing vehicle/>Heterogeneity parameter,/>Representing a vehicle state error vector, when/>Time,/>；
Performing the difference elimination of the actuator to make the adaptive control quantity of heterogeneous parameters be as followsThe state space model compact form of a heterogeneous vehicle is expressed as:
；
Wherein, For vehicle/>Estimate of the heterogeneity parameter of/>For vehicle/>An estimation error of the heterogeneity parameter of (2);
The onboard reference model is expressed as:
；
Wherein, Representing the time derivative of the state vector on board the vehicle,/>Representing a state vector on board,/>Representing an onboard input vector;
Definition following vehicle The state following error with the reference model is:
；
The adaptive control law is expressed as:
；
Wherein, Representing a positive definite symmetric matrix, T representing a transpose;
The self-adaptive control quantity is as follows:
；
Adaptive parameter satisfaction R is an adaptive adjustment parameter.
Furthermore, the Lyapunov function is selected as:
；
Wherein, Representing the sum of the diagonal elements of the matrix. The lyapunov function is derived over time, expressed as:
；
Because the trace of the matrix satisfies the property Updating the function:
；
Wherein, And the monitoring value meets the requirement, and the monitoring value indicates that the motorcade is in a stable running state.
Example 2
Referring to fig. 2 to 5, for one embodiment of the present invention, a multi-autopilot mining card synchronous control method considering vehicle heterogeneity is provided, and in order to verify the beneficial effects of the present invention, scientific demonstration is performed through economic benefit calculation and simulation experiments.
Firstly, performing simulation experiment verification by using Simulink, wherein the simulation model comprises a guide vehicle and four following vehicles. The guiding vehicle is used as a reference model, the following vehicle and the guiding vehicle have different inertia time constants, the inertia time constants of the vehicles are selected as shown in the following table 1, and the other parameters are selected as shown in the following table 2.
TABLE 1 vehicle inertial time constant
Table 2 simulation parameters
Fig. 2 and 3 are simulation results. Wherein fig. 2 shows the results of the queue simulation when the own-vehicle target control amount is calculated using the basic control amount, i.e., using only the target control amounts and the state amounts of the head-vehicle and the front-vehicle, without including the adaptive control amount compensating for the actuator lag. Fig. 3 shows the results of a queue simulation of a controller containing adaptive compensation. It has been found that when the adaptive control amount for compensating for the actuator lag is not included, the vehicle-to-vehicle distance deviation tends to be amplified, the maximum speed and acceleration of the following vehicle exceed the speed and acceleration of the leading vehicle, and the larger the actuator lag, the larger the deviation, and the longer the time for the queue to cancel the deviation, and the slower the convergence speed. When the controller with self-adaptive compensation is included, the vehicle distance error of the queue is in a decreasing trend when propagating towards the tail of the queue, and the delay of the actuator of the following vehicle is different, but the maximum speed and the acceleration peak value are not obviously different, which indicates that the self-adaptive control quantity compensates the difference of the actuators of the vehicles, so that the queue is more stable.
Under the same simulation scene, the self-adaptive control result when a fixed time interval strategy is adopted is explored, and the method is found to be still applicable. Fig. 4 and 5 show simulation results using a fixed time interval. It can be found that when the inertia time constant is greater than that of the reference system, the inter-vehicle distance error is a positive value, indicating that the vehicle is behind the reference vehicle, and the inter-vehicle distance is greater than the standard value. Otherwise, the vehicle distance is smaller than the standard value. When the adaptive controller is not included, the maximum acceleration of the following vehicle with a large inertia time constant exceeds the maximum acceleration of the guiding vehicle, and the maximum speed of the vehicle also exceeds the maximum speed of the guiding vehicle. When the self-adaptive controller is provided, the maximum speed and the acceleration of the following vehicle do not obviously exceed the speed and the acceleration of the guiding vehicle, and the vehicle distance error is obviously smaller than the situation without the self-adaptive controller, so that the self-adaptive controller achieves a better queue following effect.
Example 3
Referring to fig. 6, for one embodiment of the present invention, a multi-autopilot mining card synchronous control system considering vehicle heterogeneity is provided, which includes a driving module, a vehicle dynamics model building module, and an adaptive control module.
The driving module is used for calculating vehicle distance errors by adopting a fixed distance strategy mode among vehicles. The vehicle dynamics model building module is used for building vehicle dynamics models with different inertia time constants in combination with the controller. The self-adaptive control module is used for constructing a self-adaptive synchronous controller to carry out heterogeneous conversion matching, and constructing a self-adaptive control law through a Lyapunov stability method.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method of the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, randomAccess Memory), a magnetic disk, an optical disk, or other various media capable of storing program codes.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium may even be paper or other suitable medium upon which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like. It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
Claims (5)
1. A synchronous control method for a plurality of automatic driving mine cards considering vehicle heterogeneity is characterized by comprising the following steps:
The vehicles adopt a fixed spacing strategy form, and the vehicle spacing error is calculated;
Constructing vehicle dynamics models with different inertia time constants by combining the controller;
Constructing an adaptive synchronous controller to perform heterogeneous conversion matching, and constructing an adaptive control law by a Lyapunov stability method;
The calculating the inter-vehicle distance error comprises the steps that a fixed distance strategy is adopted among vehicles, and the inter-vehicle distance is expressed as:
wherein L i is a fixed interval, The distance between the vehicle r and the target vehicle i at time t;
the inter-vehicle distance error is expressed as:
ei(t)＝pi-1-pi-Di-Li
Wherein p i-1 is the position of vehicle i-1, p i is the position of vehicle i, and D i is the length of vehicle i;
the vehicle dynamics model comprises a control quantity for responding to a first-order inertia link and inputting acceleration, wherein the control quantity is expressed as follows:
Where a i is the acceleration of vehicle i, u i is the control input, τ i is the inertia time constant of vehicle i, Indicating the acceleration change rate of the vehicle i;
Constructing a vehicle distance error and vehicle dynamics model, which is expressed as follows:
Wherein, Representing the rate of change of the position error of vehicle i,/>Representing the speed change rate of the vehicle i, e i being the position error of the vehicle i, v i representing the speed of the vehicle i, v i-1 representing the front vehicle speed of the vehicle i, Λ i being the driving force attenuation coefficient of the vehicle i, u i,0 being the target driving force of the vehicle i;
the inertia time constant of different vehicles meets tau i＝τ0+Δτι and is substituted into The output acceleration rate of change is expressed as:
Where τ 0 represents a reference value of the inertia time constant, Δτ i is a difference value of the inertia constant of the vehicle i, and determines an absolute value of the heterogeneity parameter of the vehicle i according to the difference value of the inertia constant of the vehicle i and the inertia time constant relation of the vehicle i, which is expressed as:
a vehicle model with heterogeneous inertial time constants is expressed as:
after the vehicle model of heterogeneous inertia time constant is built, a self-adaptive synchronous controller is built;
the constructing the adaptive synchronous controller includes performing a controller input, expressed as:
Where e j denotes the position error of vehicle j, v j denotes the speed of vehicle j, u r is the target acceleration of the train guiding vehicle, K p is the controller gain for the adjacent vehicle distance deviation, K d is the controller gain for the adjacent vehicle speed deviation, K dL is the controller gain for the head vehicle speed deviation, u L is the control input of the guiding vehicle;
the final controller input is expressed as:
After the controller is determined, a state space vehicle model is constructed;
Constructing the vehicle state space model includes constructing the state space vehicle model in combination with the vehicle model having heterogeneous inertial time constants and the final controller input, expressed as:
Inputting guiding vehicle data to obtain a dynamic state space model of the head car;
the constructing of the adaptive control law comprises the step of constructing the adaptive control law through a Lyapunov stability method, and dividing the control input of each vehicle into two parts, wherein the two parts are expressed as follows:
ui＝ui,bl+ui,ad
Wherein u i,bl is a reference control quantity, u i,ad is an adaptive control quantity, and before control, a vehicle control quantity u i transmitted in a vehicle team is actually equivalent to the reference control quantity u i,bl, and u i＝ui,bl+ui,ad is substituted into a vehicle state space model to construct a state space model of a heterogeneous vehicle and perform compact conversion, which is expressed as:
Wherein, For the time derivative of the state vector of vehicle i, a i represents the internal dynamics of vehicle i, x i represents the state vector of vehicle i, B i represents the influence of the control input on the state of vehicle i, w i represents the input vector, G i represents the heterogeneity parameter of vehicle i, u i,ad represents the adaptive control speed vector, H i represents the heterogeneity parameter of vehicle i, Φ i represents the vehicle state error vector, a i＝Ai-1＝A,Bi＝Bi-1＝B,Gi＝Gi-1 =g when i-1 is equal to or greater than 1;
Performing the difference elimination of the executor to enable the adaptive control quantity of the heterogeneous parameters to be u i,ad＝-Hiφi, and enabling the compact form of the state space model of the heterogeneous vehicle to be expressed as:
Wherein, Phi i is the estimated value of the heterogeneity parameter of the vehicle i, and phi i is the estimated error of the heterogeneity parameter of the vehicle i;
The onboard reference model is expressed as:
Wherein, Representing the time derivative of the onboard state vector, x i,r represents the onboard state vector, and w i,r represents the onboard input vector;
the state following error of the following vehicle i and the reference model is defined as follows:
ei＝xi-xi,r
The adaptive control law is expressed as:
ATP+PA＝-Q
Wherein R, P, Q denotes a positive definite symmetric matrix, and T denotes a transpose;
The self-adaptive control quantity is as follows:
Adaptive parameter satisfaction And r is an adaptive adjustment parameter.
2. The multi-autopilot mining card synchronous control method considering vehicle heterogeneity according to claim 1, wherein: the construction of the adaptive control law further comprises stability feedback detection, expressed as:
Wherein V represents a Lyapunov function, and when the monitored value meets the requirement, the vehicle team is in a stable running state.
3. A system employing the multi-autopilot mining card synchronous control method considering vehicle heterogeneity according to any one of claims 1-2, wherein: the system comprises a driving module, a vehicle dynamics model building module and a self-adaptive control module;
The driving module is used for calculating vehicle spacing errors by adopting a fixed spacing strategy form between vehicles;
the vehicle dynamics model building module is used for building vehicle dynamics models with different inertia time constants by combining the controller;
the self-adaptive control module is used for constructing a self-adaptive synchronous controller to carry out heterogeneous conversion matching, and constructing a self-adaptive control law through a Lyapunov stability method.
4. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the multi-autopilot mining card synchronous control method taking into account vehicle heterogeneity according to any one of claims 1 to 2.
5. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the multi-autopilot mining card synchronous control method taking into account vehicular heterogeneity according to any one of claims 1 to 2.
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