CN117518786A - Model-free self-adaptive automatic driving automobile path tracking control method - Google Patents
Model-free self-adaptive automatic driving automobile path tracking control method Download PDFInfo
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Abstract
The invention provides a model-free self-adaptive automatic driving automobile path tracking control method, which comprises the following steps: constructing a control dynamics system in an automatically driven vehicle, and describing the control dynamics system by adopting a discrete time nonlinear time lag system; converting the discrete time nonlinear time lag system into a partial format dynamic linearization data model; obtaining differential signals by using a second-order tracking differentiator by using a partial format dynamic linearization data model, and then constructing a PFDL-IMFAC controller; the implementation of the method for path control by the PFDL-IMFAC controller is only based on the input and output data of the automatic driving automobile, complex mathematical modeling of the automatic driving automobile is not needed, and good self-adaptability in a complex environment is ensured. Compared with other model-free self-adaptive control methods, the method enables the designed controller to process a system with time delay.
Description
Technical Field
The invention belongs to the field of path tracking control methods, and particularly relates to a model-free self-adaptive automatic driving automobile path tracking control method.
Background
Currently, path tracking control is one of the important functions of an automatically driven automobile. The aim of this technique is to control the autonomous vehicle to follow a predetermined trajectory. In the existing path tracking control technology, main methods include an MPC method, an optimal control method, a robust control method and the like. The methods are mostly model-based control methods, the controllers are designed by adopting complex mathematical theory, mathematical modeling and analysis are needed when the controllers are designed, and the implementation process is complex. Moreover, the model-based control method is large in calculation amount, and a large amount of calculation resources of a computer are occupied in the control process. Therefore, selecting a data driven method to accomplish the path tracking task is a more appropriate option. The model-free adaptive control (MFAC) method is a data driving method, does not need to establish an accurate mathematical model, has small calculation amount, and is suitable for path tracking control of an automatic driving automobile under the condition of difficult modeling. It should be noted that, the autopilot has the characteristics of large mass and large inertia, and is a typical time lag system, so that the influence of time lag needs to be considered in the control process. However, the existing model-free adaptive control is insufficient in processing under the time lag condition, so that the path tracking control precision is not ideal.
Therefore, there is a need for a model-free adaptive automatic driving vehicle path tracking control method.
Disclosure of Invention
The invention provides a model-free self-adaptive automatic driving automobile path tracking control method, which solves the problem of non-ideal path tracking control precision caused by insufficient processing under the condition of time delay of the existing model-free self-adaptive control in the prior art.
The technical scheme of the invention is realized as follows: a model-free adaptive autopilot vehicle path tracking control method, the method comprising the steps of:
constructing a control dynamics system in an automatically driven vehicle, and describing the control dynamics system by adopting a discrete time nonlinear time lag system;
converting the discrete time nonlinear time lag system into a partial format dynamic linearization data model;
obtaining differential signals by using a second-order tracking differentiator by using a partial format dynamic linearization data model, and then constructing a PFDL-IMFAC controller;
path control is performed by a PFDL-IMFAC controller.
As a preferred embodiment, the discrete-time nonlinear time lag system describes it, and is expressed by the following formula:
y(k+τ+1)＝f(y(k+τ),……,y(k+τ-n θ ),u(k),……,u(k-n u ))
wherein f (·) is an unknown nonlinear function representing the unmanned vehicle nonlinear transverse/longitudinal dynamics system; n is n θ ，n u Is an unknown parameter representing the order of the kinetic system; τ is the lag time coefficient of the autopilot system; u (k) represents the current moment input of the system, namely the throttle/brake opening or steering wheel angle at the current moment; y (k) represents the current time output of the system, i.e., the current time travel speed or travel angle.
As a preferred embodiment, the partial format dynamic linearization data model is expressed as:
wherein phi is L (k)＝[φ 1 (k),φ 2 (k),...,φ L (k)] T Is a pseudo-gradient, is a time-varying vector;
ΔU L (k)＝[Δu(k),...,Δu(k-L+1)] T ；
l is the control input linearization length constant;
ΔU L (k)＝U L (k)-U L (k-1)；
at a transfer function of G p (s)e -τs And in the case of a slowly time-varying structure of the system parameters, τs+1 is used instead of e -τs To complete the function of the Smith predictor; the system controlling the effect DeltaU at time k L (k) The feedback amount under drive is expressed as:
where y' (k) is the derivative of y (k).
As a preferred embodiment, when the partial-format dynamic linearization data model uses a second-order tracking differentiator to obtain a differential signal, the following formula is adopted for expression:
wherein T is sampling time, x TD (k) And x' TD (k) Two output signals, x, of a 2 nd order track-differentiator TD (k) For y (k) tracking signal, x' TD (k) To track the differential signal of y (k), h and r are the filter factor and the velocity factor, respectively, fst (·) is a nonlinear function.
As a preferred embodiment, the FDL-IMFAC controller performs path control using the following formula:
in order for the algorithm to have stronger time-varying parameter tracking capability, the reset algorithm is set as follows:
wherein,is phi L (k) Is the estimated value of (y) * (k+τ+1) is the desired output; eta and rho are step factors of the algorithm, and eta epsilon (0, 1)]，ρ∈(0,1]The method comprises the steps of carrying out a first treatment on the surface of the Lambda > 0, mu > 0 is the controller parameter.
As a preferred embodiment, the path control includes a lateral control for causing the vehicle to follow a predetermined trajectory by controlling a steering wheel angle to control a traveling direction, and a longitudinal control; the longitudinal control controls the speed of the vehicle by controlling the opening degree of the accelerator/the brake, so that the vehicle runs at the expected speed, and the path tracking control of the unmanned automobile path is realized by separately designing the controllers for the longitudinal control and the transverse control respectively. By performing the lateral control and the longitudinal control, the running state and the speed of the vehicle are changed, and the direction is adjusted.
As a preferred embodiment, a sensor is disposed in the vehicle, and the sensor continuously transmits the state information data of the vehicle to the main control computer, and the main control computer calculates the state information data of the vehicle to obtain the expected speed and the expected position. The system is characterized in that a sensor is arranged in the vehicle, and the sensor can continuously acquire state information data of the vehicle and transmit the state information data to a main control computer. The host computer will calculate this data to derive the desired speed and desired position, which can help the vehicle control system better control the vehicle's operating state and direction of travel. The system can improve the accuracy and safety of vehicle running and optimize the energy consumption of the vehicle.
As a preferred embodiment, the current speed and the current position are detected by a sensor, and during the longitudinal control, the current control amount of the accelerator or the brake pedal is calculated by a designed controller based on the desired speed and the current speed; in the transverse control process, the control amount of the steering wheel rotation angle is calculated by using the current running posture and the expected running posture. The system detects the speed and position of the current vehicle by means of sensors, and then calculates the current control amount of the throttle or brake pedal during longitudinal control according to the desired speed and the current speed. The control quantity can help the vehicle to run at a more proper speed under different road conditions, so that the safety and stability of the vehicle are improved.
During lateral control, the system calculates the control amount of the steering wheel rotation angle using the current running posture and the desired running posture. This control amount can help the vehicle to remain stable while turning or changing lanes, thereby improving the handling and safety of the vehicle. Specifically, if the current attitude of the vehicle does not coincide with the desired attitude, the controller calculates the steering wheel rotation angle to be adjusted, and transmits this angle to the steering wheel control system to achieve lateral control of the vehicle.
In summary, the vehicle control system may help the vehicle to remain stable and safe under different road conditions through longitudinal and lateral control. By calculating the current speed and position, the expected speed and posture, and using a specific controller, accurate control of vehicle travel can be achieved, thereby improving vehicle performance and safety.
As a preferred implementation mode, the vehicle is internally provided with a path tracking system, the path tracking system comprises a main control module, an actuator module and a state sensing module, the main control module drives transverse and longitudinal control quantity through the actuator module, and executes a transverse and longitudinal control algorithm, calculates the rotation angle of a steering wheel, calculates the opening of a throttle and a brake and transmits control instructions. This system can manipulate the lateral and longitudinal movement of the vehicle by controlled amounts. This system consists of three main modules: the system comprises a main control module, an executor module and a state sensing module.
The main control module is the core of the system and is responsible for calculating the control quantity and transmitting the control quantity to the executor module. The main control module can calculate the rotation angle of the steering wheel, the opening degree of the accelerator and the opening degree of the brake according to different control algorithms, and transmits the information to the executor module. In this way, the master control module can control the movement of the vehicle to follow a predetermined path.
The actuator module is an execution part of the system and is responsible for converting the control instruction calculated by the main control module into actual motion control. The actuator module can calculate the control quantity of the transverse direction and the longitudinal direction according to the instruction, and control is realized by executing the opening degrees of the steering wheel angle, the throttle and the brake. This way, it is ensured that the vehicle runs along a predetermined path while ensuring the safety of the driver.
The state sensing module is a sensing part of the system, and can acquire the state information of the vehicle and transmit the state information to the main control module. The state sensing module can sense information such as speed, acceleration, direction and the like of the vehicle and transmit the information to the main control module. Therefore, the main control module can calculate a proper control instruction according to the state information of the vehicle, and the safety and stability of the running of the vehicle are ensured. In summary, the path tracking system built in the vehicle can control the movement of the vehicle by controlling the amount.
As a preferred embodiment, the state sensing module is internally provided with a speed sensor, a laser radar and a GPS, wherein the speed sensor uploads vehicle speed information, the laser radar uploads vehicle surrounding obstacle information, the GPS uploads vehicle position information, and the state sensing module aggregates the vehicle speed information, the vehicle surrounding obstacle information and the vehicle position information and uploads the vehicle speed information, the vehicle surrounding obstacle information and the vehicle position information to the main control module.
After the technical scheme is adopted, the invention has the beneficial effects that: the implementation of the method is only based on the input and output data of the automatic driving automobile, complex mathematical modeling is not needed for the automatic driving automobile, and good self-adaptability in a complex environment is ensured. Compared with other model-free self-adaptive control methods, the method enables the designed controller to process a system with time delay. Compared with other model-free self-adaptive control methods based on a tight format data model, the method is based on a partial format dynamic linearization model when the controller is designed, and the tracking performance of the controller is further improved.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive faculty for a person skilled in the art.
FIG. 1 is a flow chart of the operation of the control algorithm of the present invention;
FIG. 2 is a block diagram of a path trace control;
FIG. 3 path tracking control flow;
FIG. 4 is a schematic diagram of a lateral control problem;
FIG. 5 throttle brake conversion rules.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Examples:
as shown in fig. 1 to 5, a model-free adaptive automatic driving automobile path tracking control method comprises the following steps:
constructing a control dynamics system in an automatically driven vehicle, and describing the control dynamics system by adopting a discrete time nonlinear time lag system;
converting the discrete time nonlinear time lag system into a partial format dynamic linearization data model;
obtaining differential signals by using a second-order tracking differentiator by using a partial format dynamic linearization data model, and then constructing a PFDL-IMFAC controller;
path control is performed by a PFDL-IMFAC controller.
The path tracking control of the automatic driving automobile comprises transverse control and longitudinal control, wherein the transverse control mainly controls steering wheel rotation angle to control running direction, so that the automobile tracks a preset track, and the longitudinal control mainly controls accelerator/brake opening to control the speed of the automobile, so that the automobile runs at the expected speed. The controller is designed separately for longitudinal control and transverse control respectively to realize the path tracking control of the unmanned automobile path.
As shown in fig. 2, the path tracking control structure is that during the driving process, the sensor mounted on the unmanned vehicle continuously transmits the state information data of the vehicle to the main control computer, and the main control computer calculates the state information data of the vehicle to obtain the expected speed and the expected position. At the same time, the current speed and the current position can also be detected. During longitudinal control, the current control amount of the throttle or brake pedal can be calculated by a designed controller based on the desired speed and the current speed. Meanwhile, in the transverse control process, the control quantity of the steering wheel rotation angle can be calculated by using the current running gesture and the expected running gesture. The calculated transverse and longitudinal control amounts drive corresponding actuators, and mainly comprise a main control module, an actuator module and a state sensing module, wherein the control flow is shown in figure 3.
The main control module is a core part of the whole path tracking control system and is used for executing a transverse and longitudinal control algorithm, calculating the rotation angle of the steering wheel, calculating the opening of the throttle and the brake and transmitting control instructions. The state information data of the vehicle is transmitted to the main control computer, and the expected speed and the expected position are calculated after the state information data of the vehicle is calculated by the main control computer. Meanwhile, based on the expected state and the current state, the required control amount is calculated by executing a control algorithm to drive the actuator.
An actuator module of the path tracking system includes an accelerator/brake pedal and a steering wheel. In the longitudinal control, the opening degree of the accelerator/brake pedal directly determines the speed of the automatically driven automobile. In the lateral control, the driving direction of the automatic driving car can be directly determined by the angle of the steering wheel, so that the automatic driving car can drive according to a preset path.
The state sensing module mainly comprises a speed sensor, a laser radar and a GPS. The state sensing module may process input data of various sensors. By processing these data, the current running speed and position information of the vehicle can be acquired.
Lateral control section in which path tracking control is performed
From the transversal dynamics, the unmanned transversal dynamics process of the vehicle can be represented as shown in fig. 4, and the prior studies show that the error of the path tracking of the transversal control is zero, namely LD to 0, AD to 0 is equivalent to that the pre-drawing deviation angle in fig. 4 (a) tends to 0, namely theta to 0, and the conclusion can be described asBased on the analysis result of transverse dynamics, the solution idea that the control pre-aiming deviation angle tends to 0 is the problem of transverse control can be obtained.
Based on the analysis of transverse dynamics, the longitudinal movement process of the unmanned vehicle is expressed by a general nonlinear discrete time dynamics equation:
θ(k+τ+1)＝f(θ(k+τ),......,θ(k+τ-n θ ),u(k),......,u(k-n u ))
wherein u (k) represents the steering wheel rotation angle at the current moment k, and θ (k) represents the pretightening deviation angle at the current moment; n is n θ ，n u Is an unknown parameter representing the order of the kinetic system; f (·) is an unknown nonlinear function representing the transverse dynamics system of the unmanned vehicle nonlinearity; τ is the lag time coefficient of the autopilot system.
Based on the lateral dynamics analysis, a lateral controller was constructed as follows:
wherein,is phi L (k) Estimated value of θ * (k+τ+1) is the desired pretighted offset angle output; eta and rho are step factors of the algorithm, and eta epsilon (0, 1)]，ρ∈(0,1]The method comprises the steps of carrying out a first treatment on the surface of the Lambda > 0, mu > 0 is the controller parameter; θ'. TD (k) And pre-aiming the differential signal of the pre-aiming deviation angle at the current moment.
Lateral control portion of path tracking control
In general, for an unmanned vehicle longitudinal tracking control system, it can be described as:
v(k+τ+1)＝f(v(k+τ),...,v(k+τ-n x ),u(k),...,u(k-n u ))
wherein n is x ，n u Is an unknown parameter representing the order of the kinetic system; f (·) is an unknown nonlinear function representing the longitudinal dynamics system of the unmanned vehicle nonlinearity; τ is the lag time coefficient of the autopilot system. v (k) represents the running speed of the unmanned vehicle at the current moment k, u (k) represents the throttle/brake opening at the current moment k, and specific conversion rules are shown in fig. 4.
Based on the longitudinal dynamics analysis, a longitudinal controller was constructed as follows:
wherein,is phi L (k) Estimate of v * (k+τ+1) is the desired speed; eta and rho are step factors of the algorithm, and eta epsilon (0, 1)]，ρ∈(0,1]The method comprises the steps of carrying out a first treatment on the surface of the Lambda > 0, mu > 0 is the controller parameter; v' TD (k) Is a differential signal of the running speed at the current moment.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (10)
1. A model-free adaptive automatic driving automobile path tracking control method, which is characterized by comprising the following steps:
constructing a control dynamics system in an automatically driven vehicle, and describing the control dynamics system by adopting a discrete time nonlinear time lag system;
converting the discrete time nonlinear time lag system into a partial format dynamic linearization data model;
obtaining differential signals by using a second-order tracking differentiator by using a partial format dynamic linearization data model, and then constructing a PFDL-IMFAC controller;
path control is performed by a PFDL-IMFAC controller.
2. A model-free adaptive automatic driving car path tracking control method as claimed in claim 1, wherein: the discrete time nonlinear time lag system describes the system and is expressed by the following formula:
y(k+τ+1)＝f(y(k+τ),……,y(k+τ-n θ ),u(k),……,u(k-n u ))
wherein f (·) is an unknown nonlinear function representing the unmanned vehicle nonlinear transverse/longitudinal dynamics system; n is n θ ，n u Is an unknown parameter representing the order of the kinetic system; τ is the lag time coefficient of the autopilot system; u (k) represents the current moment input of the system, namely the throttle/brake opening or steering wheel angle at the current moment; y (k) represents the current time output of the system, i.e., the current time travel speed or travel angle.
3. A model-free adaptive automatic driving car path tracking control method as claimed in claim 1, wherein: the partial format dynamic linearization data model is expressed as:
wherein phi is L (k)＝[φ 1 (k),φ 2 (k),...,φ L (k)] T Is a pseudo-gradient, is a time-varying vector;
ΔU L (k)＝[Δu(k),...,Δu(k-L+1)] T ；
l is the control input linearization length constant;
ΔU L (k)＝U L (k)-U L (k-1)；
at a transfer function of G p (s)e -τs And in the case of a slowly time-varying structure of the system parameters, τs+1 is used instead of e -τs To complete the function of the Smith predictor; the system controlling the effect DeltaU at time k L (k) The feedback amount under drive is expressed as:
where y' (k) is the derivative of y (k).
4. A model-free adaptive automatic driving car path tracking control method as claimed in claim 1, wherein: when the partial format dynamic linearization data model is used for obtaining a differential signal by using a second-order tracking differentiator, the differential signal is expressed by adopting the following formula:
wherein T is sampling time, x TD (k) And x' TD (k) Two output signals, x, of a 2 nd order track-differentiator TD (k) For y (k) tracking signal, x' TD (k) To track the differential signal of y (k), h and r are the filter factor and the velocity factor, respectively, fst (·) is a nonlinear function.
5. A model-free adaptive automatic driving car path tracking control method as claimed in claim 1, wherein: the FDL-IMFAC controller performs path control by adopting the following formula:
in order for the algorithm to have stronger time-varying parameter tracking capability, the reset algorithm is set as follows:
wherein,is phi L (k) Is the estimated value of (y) * (k+τ+1) is the desired output; eta and rho are step factors of the algorithm, and eta epsilon (0, 1)]，ρ∈(0,1]The method comprises the steps of carrying out a first treatment on the surface of the Lambda > 0, mu > 0 is the controller parameter.
6. A model-free adaptive automatic driving car path tracking control method as claimed in claim 1, wherein: the path control comprises transverse control and longitudinal control, wherein the transverse control controls the steering wheel to control the running direction by controlling the steering wheel angle so that the vehicle tracks a preset track; the longitudinal control controls the speed of the vehicle by controlling the opening degree of the accelerator/the brake, so that the vehicle runs at the expected speed, and the path tracking control of the unmanned automobile path is realized by separately designing the controllers for the longitudinal control and the transverse control respectively.
7. A model-free adaptive automatic driving car path tracking control method as claimed in claim 1, wherein: the vehicle is provided with a sensor, the sensor continuously transmits the state information data of the vehicle to the main control computer, and the main control computer calculates the state information data of the vehicle to obtain the expected speed and the expected position.
8. The model-free adaptive automatic driving automobile path tracking control method as claimed in claim 7, wherein: detecting the current speed and the current position through a sensor, and calculating the current control quantity of an accelerator or a brake pedal through a designed controller based on the expected speed and the current speed in the longitudinal control process; in the transverse control process, the control amount of the steering wheel rotation angle is calculated by using the current running posture and the expected running posture.
9. A model-free adaptive automatic driving car path tracking control method as claimed in claim 1, wherein: the vehicle is internally provided with a path tracking system, the path tracking system comprises a main control module, an executor module and a state sensing module, the main control module drives transverse and longitudinal control amounts through the executor module, and executes a transverse and longitudinal control algorithm, calculates the rotation angle of a steering wheel, calculates the opening of a throttle and a brake and transmits control instructions.
10. A model-free adaptive autopilot vehicle path tracking control method as defined in claim 9 wherein: the state sensing module is internally provided with a speed sensor, a laser radar and a GPS, wherein the speed sensor uploads vehicle speed information, the laser radar uploads vehicle surrounding obstacle information, the GPS uploads vehicle position information, and the state sensing module integrates the vehicle speed information, the vehicle surrounding obstacle information and the vehicle position information and uploads the vehicle speed information, the vehicle surrounding obstacle information and the vehicle position information into the main control module.
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