CN112666833A - Vehicle speed following self-adaptive robust control method for electric automatic driving vehicle - Google Patents
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Abstract
The invention discloses a vehicle speed following self-adaptive robust control method for an electric automatic driving vehicle, which comprises the following steps: step one, establishing a vehicle second-order longitudinal dynamics model containing uncertain parameters and external interference terms; step two, smoothing the step planning vehicle speed; thirdly, designing the generalized longitudinal force control rate of the vehicle by using a dynamic sliding mode control theory; step four, using RBF neural network to carry out online estimation on the uncertainty item; and step five, designing reasonable bottom-layer actuator switching logic. Has the advantages that: ensuring that the tracking target and its derivative converge to 0 at an exponential rate. The practical application problem of the conventional sliding mode control is relieved to a great extent. The buffeting problem of sliding mode control can be further relieved to a certain extent while the stability of the controller is guaranteed.
Description
Technical Field
The invention relates to a vehicle speed following self-adaptive robust control method, in particular to a vehicle speed following self-adaptive robust control method for an electric automatic driving vehicle.
Background
Currently, the autonomous vehicle is considered to be capable of greatly alleviating social problems caused by improper operation of a driver due to the advantage that the autonomous vehicle does not need to be operated by the driver, and the autonomous vehicle also has great potential application value in unstructured scenes such as military, agriculture or planetary exploration. The control layer is positioned at the bottom end of the automatic driving vehicle framework, and the vehicle speed tracking module is mainly responsible for accurately tracking the expected vehicle speed, so that the control layer has a very important position in realizing a complex automatic driving task.
The common vehicle speed tracking control algorithm generally uses a desired speed and an acceleration as controller inputs, calculates a proper driving moment or a proper braking pressure to control the vehicle so as to ensure a vehicle speed following effect, and mainly comprises PID control, sliding mode control, model prediction control and the like as shown in Chinese patent publication No. CN112026753A, Chinese patent publication No. CN109991856A and Chinese patent publication No. CN 110780674A.
Since the vehicle itself is a complex time-varying nonlinear system and there are several unknown disturbances in the actual interaction of the vehicle with the environment, these factors will all affect the tracking performance of the vehicle. The PID control robustness of the algorithm is poor, a large amount of setting work is needed for determining a proper PID parameter, a plurality of feedforward calibration quantities are matched to achieve a good control effect, and the debugging workload is large; the conventional sliding mode control has strong interference resistance but has the problem of output buffeting, the method described in the Chinese patent publication No. CN109991856A reduces the buffeting problem by using the fuzzy control principle, but the design of the fuzzy rule and the membership function still needs a large amount of debugging work; the model predictive control described in chinese patent publication No. CN110780674A has high requirements for model accuracy and real-time calculation capability of a processor, and has more limitations in practical application, so it is important to design a practical vehicle speed tracking control algorithm capable of handling factors such as uncertainty of parameters and unavoidable external interference.
Disclosure of Invention
The invention aims to provide a vehicle speed following adaptive robust control method for an electric automatic driving vehicle, which aims to solve the problems that parameters of the electric automatic driving vehicle are uncertain, and the tracking performance of the vehicle is influenced by a plurality of unknown interferences in the actual interaction process with the environment.
The invention provides a vehicle speed following self-adaptive robust control method for an electric automatic driving vehicle, which comprises the following steps:
step one, establishing a vehicle second-order longitudinal dynamics model containing uncertain parameters and external interference terms:
differentiation of the model by generalized longitudinal forcesAs input, with vehicle speed vxAnd longitudinal accelerationThe status is as follows:
in the formula, x1As the longitudinal speed v of the vehiclex;x2As derivative of longitudinal speed of vehicle Wherein C isDThe air resistance coefficient of the vehicle, rho is the air density, and A is the frontal area of the vehicle; u is the generalized longitudinal force F applied to the vehicletDerivative of (2) Is an interference item composed of uncertain parameters and external unknown interference together, whereinIs road gradient, F is tire rolling resistance coefficient, FdFor the vehicle to be subjected to external unknown interference, M is the total of the vehicleThe masses include a known vehicle trim mass m and an unknown additional bearing mass Δ m that may vary with the actual use of the vehicle;
step two, smoothing the step planning vehicle speed, which comprises the following specific steps:
smoothing the step planning vehicle speed by using a quadratic function, a linear function and a 2-1-2 spline curve which is connected end to end and can be guided everywhere;
the time function of the actual expected vehicle speed and the longitudinal acceleration is written according to the characteristics of a 2-1-2 spline curve into the following form:
in the formula vxdIs the processed desired vehicle speed; v. ofxd1、vxd2、vxd3、The piecewise expected vehicle speed and the longitudinal acceleration are respectively in the form of a quadratic function, a linear function and a quadratic function; a is0、a1、a2、b0、b1、c0、c1、c2Respectively is the coefficient corresponding to each segment of function; t is t0、t1、t2、t3Respectively, start-stop time nodes of each segment function, where t0In order to plan the time point of updating the vehicle speed, other time points need to be set by self, and according to the characteristic that a 2-1-2 spline curve is continuously guided everywhere, the following constraint conditions are obtained:
according to the three formulas, the concrete coefficient corresponding to each section of function can be solved, so that a continuously derivable expected vehicle speed curve is obtained;
step three, designing the generalized longitudinal force control rate of the vehicle by utilizing a dynamic sliding mode control theory, which comprises the following steps:
first a tracking error e is defined1=x1-x1d,x1dIs the system state quantity x1Is desired value, i.e. desired vehicle speed vxdTo ensure that the tracking error can converge to 0, a Lyapunov function V is defined1And derived to obtainAs follows:
define x according to the above formula2Expected value ofWherein phi1Is a normal number, the above formula is written asAccordingly e1May converge to 0 within a finite time and then define a second tracking errorConstructing a sliding mode function sigma according to two defined tracking errors1=φ2e1+e2Wherein phi2As a normal number, the tracking error e2Is brought into a sliding mode function to obtainTherefore, when the system enters the ideal sliding mode sigma1When 0, tracking error e1Andconverging the index to 0 to achieve a vehicle speed tracking control target;
designing the control rate of the generalized longitudinal force differential term based on the state space equation of the step one is shown as follows, whereinFor an unknown interference term D1Upper bound of, h1、h2Is a normal number:
the stability proves that:
defining Lyapunov function V2Derivative it and apply a sliding mode function sigma1The following steps are carried out:
two tracking errors e1=x1-x1d、And substituting the state space equation of the step one into the formula to obtain:
finally, the control rate designed in the third step is carried into to obtain the following formula, so as to ensure thatIn the case of (2), a corroborative sliding mode function σ1Will converge to 0 within a finite time;
wherein:
step four, using the RBF neural network to carry out online estimation on the uncertainty item, which comprises the following specific steps:
aiming at uncertain item upper bound under different driving environmentsThe defect which can not be predicted generally is solved by further using an RBF neural network to carry out online estimation on the uncertainty item;
according to the structural characteristics of the RBF neural network, the estimated value of the uncertainty is designed toWherein omega is the weight matrix of RBF neural network, h (sigma)1) For the intermediate hidden layer neuron output matrix, the neuron output matrix h (σ)1) The concrete form is shown as follows, wherein ciIs the central position of the ith neuron, biWidth for the ith neuron:
in defining RBF neural network omega, bi,ciBefore updating rules of three types of parameters, firstly giving adaptive control rate of a generalized longitudinal force differential term:
the updating rule of omega is obtained by stability analysis, and the specific steps are as follows:
some parameters are first defined: assume that the best estimate of the uncertainty term isIn the formula of omega*The weight is best estimated; error between optimum estimated value of uncertainty and actual valueThe difference between the network weight of the best estimate of uncertainty and the network weight of the actual estimate is
Then defining Lyapunov function V3Derivative it and apply a sliding mode function sigma1Two tracking errors e1、e2The specific form of (1) and the state space equation of step one are taken into:
finally, the control rate designed in the step four is substituted to obtain the following formula:
let the update rate of the RBF neural network weight be:
thus ensuring thatAndin the case of (2), a corroborative sliding mode function σ1Will converge to 0 within a finite time;
biand ciThe updating rule can be realized by a common gradient descent method, and the specific derivation steps are as follows:
firstly, defining a performance index function of the RBF network according to a control target of sliding mode control:
calculation of biGradient under performance indicator function E:
in the same way, c can be obtainedjGradient under performance indicator function E:
the parameter updating rule based on the gradient descent method is finally as follows:
in the formula of gammac,γbTo learn the rate, muc,μbIs a momentum factor;
step five, designing a reasonable bottom layer executor switching logic, specifically as follows:
according to the relation between the driving torque, the braking torque and the generalized longitudinal force, the switching rule is designed as follows:
in the formula Ft0> 0, represents the drive torque TdOr brake master cylinder pressure PbAn activation threshold of; f. ofd(Ft) And fb(Ft) Formulas for converting the generalized driving force into the driving torque and the master cylinder pressure are respectively expressed as follows:
in the formula rwIs the rolling radius of the tire, igTo the transmission ratio of the variator, ifTo distribute the torque of the front axle if0For front axle final drive ratio, ir0Is the transmission ratio of the rear axle main reducer, kbfIs the front wheel braking torque coefficient, kbrIs the rear wheel braking torque coefficient.
The invention has the beneficial effects that:
1. based on the thought of a back-stepping method, the invention redesigns the vehicle speed tracking sliding mode function and ensures that the tracking target and the derivative thereof can be converged to 0 at an exponential speed.
2. The method utilizes a dynamic sliding mode control principle, incorporates actual control input into a sliding mode function, transfers the buffeting defect of the conventional sliding mode control into a differential term by designing the control rate of the actual control input differential term, and finally performs buffeting inhibition through an integrator, thereby greatly relieving the practical application problem of the conventional sliding mode control.
3. The invention solves the problem that the uncertain interference upper bound of the longitudinal vehicle speed tracking is not suitable for obtaining by using the self-adaptive principle and the RBF neural network self-adaptive algorithm, thereby not only ensuring the stability of the controller, but also further relieving the buffeting problem of the sliding mode control to a certain extent.
Drawings
Fig. 1 is a schematic diagram of a general architecture of the trajectory tracking control of the electric autonomous vehicle according to the present invention.
FIG. 2 is a schematic diagram of a vehicle speed adaptive robust control architecture of an electric autopilot vehicle according to the present invention.
Detailed Description
Please refer to fig. 1 to 2:
the invention provides a strong-robustness vehicle speed following control method of an electric four-wheel-drive automatic driving vehicle, which specifically comprises the following steps of:
step one, establishing a vehicle second-order longitudinal dynamics model containing uncertain parameters and external interference terms.
In general, the longitudinal dynamics model of the vehicle ignores the influence of air resistance, ramp resistance and the like, but the method of the invention mainly researches the vehicle speed tracking control under the conditions that the parameters are uncertain and certain external interference is generated, and the two items cannot be ignored. Thus, a complete longitudinal dynamics model was used, as shown in the following formula:
wherein M is the total mass of the vehicle including the known vehicle service mass M and the unknown additional bearing mass Δ M that may vary with the actual use of the vehicle; v. ofxIs the vehicle longitudinal speed; the longitudinal acceleration of the vehicle can be expressed as the derivative of the longitudinal speed of the vehicle, irrespective of the influence of the yaw rate and the lateral speed when the vehicle is turningFtLongitudinal forces generated by the interaction of the vehicle tire with the ground; fslopeThe vehicle is subjected to ramp resistance; g is the acceleration of gravity;is the road grade; ffThe rolling resistance of the vehicle; f is the tire rolling resistance coefficient; fwindIs the air resistance borne by the vehicle; cDIs the vehicle air resistance coefficient; ρ is the air density; a is the frontal area of the vehicle; fdThe vehicle is subjected to external unknown interference.
Deriving said longitudinal dynamics model to obtain a differential with a generalized longitudinal forceAs input, with vehicle speed vxAnd longitudinal accelerationA second order model of the state, as shown in detail below:
in the formula, x1As the longitudinal speed v of the vehiclex;x2As derivative of longitudinal speed of vehicle u is the generalized longitudinal force F applied to the vehicletA derivative of (a);the interference item is composed of uncertain parameters and external unknown interference.
And step two, smoothing the step planning vehicle speed in order to improve the control effect of the controller.
In order to improve the control effect of the controller and ensure the riding comfort of the vehicle, a quadratic function, a linear function and a 2-1-2 spline curve which is connected end to end and can be guided everywhere are adopted to carry out smoothing treatment on the step planning vehicle speed.
The time function of the actual desired vehicle speed and longitudinal acceleration can be written in the following form according to the characteristics of the '2-1-2' spline curve:
in the formula vxdIs the processed desired vehicle speed; v. ofxd1、vxd2、vxd3、The piecewise expected vehicle speed and the longitudinal acceleration are respectively in the form of a quadratic function, a linear function and a quadratic function; a is0、a1、a2、b0、b1、c0、c1、c2Respectively is the coefficient corresponding to each segment of function; t is t0、t1、t2、t3Respectively, start-stop time nodes of each segment function, where t0In order to plan the time point of updating the vehicle speed, other time points need to be set by self. According to the characteristic of continuous guidance everywhere of the 2-1-2 spline curve, the following constraint conditions can be written:
writing the constraint in the form of a matrix:
Vxp=TC
in the formula VxpIs t0The vehicle speed information vector at the moment, T is a matrix formed by time information of each section of the spline curve, and C is a coefficient vector of each section of the spline curve, and the concrete form is as follows:
C=[a0 a1 a2 b0 b1 c0 c1 c2]T
in the above matrix, VxpAll of them beingAs is known, the time point information in T can be artificially defined, so that the coefficient vector C can be directly solved, and finally a smooth expected speed curve is obtained.
And step three, designing the generalized longitudinal force control rate of the vehicle by utilizing a dynamic sliding mode control theory based on the thought of a backstepping method.
First a tracking error e is defined1=x1-x1d,x1dIs the system state quantity x1Is desired value, i.e. desired vehicle speed vxd. To ensure that the tracking error can converge to 0, a Lyapunov function V is defined1And derived to obtainAs follows:
define x according to the above formula2Expected value ofWherein phi1Is a normal number, the above formula can be written asAccordingly e1Can converge to 0 within a limited time. Then defining a second tracking errorConstructing a sliding mode function sigma according to two defined tracking errors1=φ2e1+e2Wherein phi2As a normal number, the tracking error e2Is brought into a sliding mode function to obtainTherefore, when the system enters the ideal sliding mode sigma1When 0, tracking error e1Andand converging the index to 0 to achieve the vehicle speed tracking control target.
Designing the control rate of the generalized longitudinal force differential term based on the state space equation of the step one is shown as follows, whereinFor an unknown interference term D1Upper bound of, h1、h2Is a normal number:
the stability proves that:
defining Lyapunov function V2Derivative it and apply a sliding mode function sigma1=φ2e1+e2The following steps are carried out:
two tracking errors e1=x1-x1d、And substituting the state space equation of the step one into the formula to obtain:
finally, the control rate designed in the step three is substituted to obtain the following formula,
wherein:
under the assumption of phi1,φ2If it is determined that h can be set1Such that Q is a positive definite matrix, can be madeAnd the zero point is always smaller than zero in the interval of removing the zero point, so that the system is ensured to enter a sliding mode within limited time, and the control target is finally realized.
To make Q a positive definite matrix only guarantees the following:
step four, using RBF neural network to carry out online estimation on the uncertainty item;
aiming at uncertain item upper bound under different driving environmentsThe disadvantage that can not be predicted in general is solved by the step of performing online estimation on the uncertainty term by using an RBF neural network.
According to the structural characteristics of the RBF neural network, the estimated value of the uncertainty is designed toWherein omega is the weight matrix of RBF neural network, h (sigma)1) The matrix is output for the intermediate hidden layer neurons. Neuron output matrix h (σ)1) A concrete form can be represented as follows, wherein ciIs the central position of the ith neuron, biWidth for the ith neuron:
in defining RBF neural network omega, bi,ciBefore updating the rules of the three parameters, the generalized longitudinal force is given firstlyDifferential term adaptive control rate:
the updating rule of omega is obtained by stability analysis, and the specific steps are as follows:
some parameters are first defined: assume that the best estimate of the uncertainty term isIn the formula of omega*The weight is best estimated; error between optimum estimated value of uncertainty and actual valueThe difference between the network weight of the best estimate of uncertainty and the network weight of the actual estimate is
Then defining Lyapunov function V3Derivative it and apply a sliding mode function sigma1Two tracking errors e1、e2The specific form of (1) and the state space equation of step one are taken into:
finally, the control rate designed in the step four is substituted to obtain the following formula:
let the update rate of the RBF neural network weight be:
thus ensuring thatAndin the case of (2), a corroborative sliding mode function σ1Will converge to 0 within a finite time.
biAnd ciThe updating rule can be realized by a common gradient descent method, and the specific derivation steps are as follows:
firstly, defining a performance index function of the RBF network according to a control target of sliding mode control:
calculation of biGradient under performance indicator function E:
in the same way, c can be obtainedjGradient under performance indicator function E:
the parameter updating rule based on the gradient descent method is finally as follows:
in the formula of gammac,γbTo learn the rate, muc,μbIs a momentum factor.
Fifthly, reasonably designing bottom-layer actuator switching logic;
in general, the driving actuator and the braking actuator cannot act on the vehicle at the same time, so that a reasonably designed bottom-layer actuator switching logic is needed to ensure that only one actuator acts at the same time.
According to the relation between the driving torque, the braking torque and the generalized longitudinal force, the switching rule is designed as follows:
in the formula Ft0> 0, represents the drive torque TdOr brake master cylinder pressure PbAn activation threshold of; f. ofd(Ft) And fb(Ft) Formulas for converting the generalized driving force into the driving torque and the master cylinder pressure are respectively expressed as follows:
in the formula rwIs the rolling radius of the tire, igTo the transmission ratio of the variator, ifTo distribute the torque of the front axle if0For front axle final drive ratio, ir0Is the transmission ratio of the rear axle main reducer, kbfIs the front wheel braking torque coefficient, kbrIs the rear wheel braking torque coefficient.
Claims (1)
1. A vehicle speed following adaptive robust control method for an electric autonomous vehicle, characterized by: the method comprises the following steps:
step one, establishing a vehicle second-order longitudinal dynamics model containing uncertain parameters and external interference terms:
the model is based on generalized longitudinal forceDifferential of (2)As input, with vehicle speed vxAnd longitudinal accelerationThe status is as follows:
in the formula, x1As the longitudinal speed v of the vehiclex;x2As derivative of longitudinal speed of vehicle Wherein C isDThe air resistance coefficient of the vehicle, rho is the air density, and A is the frontal area of the vehicle; u is the generalized longitudinal force F applied to the vehicletDerivative of (2) Is an interference item composed of uncertain parameters and external unknown interference together, whereinIs road gradient, F is tire rolling resistance coefficient, FdThe total mass of the vehicle comprises known vehicle servicing mass M and unknown additional bearing mass Deltam which can change along with the actual use process of the vehicle;
step two, smoothing the step planning vehicle speed, which comprises the following specific steps:
smoothing the step planning vehicle speed by using a quadratic function, a linear function and a 2-1-2 spline curve which is connected end to end and can be guided everywhere;
the time function of the actual expected vehicle speed and the longitudinal acceleration is written according to the characteristics of a 2-1-2 spline curve into the following form:
in the formula vxdIs the processed desired vehicle speed; v. ofxd1、vxd2、vxd3、The piecewise expected vehicle speed and the longitudinal acceleration are respectively in the form of a quadratic function, a linear function and a quadratic function; a is0、a1、a2、b0、b1、c0、c1、c2Respectively is the coefficient corresponding to each segment of function; t is t0、t1、t2、t3Respectively, start-stop time nodes of each segment function, where t0In order to plan the time point of updating the vehicle speed, other time points need to be set by self, and according to the characteristic that a 2-1-2 spline curve is continuously guided everywhere, the following constraint conditions are obtained:
according to the three formulas, the concrete coefficient corresponding to each section of function can be solved, so that a continuously derivable expected vehicle speed curve is obtained;
step three, designing the generalized longitudinal force control rate of the vehicle by utilizing a dynamic sliding mode control theory, which comprises the following steps:
first a tracking error e is defined1=x1-x1d,x1dIs the system state quantity x1Is desired value, i.e. desired vehicle speed vxdTo ensure that the tracking error can converge to 0, a Lyapunov function V is defined1And derived to obtainAs follows:
define x according to the above formula2Expected value ofWherein phi1Is a normal number, the above formula is written asAccordingly e1May converge to 0 within a finite time and then define a second tracking errorConstructing a sliding mode function sigma according to two defined tracking errors1=φ2e1+e2Wherein phi2As a normal number, the tracking error e2Is brought into a sliding mode function to obtainTherefore, when the system enters the ideal sliding mode sigma1When 0, tracking error e1Andconverging the index to 0 to achieve a vehicle speed tracking control target;
designing the control rate of the generalized longitudinal force differential term based on the state space equation of the step one is shown as follows, whereinFor an unknown interference term D1Upper bound of, h1、h2Is a normal number:
the stability proves that:
defining Lyapunov function V2Derivative it and apply a sliding mode function sigma1The following steps are carried out:
two tracking errors e1=x1-x1d、And substituting the state space equation of the step one into the formula to obtain:
finally, the control rate designed in the third step is carried into to obtain the following formula, so as to ensure thatIn the case of (2), a corroborative sliding mode function σ1Will converge to 0 within a finite time;
wherein:
step four, using the RBF neural network to carry out online estimation on the uncertainty item, which comprises the following specific steps:
aiming at uncertain item upper bound under different driving environmentsThe defect which can not be predicted generally is solved by further using an RBF neural network to carry out online estimation on the uncertainty item;
according to the structural characteristics of the RBF neural network, the estimated value of the uncertainty is designed toWherein omega is the weight matrix of RBF neural network, h (sigma)1) For the intermediate hidden layer neuron output matrix, the neuron output matrix h (σ)1) The concrete form is shown as follows, wherein ciIs the central position of the ith neuron, biWidth for the ith neuron:
in defining RBF neural network omega, bi,ciBefore updating rules of three types of parameters, firstly giving adaptive control rate of a generalized longitudinal force differential term:
the updating rule of omega is obtained by stability analysis, and the specific steps are as follows:
some parameters are first defined: assume that the best estimate of the uncertainty term isIn the formula of omega*The weight is best estimated; error between optimum estimated value of uncertainty and actual valueThe difference between the network weight of the best estimate of uncertainty and the network weight of the actual estimate is
Then defining Lyapunov function V3Derivative it and apply a sliding mode function sigma1Two tracking errors e1、e2The specific form of (1) and the state space equation of step one are taken into:
finally, the control rate designed in the step four is substituted to obtain the following formula:
let the update rate of the RBF neural network weight be:
thus ensuring thatAndin the case of (2), a corroborative sliding mode function σ1Will converge to 0 within a finite time;
biand ciThe updating rule can be realized by a common gradient descent method, and the specific derivation steps are as follows:
firstly, defining a performance index function of the RBF network according to a control target of sliding mode control:
calculation of biGradient under performance indicator function E:
in the same way, c can be obtainedjGradient under performance indicator function E:
the parameter updating rule based on the gradient descent method is finally as follows:
in the formula of gammac,γbTo learn the rate, muc,μbIs a momentum factor;
step five, designing a reasonable bottom layer executor switching logic, specifically as follows:
according to the relation between the driving torque, the braking torque and the generalized longitudinal force, the switching rule is designed as follows:
in the formula Ft0> 0, represents the drive torque TdOr brake master cylinder pressure PbAn activation threshold of; f. ofd(Ft) And fb(Ft) Formulas for converting the generalized driving force into the driving torque and the master cylinder pressure are respectively expressed as follows:
in the formula rwIs the rolling radius of the tire, igTo the transmission ratio of the variator, ifTo distribute the torque of the front axle if0For front axle final drive ratio, ir0Is the transmission ratio of the rear axle main reducer, kbfIs the front wheel braking torque coefficient, kbrIs the rear wheel braking torque coefficient.
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Cited By (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN113221349A (en) * | 2021-05-10 | 2021-08-06 | 的卢技术有限公司 | Vehicle distance sliding mode control algorithm and system suitable for intelligent driving vehicle |
CN113359477A (en) * | 2021-07-13 | 2021-09-07 | 吉林大学 | Design method of vehicle longitudinal and lateral coupling trajectory tracking controller |
CN113359466A (en) * | 2021-06-30 | 2021-09-07 | 南通大学 | Fleet cooperative control method based on self-adaptive sliding mode control |
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CN113685398A (en) * | 2021-08-30 | 2021-11-23 | 吉林大学 | Integrated hydraulic braking system servo displacement control method |
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Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108248605A (en) * | 2018-01-23 | 2018-07-06 | 重庆邮电大学 | The transverse and longitudinal control method for coordinating that a kind of intelligent vehicle track follows |
CN108454623A (en) * | 2018-01-22 | 2018-08-28 | 大连理工大学 | A kind of unmanned electric vehicle Trajectory Tracking Control method of four motorized wheels |
CN108519773A (en) * | 2018-03-07 | 2018-09-11 | 西安交通大学 | The paths planning method of automatic driving vehicle under a kind of structured environment |
CN110824912A (en) * | 2018-08-08 | 2020-02-21 | 华为技术有限公司 | Method and apparatus for training a control strategy model for generating an autonomous driving strategy |
CN110816530A (en) * | 2019-11-14 | 2020-02-21 | 东风商用车有限公司 | Speed following control method and system of adaptive cruise system |
CN111497842A (en) * | 2020-04-30 | 2020-08-07 | 重庆大学 | Man-machine double-closed-loop layered cooperative longitudinal car following control method |
JP2020149178A (en) * | 2019-03-12 | 2020-09-17 | 株式会社日立製作所 | Control device |
CN111703423A (en) * | 2019-03-18 | 2020-09-25 | 长城汽车股份有限公司 | Longitudinal control safety monitoring method and system for automatic driving vehicle |
-
2020
- 2020-12-25 CN CN202011556853.5A patent/CN112666833B/en active Active
Patent Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108454623A (en) * | 2018-01-22 | 2018-08-28 | 大连理工大学 | A kind of unmanned electric vehicle Trajectory Tracking Control method of four motorized wheels |
CN108248605A (en) * | 2018-01-23 | 2018-07-06 | 重庆邮电大学 | The transverse and longitudinal control method for coordinating that a kind of intelligent vehicle track follows |
CN108519773A (en) * | 2018-03-07 | 2018-09-11 | 西安交通大学 | The paths planning method of automatic driving vehicle under a kind of structured environment |
CN110824912A (en) * | 2018-08-08 | 2020-02-21 | 华为技术有限公司 | Method and apparatus for training a control strategy model for generating an autonomous driving strategy |
JP2020149178A (en) * | 2019-03-12 | 2020-09-17 | 株式会社日立製作所 | Control device |
CN111703423A (en) * | 2019-03-18 | 2020-09-25 | 长城汽车股份有限公司 | Longitudinal control safety monitoring method and system for automatic driving vehicle |
CN110816530A (en) * | 2019-11-14 | 2020-02-21 | 东风商用车有限公司 | Speed following control method and system of adaptive cruise system |
CN111497842A (en) * | 2020-04-30 | 2020-08-07 | 重庆大学 | Man-machine double-closed-loop layered cooperative longitudinal car following control method |
Non-Patent Citations (1)
Title |
---|
张家旭等: "基于非线性干扰观测器的车轮滑移率跟踪控制", 《华中科技大学学报(自然科学版)》 * |
Cited By (12)
Publication number | Priority date | Publication date | Assignee | Title |
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CN113221349A (en) * | 2021-05-10 | 2021-08-06 | 的卢技术有限公司 | Vehicle distance sliding mode control algorithm and system suitable for intelligent driving vehicle |
CN113221349B (en) * | 2021-05-10 | 2024-05-03 | 的卢技术有限公司 | Inter-vehicle distance sliding mode control algorithm and system suitable for intelligent driving vehicle |
CN113359466A (en) * | 2021-06-30 | 2021-09-07 | 南通大学 | Fleet cooperative control method based on self-adaptive sliding mode control |
CN113359466B (en) * | 2021-06-30 | 2023-01-24 | 南通大学 | Fleet cooperative control method based on self-adaptive sliding mode control |
CN113359477A (en) * | 2021-07-13 | 2021-09-07 | 吉林大学 | Design method of vehicle longitudinal and lateral coupling trajectory tracking controller |
CN113655718A (en) * | 2021-08-25 | 2021-11-16 | 的卢技术有限公司 | Self-adaptive control method for distance between automatic driving vehicles based on sliding mode control |
CN113685398A (en) * | 2021-08-30 | 2021-11-23 | 吉林大学 | Integrated hydraulic braking system servo displacement control method |
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CN114056123A (en) * | 2021-09-08 | 2022-02-18 | 吉林大学 | Steep slope vehicle speed control method for centralized motor-driven vehicle |
CN114056123B (en) * | 2021-09-08 | 2023-11-07 | 吉林大学 | Steep slope vehicle speed control method for centralized motor driven vehicle |
CN114200841A (en) * | 2021-12-13 | 2022-03-18 | 电子科技大学 | Networked automobile system safety control method based on fuzzy backstepping |
CN114200841B (en) * | 2021-12-13 | 2023-05-23 | 电子科技大学 | Fuzzy backstepping-based network-connected automobile system safety control method |
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