CN117311346A - Robot transverse stability cooperative steering control method, device, terminal and medium - Google Patents
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Abstract
The invention discloses a method, a device, a terminal and a medium for controlling transverse stable cooperative steering of a robot, wherein the method comprises the steps of constructing a driving model of an intelligent robot global cooperative steering system; determining a main control target of the system; establishing a transverse stable cooperative steering dynamics model based on the actuator fault based on a neural network and a fuzzy modeling method; determining conditions which are required to be met by the gradual stability and the interference suppression performance of the transverse stability cooperative steering dynamics model; establishing a matrix inequality of global cooperative steering system stability; and performing linear optimization of the controller and solving a state feedback control gain, thereby obtaining the intelligent robot global cooperative steering controller. The intelligent robot control method aims at increasing the accuracy of the intelligent robot lateral stability control, achieves intelligent cooperative running on the premise of ensuring the safety and stability of the intelligent robot on the basis of the controller gain required by the cooperative steering control, and provides powerful support for the development of intelligent robot application technology.
Description
Technical Field
The invention relates to the technical field of intelligent robots, in particular to a transverse stability cooperative steering control method, a transverse stability cooperative steering control device, a terminal and a medium for a robot.
Background
With the development and application of artificial intelligence, communication technology and computer technology in China, intelligent robots meet new development opportunities. Particularly, application technology of mobile intelligent robots represented by cooperative steering control is a focus and a hot spot of industrial attention. At the current technology level, single active steering driving is not the best way to solve the problem of stable steering road traffic accidents of mobile robots. Therefore, a cooperative steering concept is proposed, which emphasizes the cooperative cooperation of active steering and auxiliary steering of the mobile intelligent robot, and provides a new thought and direction for the development of application technology of the mobile intelligent robot.
Because the inherent system of the robot is nonlinear in the running process, the longitudinal speed change and the tire cornering stiffness uncertainty of the robot have great influence on the transverse stability of the robot under various working conditions, and meanwhile, the steering behavior uncertainty and the possibly encountered actuator fault condition in the actual running process also have influence on the performance of the robot.
In view of the foregoing, it is highly desirable to invent an intelligent robot lateral stability cooperative steering control method that includes an accurate modeling method and integrates fault tolerant control into a cooperative steering controller.
Disclosure of Invention
The invention provides a method, a device, a terminal and a medium for controlling transverse stability cooperative steering of an intelligent robot, which are used for designing a cooperative steering controller aiming at the influence of system uncertainty parameters and actuator faults on an intelligent robot system, integrating fault-tolerant control into the cooperative steering controller and solving the problem of poor control effect of the transverse stability of the system caused by the system uncertainty parameters and the actuator faults.
In order to achieve the above object, in a first aspect, an embodiment of the present invention provides a method for controlling lateral stability cooperative steering of a robot, including:
based on a chassis system of the intelligent robot and degrees of freedom in two directions of transverse and yaw, a data processing platform is adopted to establish a transverse dynamics model and a path tracking model of the chassis system, so that a global cooperative steering system model of the intelligent robot is established;
setting a control target of the global cooperative steering system model through state feedback of a closed-loop system according to path tracking performance and lateral stability of the intelligent robot in the moving process;
based on uncertain tire cornering stiffness, a neural network and a fuzzy modeling method are adopted to establish a transverse stable cooperative steering dynamics model of the intelligent robot actuator fault according to the global cooperative steering system model, load distribution and road conditions;
establishing constraint conditions of stability and interference suppression performance of the transverse stable cooperative steering dynamics model through state feedback of the closed loop system; based on the stability and interference suppression performance of the transverse stable cooperative steering dynamics model, establishing a matrix inequality of the stability of the global cooperative steering system;
based on a matrix inequality of the global cooperative steering system stability, the controller is linearly optimized, and a state feedback control gain is obtained, so that the intelligent robot global cooperative steering controller is obtained, and the intelligent robot transverse stable cooperative steering is controlled.
As an improvement of the above solution, the lateral dynamics model is:
wherein m represents the mass of the intelligent robot, v x And v y Respectively represent the transverse and longitudinal speeds, I z Representing moment of inertia, gamma representing yaw rate, l f And l r Respectively represent the distance between the mass center and the front and rear axes of the tire, F yf And F yr The lateral forces of the front and rear tires are respectively shown;
the path tracking model is as follows:
wherein, ψ is a course angle error, e is a transverse error, and ρ is the curvature of the road;
the global cooperative steering system model is as follows:
wherein S is Laplacian, G d 、τ p And τ d Steering gain, pretightening time and reaction time lag respectively representing steering behavior characteristic parameters.
As an improvement of the above scheme, the building of the lateral stable cooperative steering dynamics model of the intelligent robot actuator fault by adopting a neural network and a fuzzy modeling method based on the uncertain tire cornering stiffness according to the global cooperative steering system model, load distribution and road condition comprises the following steps:
performing data normalization, data correlation analysis and sampling and wavelet denoising processing on the actuator fault data of the intelligent robot to obtain a training data set; the actuator fault data are data acquired by the intelligent robot under the working conditions of straight line running, curve running, annular running, s-shaped running and emergency obstacle avoidance running;
establishing a network structure, an activation function and a loss function of a fully-connected neural network model, and training the fully-connected neural network model through the training data set to obtain weight values and bias values of all neurons of the fully-connected neural network model, so as to obtain a trained fully-connected neural network model;
training and verifying the global cooperative steering system model based on the trained fully connected neural network model;
and modeling the tire dynamics by adopting a T-S fuzzy modeling method based on uncertain tire cornering stiffness according to load distribution and road conditions to obtain a transverse stable cooperative steering dynamics model.
As an improvement of the scheme, the normalization processing of the data is to map the data into decimal numbers between 0 and 1, and the normalization calculation formula is as follows:
wherein X' is the value of the original value of the variable X after mapping by a normalization function, X max And X min Representing the maximum and minimum values in variable X;
the calculation formula of wavelet denoising processing of the data is as follows:
where f (t) is the input signal, H, G is the decomposition coefficient of the high and low pass filters, t is the time of the discrete sequence, j is the number of decomposition layers, A j Representing the approximation component coefficients of the j-th layer of the signal, D j Representing the detail component coefficients of the j-th layer of the signal.
As an improvement of the above scheme, the loss function of the fully connected neural network model is:
wherein L is the loss function of the fully connected neural network model, y i For the ith true value of the fully connected neural network model,outputting a value for the ith network of the fully-connected neural network model, wherein N is the number of the true values of the fully-connected neural network model;
the calculation formula of the neuron is as follows:
wherein y is the output of the neuron, x i For the input of neuron i, i denotes the number of the input variable, w i B is the weight value of the neuron i i For the bias value of the neuron i, σ is an activation function;
the output vector of the fully connected neural network model is as follows:
wherein Y is l For the output vector of the full-connection neural network model for the first time, X is an input vector, l represents the number of the current vector, and sigma l For the first activation function,for a weight matrix W l The weight values of the r-th row and the c-th column in the matrix, c and r respectively represent the column number and the line number of the weight matrix, and x c Is the input vector for neuron c, +.>For the bias value of the first neuron c, B l A bias value vector for the first time of the fully connected neural network model;
the calculation formulas of the weight value and the bias value of the neuron are as follows:
where α is a learning rate, W is a weight value, and b is a bias value.
As an improvement of the above-described scheme, the expression for describing the tire nonlinear dynamics of the uncertain tire cornering stiffness is:
C i ＝C i0 +N ci ΔC i ，
where i=f and r denote front and rear wheels, respectively. C (C) i0 And DeltaC i Representing nominal and uncertainty values of tire cornering stiffness, respectively; n (N) Ci To uncertainty the coefficient, we need to satisfy |N Ci |<1；
The expression of the uncertain parameters of the global cooperative steering system model is as follows:
N χ ≤1,χ∈{τ d ,G d ,τ p }，
wherein τ d0 For initial reaction time lag τ p0 For initial pre-sighting time, deltaτ d G as the rate of change of the reaction time lag d0 For initial steering gain ΔG d To change the steering gain, N τd N, an uncertain parameter of system reaction time lag Gd An uncertainty parameter for the system pre-sighting time,frequency corresponding to system reaction time lag, +.>Frequency corresponding to system reaction time lag, +.>Is the overall frequency of the steering system;
the state equation of the transverse stable cooperative steering dynamics model is as follows:
in the formula, the state vector x (t) = [ v ] y γψeσ d ]Control input vector u (t) =δ c T The external disturbance input is defined as ω (t) =ρ, a, Δ A, B 1 、B 2 、ΔB 2 Are coefficient matrices.
As an improvement of the above solution, the calculation formula of the state feedback of the closed loop system is:
‖z(t)‖ 2 <γ g ‖ω(t)‖ 2 ，
wherein z (t) is the output value of the closed loop system, ω (t) is the input disturbance value existing in the closed loop system, and γ g Is a specific constant gamma g >0。
The condition of the state feedback of the closed loop system is as follows:
the state feedback controller of the closed loop system exists and satisfies H ∞ Specific constant gamma of progressive stability performance criterion g If and only if there is a positive definite matrix P, control the gain matrix K j Sum positive scalar epsilon 1 ,ε 2 And satisfies the following conditions:
wherein,
Υ 11 ＝[P(A i +ΔA i )] s +[P(B 2 +ΔB 2 )F f K j ] s ，
γ 22 ＝diag{-γ g 2 I -I -ε 1 I -ε 1 I -ε 2 I -ε 2 I}。
in a second aspect, an embodiment of the present invention provides a lateral stability cooperative steering control device for a robot, including:
the system model building module is used for building a transverse dynamics model and a path tracking model of the chassis system based on the chassis system of the intelligent robot and the degrees of freedom in the transverse direction and the yaw direction by adopting a data processing platform so as to build a global cooperative steering system model of the intelligent robot;
the control target setting module is used for setting a control target of the global cooperative steering system model through state feedback of a closed-loop system according to the path tracking performance and the lateral stability of the intelligent robot in the moving process;
the dynamics model building module is used for building a transverse stable cooperative steering dynamics model of the intelligent robot actuator fault by adopting a neural network and a fuzzy modeling method based on uncertain tire cornering stiffness according to the global cooperative steering system model, load distribution and road condition;
the matrix inequality module is used for establishing constraint conditions of stability and interference suppression performance of the transverse stable cooperative steering dynamics model through state feedback of the closed-loop system; based on the stability and interference suppression performance of the transverse stable cooperative steering dynamics model, establishing a matrix inequality of the stability of the global cooperative steering system;
and the steering controller module is used for carrying out linear optimization on the controller based on a matrix inequality stabilized by the global cooperative steering system and solving a state feedback control gain to obtain the intelligent robot global cooperative steering controller so as to control the intelligent robot to transversely stabilize cooperative steering.
In a third aspect, an embodiment of the present invention correspondingly provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, where the processor implements the above-mentioned method for controlling lateral stability cooperative steering of a robot when executing the computer program.
In addition, the embodiment of the invention also provides a computer readable storage medium, which comprises a stored computer program, wherein the equipment where the computer readable storage medium is located is controlled to execute the method for controlling the transverse stability cooperative steering of the robot when the computer program runs.
Compared with the prior art, the transverse stability cooperative steering control method, device, terminal and medium for the robot disclosed by the embodiment of the invention are characterized in that a data processing platform is adopted to build a transverse dynamics model and a path tracking model of a chassis system based on the chassis system and two degrees of freedom of transverse and transverse directions of an intelligent robot, so that a global cooperative steering system model of the intelligent robot is built; setting a control target of the global cooperative steering system model through state feedback of a closed-loop system according to path tracking performance and lateral stability of the intelligent robot in the moving process; based on uncertain tire cornering stiffness, a neural network and a fuzzy modeling method are adopted to establish a transverse stable cooperative steering dynamics model of the intelligent robot actuator fault according to the global cooperative steering system model, load distribution and road conditions; establishing constraint conditions of stability and interference suppression performance of the transverse stable cooperative steering dynamics model through state feedback of the closed loop system; based on the stability and interference suppression performance of the transverse stable cooperative steering dynamics model, establishing a matrix inequality of the stability of the global cooperative steering system; based on a matrix inequality of the global cooperative steering system stability, the controller is linearly optimized, and a state feedback control gain is obtained, so that the intelligent robot global cooperative steering controller is obtained, and the intelligent robot transverse stable cooperative steering is controlled. Therefore, the embodiment of the invention can creatively establish a transverse stable cooperative steering dynamics model containing actuator faults based on a fully-connected neural network and a fuzzy modeling method aiming at the condition that the intelligent robot system has parameter uncertainty, and fully considers the influence of load distribution and road condition change on robot modeling; based on the controller gain required by realizing the cooperative steering control, the intelligent cooperative driving can be realized on the premise of ensuring the safety and stability of the intelligent robot, and powerful support is provided for the development of the application technology of the intelligent robot; the cooperative steering controller is designed, fault tolerance control is integrated into the cooperative steering controller, and the cooperative steering controller is practical.
Drawings
Fig. 1 is a schematic flow chart of a method for controlling lateral stability cooperative steering of a robot according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a lateral stability cooperative steering control device for a robot according to an embodiment of the present invention.
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.
It should be noted that the terms "comprises" and "comprising," along with any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Referring to fig. 1, fig. 1 is a flow chart of a method for controlling lateral stability cooperative steering of a robot according to an embodiment of the present invention, where the method includes steps S11 to S15:
s11: based on a chassis system of the intelligent robot and degrees of freedom in two directions of transverse and yaw, a data processing platform is adopted to establish a transverse dynamics model and a path tracking model of the chassis system, so that a global cooperative steering system model of the intelligent robot is established;
s12: setting a control target of the global cooperative steering system model through state feedback of a closed-loop system according to path tracking performance and lateral stability of the intelligent robot in the moving process;
the control target of the global cooperative steering system model is that the path tracking performance and the transverse stability of the intelligent robot in the moving process are improved to the greatest extent while the safety of the intelligent robot is ensured; the control target is state feedback of a closed loop system:
‖z(t)‖ 2 <γ g ‖ω(t)‖ 2 ，
wherein z (t) is the output value of the closed loop system, ω (t) is the input disturbance value existing in the closed loop system, and γ g Is a specific constant gamma g >0。
S13: based on uncertain tire cornering stiffness, a neural network and a fuzzy modeling method are adopted to establish a transverse stable cooperative steering dynamics model of the intelligent robot actuator fault according to the global cooperative steering system model, load distribution and road conditions;
s14: establishing constraint conditions of stability and interference suppression performance of the transverse stable cooperative steering dynamics model through state feedback of the closed loop system; based on the stability and interference suppression performance of the transverse stable cooperative steering dynamics model, establishing a matrix inequality of the stability of the global cooperative steering system;
s15: based on a matrix inequality of the global cooperative steering system stability, the controller is linearly optimized, and a state feedback control gain is obtained, so that the intelligent robot global cooperative steering controller is obtained, and the intelligent robot transverse stable cooperative steering is controlled.
Specifically, in the step S11, the lateral dynamics model is:
wherein m represents the mass of the intelligent robot, v x And v y Respectively represent the transverse and longitudinal speeds, I z Representing moment of inertia, gamma representing yaw rate, l f And l r Respectively represent the distance between the mass center and the front and rear axes of the tire, F yf And F yr The lateral forces of the front and rear tires are respectively shown;
the path tracking model is as follows:
wherein, ψ is a course angle error, e is a transverse error, and ρ is the curvature of the road;
the global cooperative steering system model is as follows:
wherein S is Laplacian, G d 、τ p And τ d Steering gain, pretightening time and reaction time lag respectively representing steering behavior characteristic parameters.
It should be noted that the global cooperative steering system model is built by taking MATLAB Simulink as a platform and is used for later verification of the feasibility of the designed controller; f (F) yf And F yr The lateral forces of the front and rear tires are respectively noted and can be expressed as:
F yf ＝C f α f ，
F yr ＝C r α r ，
wherein C is f And C r The cornering stiffness of the front and rear tires, respectively, and the cornering angle alpha of the front and rear tires f And alpha r The method can be obtained by the following formula:
wherein,R s active steering angle delta for steering system gear ratio d Auxiliary rotation angle delta with controller c Together as an input to the steering system.
Specifically, in the step S13, the method specifically includes:
performing data normalization, data correlation analysis and sampling and wavelet denoising processing on the actuator fault data of the intelligent robot to obtain a training data set; the actuator fault data are data acquired by the intelligent robot under the working conditions of straight line running, curve running, annular running, s-shaped running and emergency obstacle avoidance running;
establishing a network structure, an activation function and a loss function of a fully-connected neural network model, and training the fully-connected neural network model through the training data set to obtain weight values and bias values of all neurons of the fully-connected neural network model, so as to obtain a trained fully-connected neural network model;
training and verifying the global cooperative steering system model based on the trained fully connected neural network model;
and modeling the tire dynamics by adopting a T-S fuzzy modeling method based on uncertain tire cornering stiffness according to load distribution and road conditions to obtain a transverse stable cooperative steering dynamics model.
It should be noted that the function of the data correlation analysis is to explore the correlation existing between the data, reject the highly correlated variable, achieve the purpose of data dimension reduction and redundancy elimination. In order to effectively distinguish abrupt change and noise in the signals, a wavelet decomposition and reconstruction method is introduced to denoise the transverse acceleration signals. The input vector X of the fully-connected neural network model contains 16 elements and represents 16 input features, and the input layer of the network correspondingly consists of 16 neurons; the output layer is correspondingly composed of two neurons, because of the two classification tasks. Each neuron is composed of a linear summation operation and an activation function, wherein the linear summation refers to the linear superposition of data of input neurons, and the activation function is used for nonlinear linearization of the linear superposition result. The loss function is one of important criteria for evaluating the network, the value of the loss function represents the deviation between the output value and the actual value of the neural network, and the cross entropy is selected as the loss function in the embodiment of the invention.
The learning rate determines the speed at which the parameter moves to the optimum value, and if the learning rate is too high, the parameter may go beyond the optimum value, so that the optimum value is not achieved, and if the learning rate is small, the reverse propagation efficiency is too low to be converged within a certain time.
The training data set of the fully-connected neural network model is derived from CarSim software, the data set is collected under various working conditions of straight line running, curve running, annular running, s-shaped running and emergency obstacle avoidance running, so that the built global cooperative steering system model is trained and verified, the input vector of the fully-connected neural network model is a robot corner and a road curvature, the output quantity is yaw angular acceleration and lateral acceleration at the current moment, and the network structure is a two-layer node number.
Because of variations in load distribution and road conditions, tire dynamics inevitably encounter highly nonlinear characteristics, and thus uncertain tire cornering stiffness is used to describe tire nonlinearity dynamics.
More specifically, in the step S14, the normalization process of the data maps the data to a fraction between 0 and 1, and the calculation formula of the normalization is:
wherein X' is the value of the original value of the variable X after mapping by a normalization function, X max And X min Representing the maximum and minimum values in variable X;
the calculation formula of wavelet denoising processing of the data is as follows:
where f (t) is the input signal, H, G is the decomposition coefficient of the high and low pass filters, t is the time of the discrete sequence, j is the number of decomposition layers, A j Representing the approximation component coefficients of the j-th layer of the signal, D j Representing the detail component coefficients of the j-th layer of the signal.
More specifically, the loss function of the fully connected neural network model is:
wherein L is the loss function of the fully connected neural network model, y i For the ith true value of the fully connected neural network model,outputting a value for the ith network of the fully-connected neural network model, wherein N is the number of the true values of the fully-connected neural network model;
the calculation formula of the neuron is as follows:
wherein y is the output of the neuron, x i For the input of neuron i, i denotes the number of the input variable, w i B is the weight value of the neuron i i For the bias value of the neuron i, σ is an activation function;
the output vector of the fully connected neural network model is as follows:
wherein Y is l For the output vector of the full-connection neural network model for the first time, X is an input vector, l represents the number of the current vector, and sigma l For the first activation function,for a weight matrix W l The weight values of the r-th row and the c-th column in the matrix, c and r respectively represent the column number and the line number of the weight matrix, and x c Is the input vector for neuron c, +.>To be the instituteBias value of the first neuron, B l A bias value vector for the first time of the fully connected neural network model;
the calculation formulas of the weight value and the bias value of the neuron are as follows:
where α is a learning rate, W is a weight value, and b is a bias value.
More specifically, the expression for describing tire non-linear dynamics for the uncertain tire cornering stiffness is:
C i ＝C i0 +N ci ΔC i ，
where i=f and r denote front and rear wheels, respectively. C (C) i0 And DeltaC i Representing nominal and uncertainty values of tire cornering stiffness, respectively; n (N) Ci To uncertainty the coefficient, we need to satisfy |N Ci |<1；
The expression of the uncertain parameters of the global cooperative steering system model is as follows:
N χ ≤1,χ∈{τ d ,G d ,τ p }，
wherein τ d0 For initial reaction time lag τ p0 For initial pre-sighting time, deltaτ d G as the rate of change of the reaction time lag d0 For initial steering gain ΔG d To change the steering gain, N τd N, an uncertain parameter of system reaction time lag Gd An uncertainty parameter for the system pre-sighting time,frequency corresponding to system reaction time lag, +.>Frequency corresponding to system reaction time lag, +.>Is the overall frequency of the steering system;
the state equation of the transverse stable cooperative steering dynamics model is as follows:
in the formula, the state vector x (t) = [ v ] y γψeδ d ]Control input vector u (t) =δ c T The external disturbance input is defined as ω (t) =ρ, a, Δ A, B 1 、B 2 、ΔB 2 Are coefficient matrices.
Wherein, the coefficient matrix is respectively:
specifically, the calculation formula of the state feedback of the closed loop system is as follows:
‖z(t)‖ 2 <γ g ‖ω(t)‖ 2 ，
wherein z (t) is the output value of the closed loop system, ω (t) is the input disturbance value existing in the closed loop system, and γ g Is a specific constant gamma g >0。
The condition of the state feedback of the closed loop system is as follows:
the state feedback controller of the closed loop system exists and satisfies H ∞ Specific constant gamma of progressive stability performance criterion g If and only if there is a positive definite matrix P, control the gain matrix K j Sum positive scalar epsilon 1 ,ε 2 And the conditions are as follows:
wherein,
Υ 11 ＝[P(A i +ΔA i )] s +[P(B 2 +ΔB 2 )F f K j ] s ，
γ 22 ＝diag{-γ g 2 I -I -ε 1 I -ε 1 I -ε 2 I -ε 2 I}。
the transverse stable cooperative steering dynamics model is a closed-loop system, and conditions required to be met by the closed-loop system for gradual stability and interference suppression performance in a limited frequency domain range are state feedback of the closed-loop system.
The existence quotients are as follows:
lemma 1: for a given positive scalar gamma g If there is and only one symmetric positive definite matrix P satisfies the following inequality, the closed loop system is progressively stable and satisfies H ∞ The performance is as follows:
and (4) lemma 2: given matrix Ω=Ω T X and Γ, for all satisfying Λ T Λ is less than or equal to I, and the following inequality holds:
Ω+XΛΓ+(XΛΓ) T <0，
if and only if there is a positive constant ε:
the following inequality can be deduced to hold according to equation lemma 1:
wherein, pi can be transformed into:
specifically, the controller is linearly optimized and the state feedback control gain is obtained, so that the intelligent robot global cooperative steering controller is obtained, and the specific principle is as follows:
the state feedback controller of the closed loop system exists and meets H ∞ Progressive stability performance criterion gamma g If and only if there is a positive definite matrix P, control the gain matrixSum positive scalar epsilon 1 ,ε 2 And satisfies the following conditions:
wherein,
/>
and (3) proving: definition matrixMultiplying the front and rear of formula (1) by +.>And transpose thereof, and set q=p -1 Is->The following formula can be derived:
wherein the method comprises the steps of
Θ 22 ＝diag{-γ g 2 I -I -ε 1 I -ε 1 I -ε 2 I -ε 2 I}。
The above formula is transformed by combining the lemma 2, and the following can be obtained:
solving for linearity using MATLABThe convex optimization problem of matrix inequality, the system control gain can be determined byAnd (5) calculating to obtain the product.
The optimization mode is based on linearization theory, so that inequality is linearized, and the original matrix has two unknown matrixes and cannot be directly solved.
Fig. 2 is a schematic structural diagram of a lateral stability cooperative steering control device for a robot according to an embodiment of the present invention, where the lateral stability cooperative steering control device for a robot includes:
the system model building module 21 is used for building a transverse dynamics model and a path tracking model of the chassis system based on the chassis system of the intelligent robot and the degrees of freedom of the transverse direction and the yaw direction by adopting a data processing platform so as to build a global cooperative steering system model of the intelligent robot;
the control target setting module 22 is configured to set a control target of the global cooperative steering system model according to the path tracking performance and the lateral stability of the intelligent robot in the moving process through state feedback of a closed loop system;
the dynamics model building module 23 is configured to build a lateral stable cooperative steering dynamics model of the intelligent robot with an actuator fault by using a neural network and a fuzzy modeling method based on uncertain tire cornering stiffness according to the global cooperative steering system model, load distribution and road conditions;
a matrix inequality module 24 for establishing constraints on stability and interference suppression performance of the laterally stable cooperative steering dynamics model through state feedback of the closed loop system; based on the stability and interference suppression performance of the transverse stable cooperative steering dynamics model, establishing a matrix inequality of the stability of the global cooperative steering system;
the steering controller module 25 is configured to perform linear optimization on the controller based on the matrix inequality of the global cooperative steering system stability, and calculate a state feedback control gain, so as to obtain the intelligent robot global cooperative steering controller, so as to control the intelligent robot to perform lateral stable cooperative steering.
The transverse stable cooperative steering control device for the robot provided by the embodiment of the invention can realize all the processes of the transverse stable cooperative steering control method for the robot of the embodiment, and the functions and the realized technical effects of each module in the device are respectively corresponding to the functions and the realized technical effects of the transverse stable cooperative steering control method for the robot of the embodiment and are not repeated here.
The embodiment of the invention correspondingly provides a terminal device, which comprises: a processor, a memory, and a computer program stored in the memory and executable on the processor. The steps in the embodiment of the method for controlling the transverse stability cooperative steering of the robot are realized when the processor executes the computer program. Or the processor executes the computer program to realize the functions of each module in the embodiment of the transverse stability cooperative steering control device of the robot.
The terminal equipment can be computing equipment such as a desktop computer, a notebook computer, a palm computer, a cloud server and the like. The terminal device may include, but is not limited to, a processor, a memory. It will be appreciated by those skilled in the art that the schematic diagram is merely an example of a terminal device and does not constitute a limitation of the terminal device, and may include more or less components than illustrated, or may combine certain components, or different components, e.g., the terminal device may further include an input-output device, a network access device, a bus, etc.
The processor may be a central processing unit, but also other general purpose processors, digital signal processors, application specific integrated circuits, field programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is a control center of the terminal device, and which connects various parts of the entire terminal device using various interfaces and lines.
The memory may be used to store the computer program and/or module, and the processor may implement various functions of the terminal device by running or executing the computer program and/or module stored in the memory and invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data (such as audio data, phonebook, etc.) created according to the use of the handset, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart memory card, at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device.
It should be noted that the above-described apparatus embodiments are merely illustrative, and the units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
The embodiment of the invention also provides a computer readable storage medium, which comprises a stored computer program, wherein the equipment where the computer readable storage medium is located is controlled to execute the robot transverse stability cooperative steering control method according to the embodiment when the computer program runs.
In summary, according to the method, the device, the terminal and the medium for controlling the transverse stable cooperative steering of the robot disclosed by the embodiment of the invention, a data processing platform is adopted to build a transverse dynamics model and a path tracking model of a chassis system based on the chassis system and two degrees of freedom of transverse and transverse directions of the intelligent robot, so that a global cooperative steering system model of the intelligent robot is built; setting a control target of the global cooperative steering system model through state feedback of a closed-loop system according to path tracking performance and lateral stability of the intelligent robot in the moving process; based on uncertain tire cornering stiffness, a neural network and a fuzzy modeling method are adopted to establish a transverse stable cooperative steering dynamics model of the intelligent robot actuator fault according to the global cooperative steering system model, load distribution and road conditions; establishing constraint conditions of stability and interference suppression performance of the transverse stable cooperative steering dynamics model through state feedback of the closed loop system; based on the stability and interference suppression performance of the transverse stable cooperative steering dynamics model, establishing a matrix inequality of the stability of the global cooperative steering system; based on a matrix inequality of the global cooperative steering system stability, the controller is linearly optimized, and a state feedback control gain is obtained, so that the intelligent robot global cooperative steering controller is obtained, and the intelligent robot transverse stable cooperative steering is controlled. Therefore, the embodiment of the invention can creatively establish a transverse stable cooperative steering dynamics model containing actuator faults based on a fully-connected neural network and a fuzzy modeling method aiming at the condition that the intelligent robot system has parameter uncertainty, and fully considers the influence of load distribution and road condition change on robot modeling; based on the controller gain required by realizing the cooperative steering control, the intelligent cooperative driving can be realized on the premise of ensuring the safety and stability of the intelligent robot, and powerful support is provided for the development of the application technology of the intelligent robot; the cooperative steering controller is designed, fault tolerance control is integrated into the cooperative steering controller, and the cooperative steering controller is practical.
While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that changes and modifications may be made without departing from the principles of the invention, such changes and modifications are also intended to be within the scope of the invention.
Claims (10)
1. The transverse stability cooperative steering control method for the robot is characterized by comprising the following steps of:
based on a chassis system of the intelligent robot and degrees of freedom in two directions of transverse and yaw, a data processing platform is adopted to establish a transverse dynamics model and a path tracking model of the chassis system, so that a global cooperative steering system model of the intelligent robot is established;
setting a control target of the global cooperative steering system model through state feedback of a closed-loop system according to path tracking performance and lateral stability of the intelligent robot in the moving process;
based on uncertain tire cornering stiffness, a neural network and a fuzzy modeling method are adopted to establish a transverse stable cooperative steering dynamics model of the intelligent robot actuator fault according to the global cooperative steering system model, load distribution and road conditions;
establishing constraint conditions of stability and interference suppression performance of the transverse stable cooperative steering dynamics model through state feedback of the closed loop system; based on the stability and interference suppression performance of the transverse stable cooperative steering dynamics model, establishing a matrix inequality of the stability of the global cooperative steering system;
based on a matrix inequality of the global cooperative steering system stability, the controller is linearly optimized, and a state feedback control gain is obtained, so that the intelligent robot global cooperative steering controller is obtained, and the intelligent robot transverse stable cooperative steering is controlled.
2. The method for controlling lateral stability cooperative steering of a robot according to claim 1, wherein the lateral dynamics model is:
wherein m represents the mass of the intelligent robot, v x And v y Respectively represent the transverse and longitudinal speeds, I z Representing moment of inertia, gamma representing yaw rate, l f And l r Respectively represent the distance between the mass center and the front and rear axes of the tire, F yf And F yr The lateral forces of the front and rear tires are respectively shown;
the path tracking model is as follows:
wherein, ψ is a course angle error, e is a transverse error, and ρ is the curvature of the road;
the global cooperative steering system model is as follows:
wherein S is Laplacian, G d 、τ p And τ d Steering gain, pretightening time and reaction time lag respectively representing steering behavior characteristic parameters.
3. The method for controlling the lateral stability cooperative steering of the robot according to claim 1, wherein the building of the lateral stability cooperative steering dynamics model of the intelligent robot for the actuator failure by using a neural network and a fuzzy modeling method based on the uncertain tire cornering stiffness according to the global cooperative steering system model, the load distribution and the road condition conditions specifically comprises:
performing data normalization, data correlation analysis and sampling and wavelet denoising processing on the actuator fault data of the intelligent robot to obtain a training data set; the actuator fault data are data acquired by the intelligent robot under the working conditions of straight line running, curve running, annular running, s-shaped running and emergency obstacle avoidance running;
establishing a network structure, an activation function and a loss function of a fully-connected neural network model, and training the fully-connected neural network model through the training data set to obtain weight values and bias values of all neurons of the fully-connected neural network model, so as to obtain a trained fully-connected neural network model;
training and verifying the global cooperative steering system model based on the trained fully connected neural network model;
and modeling the tire dynamics by adopting a T-S fuzzy modeling method based on uncertain tire cornering stiffness according to load distribution and road conditions to obtain a transverse stable cooperative steering dynamics model.
4. The method for controlling lateral stability cooperative steering of a robot according to claim 3, wherein the normalization processing of the data is mapping the data to a fraction between 0 and 1, and the calculation formula of the normalization is:
wherein X' is the value of the original value of the variable X after mapping by a normalization function, X max And X min Representing the maximum and minimum values in variable X;
the calculation formula of wavelet denoising processing of the data is as follows:
where f (t) is the input signal, H, G is the decomposition coefficient of the high and low pass filters, t is the time of the discrete sequence, j is the number of decomposition layers, A j Representing the approximation component coefficients of the j-th layer of the signal, D j Representing the detail component coefficients of the j-th layer of the signal.
5. The method for controlling lateral stability cooperative steering of a robot according to claim 3, wherein the loss function of the fully connected neural network model is:
wherein L is the loss function of the fully connected neural network model, y i For the ith true value of the fully connected neural network model,outputting a value for the ith network of the fully-connected neural network model, wherein N is the number of the true values of the fully-connected neural network model;
the calculation formula of the neuron is as follows:
wherein y is the output of the neuron, x i For the input of neuron i, i denotes the number of the input variable, w i B is the weight value of the neuron i i For the bias value of the neuron i, σ is an activation function;
the output vector of the fully connected neural network model is as follows:
wherein Y is l For the output vector of the full-connection neural network model for the first time, X is an input vector, l represents the number of the current vector, and sigma l For the first activation function,for the rightHeavy matrix W l The weight values of the r-th row and the c-th column in the matrix, c and r respectively represent the column number and the line number of the weight matrix, and x c Is the input vector for neuron c, +.>For the bias value of the first neuron c, B l A bias value vector for the first time of the fully connected neural network model;
the calculation formulas of the weight value and the bias value of the neuron are as follows:
where α is a learning rate, W is a weight value, and b is a bias value.
6. A method of controlling lateral stability cooperative steering of a robot as set forth in claim 3, wherein the expression for describing tire non-linear dynamics of the uncertain tire cornering stiffness is:
C i ＝C i0 +N ci ΔC i ，
where i=f and r denote front and rear wheels, respectively. C (C) i0 And DeltaC i Representing nominal and uncertainty values of tire cornering stiffness, respectively; n (N) Ci To uncertainty the coefficient, we need to satisfy |N Ci |<1；
The expression of the uncertain parameters of the global cooperative steering system model is as follows:
N χ ≤1,χ∈{τ d ,G d ,τ p }，
wherein τ d0 For initial reaction time lag τ p0 For initial pre-sighting time, deltaτ d G as the rate of change of the reaction time lag d0 For initial steering gain ΔG d To change the steering gain, N τd N, an uncertain parameter of system reaction time lag Gd An uncertainty parameter for the system pre-sighting time,frequency corresponding to system reaction time lag, +.>Frequency corresponding to system reaction time lag, +.>Is the overall frequency of the steering system;
the state equation of the transverse stable cooperative steering dynamics model is as follows:
in the formula, the state vector x (t) = [ v ] y γ ψ e δ d ]Control input vector u (t) =δ c T The external disturbance input is defined as ω (t) =ρ，A、ΔA、B 1 、B 2 、ΔB 2 Are coefficient matrices.
7. The method for controlling lateral stability cooperative steering of a robot according to claim 1, wherein the calculation formula of the state feedback of the closed loop system is:
||z(t)|| 2 ＜γ g ||ω(t)|| 2 ，
wherein z (t) is the output value of the closed loop system, ω (t) is the input disturbance value existing in the closed loop system, and γ g Is a specific constant gamma g ＞0。
The condition of the state feedback of the closed loop system is as follows:
the state feedback controller of the closed loop system exists and satisfies H ∞ Specific constant gamma of progressive stability performance criterion g If and only if there is a positive definite matrix P, control the gain matrix K j Sum positive scalar epsilon 1 ,ε 2 And satisfies the following conditions:
wherein,
8. a robot lateral stability cooperative steering control device, characterized by comprising:
the system model building module is used for building a transverse dynamics model and a path tracking model of the chassis system based on the chassis system of the intelligent robot and the degrees of freedom in the transverse direction and the yaw direction by adopting a data processing platform so as to build a global cooperative steering system model of the intelligent robot;
the control target setting module is used for setting a control target of the global cooperative steering system model through state feedback of a closed-loop system according to the path tracking performance and the lateral stability of the intelligent robot in the moving process;
the dynamics model building module is used for building a transverse stable cooperative steering dynamics model of the intelligent robot actuator fault by adopting a neural network and a fuzzy modeling method based on uncertain tire cornering stiffness according to the global cooperative steering system model, load distribution and road condition;
the matrix inequality module is used for establishing constraint conditions of stability and interference suppression performance of the transverse stable cooperative steering dynamics model through state feedback of the closed-loop system; based on the stability and interference suppression performance of the transverse stable cooperative steering dynamics model, establishing a matrix inequality of the stability of the global cooperative steering system;
and the steering controller module is used for carrying out linear optimization on the controller based on a matrix inequality stabilized by the global cooperative steering system and solving a state feedback control gain to obtain the intelligent robot global cooperative steering controller so as to control the intelligent robot to transversely stabilize cooperative steering.
9. A terminal device comprising a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the robot lateral stability cooperative steering control method according to any one of claims 1 to 7 when executing the computer program.
10. A computer readable storage medium, characterized in that the computer readable storage medium comprises a stored computer program, wherein the computer program, when run, controls a device in which the computer readable storage medium is located to perform the robot lateral stability cooperative steering control method according to any one of claims 1 to 7.
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