Automated guided vehicle and path tracking control method and control system thereof

Abstract

The invention provides an automatic guided vehicle (AGV) and a path tracking control method and system thereof. The method comprises the following steps: S10, determining a system state matrix A and a control input matrix B of the path tracking control system of the AGV; s20, constructing a quadratic form performance index function of the AGV, and determining a state weighting matrix Q1 (t) and a control weighting matrix Q2 (t) according to the quadratic form performance index function; s30, solving a Riccati equation according to the system state matrix A, the control input matrix B, the state weighting matrix Q1 (t) and the control weighting matrix Q2 (t) to obtain a symmetric positive definite matrix P; s40, calculating a state feedback gain matrix K according to the symmetric positive definite matrix P, the control weighting matrix Q2 (t) and the control input matrix B, wherein the state feedback gain matrix K is used for defining optimal control input; s50, determining an optimal control law according to the state feedback gain matrix K; and S60, realizing AGV path tracking control according to the optimal control law. Therefore, the efficiency and the reliability of automatic guide transportation are improved.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to an AGV automatic trajectory control method, and specifically to an automatic guided vehicle and a path tracking control method and control system thereof. Background Art

[0002] At present, the Automatic Guided Vehicle (AGV) is an unmanned transport vehicle equipped with an electromagnetic or optical automatic guidance device. It can automatically drive and stop at any position along the specified route according to the instructions issued by the central control system and the pre-designed program, and complete a series of operating functions. It has many advantages such as high transportation efficiency, energy saving, reliable operation, flexible transportation, flexible use, and pollution-free, and has been widely used in many fields. From supermarkets and workshops to large automated warehouses, hospitals and distribution centers, it has become one of the main symbols of industrial automation.

[0003] With the development of factory automation, computer integrated manufacturing system technology, and the widespread application of flexible manufacturing systems and automated warehouses, automatic guided vehicles, as a means of connecting and adjusting discrete logistics systems, have become a necessary tool for automated handling and loading and unloading, and their application scope and technical level have developed rapidly. In the corresponding research field, path tracking control technology is a key technology in the research.

[0004] Therefore, it is very important to design a path tracking control system with small tracking error, fast dynamic response, adaptability to a variety of complex environments, and good robustness. Summary of the invention

[0005] In view of this, an object of an embodiment of the present invention is to provide an automatic guided vehicle and a path tracking control method and control system thereof to solve the above-mentioned technical problems.

[0006] To achieve the above object, in a first aspect, the present invention provides a path tracking control method for an automatic guided vehicle (AGV), which comprises the following steps:

[0007] S10: Determine the system state matrix A and the control input matrix B of the path tracking control system of the automatic guided vehicle AGV;

[0008] S20: constructing a quadratic performance index function of the automatic guided vehicle AGV, and determining a state weighted matrix Q1(t) and a control weighted matrix Q2(t) according to the quadratic performance index function;

[0009] S30: Solve the Riccati equation according to the system state matrix A, the control input matrix B, the state weight matrix Q1(t) and the control weight matrix Q2(t) to obtain a symmetric positive definite matrix P;

[0010] S40: Calculate a state feedback gain matrix K according to the symmetric positive definite matrix P, the control weight matrix Q2(t) and the control input matrix B, which is used to define the optimal control input;

[0011] S50: Determine the optimal control law according to the state feedback gain matrix K;

[0012] S60: Implementing path tracking control of the AGV according to the optimal control law.

[0013] In some possible implementations, the quadratic performance indicator function is:

[0014]

[0015] The quadratic performance index function consists of the following two parts:

[0016]

[0017] Among them, J1 represents the sum of squares of errors generated in the process of state deviation from equilibrium state, and J2 represents the energy consumption of control input in the control process;

[0018] x(t) is the state vector of the path tracking control system of the automated guided vehicle AGV;

[0019] u(t) is the control input of the path tracking control system of the automated guided vehicle AGV, representing the control variable of the AGV at time t;

[0020] Q1(t) is the state weight matrix, which is an n×n dimensional semi-positive definite matrix;

[0021] Q2(t) is the control weight matrix, which is an r×r dimensional positive definite matrix.

[0022] In some possible implementations, step S30 specifically includes:

[0023] S31: Construct the Riccati equation, which is expressed as:

[0024]

[0025] Where A is the system state matrix; B is the control input matrix; Q1(t) is the state weight matrix; Q2(t) is the control weight matrix; P is the symmetric positive definite matrix to be solved;

[0026] S32: Solve the Riccati equation to obtain a symmetric positive definite matrix P.

[0027] In some possible implementations, step S40 specifically includes:

[0028] The state feedback gain matrix of the path tracking control system of the automatic guided vehicle AGV is determined according to the following formula:

[0029] In some possible implementations, step S50 specifically includes:

[0030] According to the state feedback gain matrix K, the optimal control law is obtained as follows: u(t)=-Kx(t).

[0031] In some possible implementations, the method further includes step S70:

[0032] The stability of the path tracking control system is verified by the characteristic value of the closed-loop system matrix A-BK;

[0033] If the eigenvalues ​​of the closed-loop system matrix have negative real parts, then the path-following control system is asymptotically stable.

[0034] In a second aspect, a control system of an automatic guided transport vehicle for implementing any of the above methods is provided, comprising: a controller, a sensor system and an actuator;

[0035] The controller is specifically used for:

[0036] Determine the system state matrix A and control input matrix B of the path tracking control system of the automated guided vehicle AGV;

[0037] Constructing a quadratic performance index function of an automated guided vehicle AGV, and determining a state weighting matrix Q1(t) and a control weighting matrix Q2(t) according to the quadratic performance index function;

[0038] Solving the Riccati equation according to the system state matrix A, the control input matrix B, the state weight matrix Q1(t) and the control weight matrix Q2(t) to obtain a symmetric positive definite matrix P;

[0039] Calculating a state feedback gain matrix K according to the symmetric positive definite matrix P, the control weighting matrix Q2(t) and the control input matrix B, which is used to define the optimal control input;

[0040] Determining an optimal control law according to the state feedback gain matrix K;

[0041] Implementing path tracking control of the AGV according to the optimal control law;

[0042] The sensor system is used to monitor and collect the status information of the automatic guided vehicle AGV in real time, including the position, speed and direction, and feed the status information back to the controller so as to update the state vector x(t) to achieve real-time control;

[0043] The actuator is used to receive the optimal control law generated by the controller and convert the optimal control law into actual operation instructions to drive the automatic guided vehicle AGV to move along a predetermined trajectory to achieve the trajectory tracking control.

[0044] In some possible implementations, the control system further includes:

[0045] Path planning module, used to generate the desired path for the AGV;

[0046] The error calculation module is used to calculate the deviation between the current state of the AGV and the expected path and adjust the control signal;

[0047] The controller is further used to adjust the control signal according to the deviation to make the AGV move along the desired path.

[0048] According to a third aspect, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the method described in the first aspect is implemented.

[0049] In a fourth aspect, an automated guided vehicle is provided, which includes the control system described above.

[0050] The above technical solution has the following beneficial effects:

[0051] By determining the system state matrix A and the control input matrix B, the dynamic characteristics of the AGV can be accurately described, laying the foundation for subsequent control design. This precise modeling can improve the control accuracy of the control system on the position, speed and direction of the AGV, allowing the AGV to better travel along the predetermined trajectory.

[0052] By constructing a quadratic performance index function and selecting the state weighting matrix and the control weighting matrix, a trade-off in path tracking performance is achieved. The state weighting matrix can minimize the error of AGV deviation from the trajectory, while the control weighting matrix limits the consumption of control energy. This design effectively balances the control effect and energy consumption, enabling the AGV to achieve high-precision path tracking with minimal energy consumption.

[0053] By solving the Riccati equation, the symmetric positive definite matrix P is obtained, and based on this, the state feedback gain matrix K is calculated to ensure the calculation of the optimal control input. Such an optimal feedback gain design improves the response speed and control accuracy of the system, enables the AGV to quickly adjust when path deviation occurs, and improves the real-time and stability of path tracking.

[0054] The optimal control law is generated based on the state feedback gain matrix K, so that the control input u(t) can respond to the state changes of the AGV in real time, ensuring that the AGV can always stay on the desired path under different working conditions and interferences. The design of this optimal control law enhances the robustness of the system and can operate stably even in complex environments.

[0055] The optimal control law is used to realize the path tracking control of the AGV, so that the characteristic value of the closed-loop system matrix A-BK has a negative real part, thereby ensuring the asymptotic stability of the system. This stability design enables the AGV to maintain the accuracy of path tracking during long-term operation and is not prone to deviation and loss of control.

[0056] In the present invention, the AGV can run along a predetermined trajectory with high precision and low energy consumption. The path tracking control method not only improves the control effect of the AGV, but also enhances the stability and robustness of the system, enabling it to operate stably in a variety of environments and tasks, thereby improving the efficiency and reliability of automatic guided transportation. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. It is obvious that the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0058] Figure 1 It is a flow chart of a path tracking control method of an automatic guided vehicle AGV according to an embodiment of the present invention;

[0059] Figure 2 is a functional block diagram of a control system of an automatic guided transport vehicle according to an embodiment of the present invention;

[0060] Figure 3 is a functional block diagram of a computer-readable storage medium according to an embodiment of the present invention;

[0061] Figure 4 It is a functional block diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0063] Embodiment 1

[0064] From the path or trajectory of the AGV, different algorithms will make the AGV take different routes, so the time it takes for the AGV to reach the target is different, and the accuracy of its path tracking is also different. From an economic point of view, it is more cost-effective to spend less time and use less energy under the condition of meeting the control accuracy requirements.

[0065] In terms of control algorithms, PID control is the most widely used. It only needs to adjust three parameters: proportional coefficient, integral time constant and differential time constant to make the system control index meet the control requirements. When the control object model is relatively clear, the control algorithm is adopted according to the system characteristics and the parameters are reasonably selected to obtain a relatively ideal control effect. With the development of modern control theory, combining control with control technologies such as output feedback or state feedback control can often achieve better control effects.

[0066] 1. Analysis of the automatic guided vehicle control process

[0067] During the operation of the automatic guided vehicle, due to some inherent errors in the system, such as poor synchronization of the two drive motors, difference in the diameter of the two drive wheels, difference in the transmission effect of the reducer, and some non-system errors, such as the slippage between the wheels and the ground, the inertial load caused by the displacement of the center of gravity of the loaded goods, etc., even if the vehicle does not deviate at the beginning, these errors will accumulate with the increase of running time, causing the vehicle to deviate. Therefore, how to detect and correct the deviation is the primary task of the vehicle control system.

[0068] The state space equation of the control system is:

[0069] In the formula,

[0070] The output equation of the control system is: y = Cx + DΔT. In the formula, C = (0 1), D = (0). Among them, a = 1.165, b = 0.2342.

[0071] In order to achieve path tracking control, when the automatic guided vehicle has deviations in the tracking path, the deviation is eliminated by changing the deflection angle of the driving wheel of the vehicle. Since the definite relationship between the action time of the remote control switch and the two deviations has been established, the correction action can be achieved by controlling the switch time. Although the controlled process of the automatic guided vehicle is continuous, the control input information acquisition and controller implementation are completed by the computer, so this system is a computer sampling control system. The controller design methods of the sampling control system are divided into two categories, one is the design method based on the continuous system, and the other is the direct discretization design method. Due to the inherent time discreteness and amplitude quantization effects of digital signals, there are many differences between the computer sampling control system and the continuous control system, and special theories should be used to analyze and design the computer control system.

[0072] However, since the sampling period of the automatic guided vehicle control system is 0.02s, the period is short, the discrete effect in time can be ignored, the computer word length is currently bits, and the accuracy of the cycle and conversion of the vehicle control system is bits, and the quantization effect can also be ignored. But when the sampling period is relatively small, the discrete effect in time can be ignored, and the computer conversion and operation word length is relatively long, the quantization effect on amplitude can be ignored, the analysis and design methods of continuous systems can be used to study the design problems of computer sampling control systems.

[0073] 2. Analysis of controllability and observability

[0074] Modern control theory is based on describing the system with state space equations, and there are problems with the controllability and observability of the state. The state space equation describes the change process of the state x(t) caused by the input ΔT(t). The output equation describes the change of the output y(t) caused by the change of the state x(t). Controllability and observability are to analyze the control ability of the input ΔT(t) on the state x(t) and the reflection ability of the output y(t) on the state.

[0075] The necessary and sufficient condition for the system to be controllable and observable is the controllability matrix Q c =(B AB ... A n-1 B) and the observability matrix Q0 = (C CA ... CA n-1 ) are all full rank, that is, rank(Q c )=n and rank(Q0)=n.

[0076] In this embodiment, the rank of the controllability matrix is:

[0077]

[0078] The rank of the observability matrix is:

[0079]

[0080] Therefore, the mathematical model of the control system is controllable and observable, and the AGV can be controlled through optimal control theory.

[0081] 3. Optimal control algorithm for AGV

[0082] Figure 1 FIG. 1 is a flow chart of a path tracking control method of an automatic guided vehicle (AGV) according to an embodiment of the present invention. Figure 1 As shown, it includes the following steps:

[0083] S10: Determine the system state matrix A and the control input matrix B of the path tracking control system of the automatic guided vehicle AGV;

[0084] S20: constructing a quadratic performance index function of the automatic guided vehicle AGV, and determining a state weighted matrix Q1(t) and a control weighted matrix Q2(t) according to the quadratic performance index function;

[0085] S30: Solve the Riccati equation according to the system state matrix A, the control input matrix B, the state weight matrix Q1(t) and the control weight matrix Q2(t) to obtain a symmetric positive definite matrix P;

[0086] S40: Calculate a state feedback gain matrix K according to the symmetric positive definite matrix P, the control weight matrix Q2(t) and the control input matrix B, which is used to define the optimal control input;

[0087] S50: Determine the optimal control law according to the state feedback gain matrix K;

[0088] S60: Implementing path tracking control of the AGV according to the optimal control law.

[0089] In some embodiments, step S10 may specifically include the following sub-steps:

[0090] S11: Establish a kinematic or dynamic model of the AGV.

[0091] First, according to the specific motion mode of the AGV, its kinematic or dynamic model is established. This involves analyzing the physical structure and motion characteristics of the AGV, such as whether it is differential drive, omnidirectional drive or other drive mode. The kinematic model is suitable for describing low-speed motion characteristics such as speed and position, while the dynamic model is suitable for considering the influence of physical characteristics such as mass and inertia on motion. In this step, the basic motion equation of the AGV can be obtained, which provides a basis for the determination of the subsequent matrix.

[0092] S12: Define system state variables and construct the state vector x(t).

[0093] This step defines the state variables of the system according to the control requirements of the AGV. For example, in the path tracking problem, the state variables include position (x and y coordinates), velocity, acceleration, and direction angle θ. These state variables are combined into a state vector x(t), which can fully describe the state of the AGV at any point in time. This state vector is the basis for constructing the system state matrix A.

[0094] S13: Linearize system model (if applicable).

[0095] If the motion model of the AGV is nonlinear, it needs to be linearized so that the state equation can be expressed as a linear system form. Linearization is to perform Taylor expansion on the nonlinear equation near the equilibrium point or the desired working state and retain only the first-order terms. Through linearization, the system equation can be simplified to a linear form, which makes it easier to define matrices A and B. For example, the equilibrium point of the AGV at a constant speed and direction can be selected for linearization.

[0096] S14: Determine the system state matrix A. After determining the state vector x(t) and completing linearization (if necessary), the system state matrix A is then calculated. The state matrix A describes the dynamic changes of the system state, that is, the natural evolution of the system when there is no control input u(t). By analyzing the mutual relationships and dependencies between the state variables, these relationships are expressed in the matrix A. Specifically, each element of the matrix A can be calculated by the partial derivative of the state equation with respect to the state variable.

[0097] S15: Determine the control input matrix B.

[0098] After obtaining the state matrix A, the control input matrix B is determined to describe the influence of the control input u(t) on the state vector x(t). The control input includes physical inputs such as the thrust or torque of the drive motor, and the direct influence of these inputs on the state is reflected by the matrix B. Each element of the matrix B can be calculated by the partial derivative of the state equation with respect to the control input variable, thereby determining how different inputs act on each state variable.

[0099] S16: Verify the accuracy of the system state matrix A and the control input matrix B.

[0100] After calculating the system state matrix A and the control input matrix B, verification is performed to ensure that these matrices can accurately describe the dynamic characteristics of the AGV. The output of the model can be compared with the response of the real system through simulation or experimental testing. If the deviation is found to be too large, the model can be modified, such as re-linearization or reselection of state variables, until the A and B matrices can accurately characterize the system.

[0101] Through the above sub-steps, the state matrix A and control input matrix B of the AGV path tracking control system can be finally obtained, providing a basis for the subsequent optimal control design.

[0102] In some embodiments, the quadratic performance indicator function is:

[0103]

[0104] The quadratic performance index function consists of the following two parts:

[0105]

[0106] Among them, J1 represents the sum of squares of errors generated in the process of state deviation from equilibrium state, and J2 represents the energy consumption of control input in the control process;

[0107] x(t) is the state vector of the path tracking control system of the automated guided vehicle AGV;

[0108] u(t) is the control input of the path tracking control system of the automated guided vehicle AGV, representing the control variable of the AGV at time t;

[0109] Q1(t) is the state weight matrix, which is an n×n dimensional semi-positive definite matrix;

[0110] Q2(t) is the control weight matrix, which is an r×r dimensional positive definite matrix.

[0111] In some embodiments, step S30 specifically includes:

[0112] S31: Construct the Riccati equation, which is expressed as:

[0113]

[0114] Where A is the system state matrix; B is the control input matrix; Q1(t) is the state weight matrix; Q2(t) is the control weight matrix; P is the symmetric positive definite matrix to be solved;

[0115] S32: Solve the Riccati equation to obtain a symmetric positive definite matrix P.

[0116] In some embodiments, step S40 specifically includes:

[0117] The state feedback gain matrix of the path tracking control system of the automatic guided vehicle AGV is determined according to the following formula:

[0118] In some embodiments, step S50 specifically includes:

[0119] According to the state feedback gain matrix K, the optimal control law is obtained as follows: u(t)=-Kx(t).

[0120] In some embodiments, the method further comprises step S70:

[0121] The stability of the path tracking control system is verified by the characteristic value of the closed-loop system matrix A-BK;

[0122] If the eigenvalues ​​of the closed-loop system matrix have negative real parts, then the path-following control system is asymptotically stable.

[0123] The following is a detailed description:

[0124] For linear systems, if the integral of the quadratic function of the state variable and the control variable is taken as the performance index, this dynamic system optimization problem is called the optimal control problem of the quadratic performance index of the linear system, or linear quadratic problem for short. The quadratic performance index has a distinct physical meaning and represents a large number of performance index requirements proposed in engineering practice. The outstanding feature of the optimal control of the quadratic performance index is its linear control law, that is, its feedback control effect can be proportional to the change of the system state, that is, ΔT = kx, which is easy to form a closed-loop optimal control and easy to implement in engineering. Therefore, it has been widely used in practical engineering problems and is one of the most important achievements in modern control theory.

[0125] The mathematical model of the controlled process of the system in this embodiment is linear and steady, and is suitable for using quadratic performance indicators to design the optimal controller. The form of the quadratic performance functional is:

[0126]

[0127] Where Q1(t) is the state weight matrix that changes with time, which is an n×n dimensional semi-positive definite matrix; Q2(t) is the control weight matrix that changes with time, which is an r×r dimensional positive definite matrix; Q0(t) is the terminal weight matrix, which is an n×n dimensional semi-positive definite matrix. In engineering practice, Q1(t) and Q2(t) are symmetric matrices and take symmetric angles.

[0128] In optimal control, the positive definite control weight matrix Q2(t) ensures that the energy consumption of the control input is always positive, which can clearly express the cost of the control input. This property of the positive definite matrix makes the objective function convex when solving the optimal control problem, so that a unique optimal solution can be found.

[0129] In optimal control, the semi-positive definite state weight matrix Q1(t) allows errors in some states to have no direct cost or penalty. For example, in some directions of some system states, there is no penalty for errors. This adds flexibility in the optimization and allows the system to be less sensitive to errors in some directions.

[0130] Therefore, the positive definite matrix ensures that the quadratic form is always positive, which is suitable for representing the energy consumption of the control input and ensuring that the control cost is strictly greater than zero. The semi-positive definite matrix ensures that the quadratic form is non-negative, which is suitable for state errors and allows some directions to be free of cost in weighting.

[0131] Specifically, Q1(t) is a matrix for weighting state errors, which defines the system's sensitivity to deviations from the target state during regulation. Q2(t) is a matrix for weighting control inputs, reflecting the degree of concern for controlling energy consumption.

[0132] In the formula, J refers to the performance index of the control system, which is called the performance functional. This index measures the energy consumed by the control system during the adjustment process and the size of the error from the desired state. The goal of optimal control is to minimize this performance index.

[0133] Where x(t) is the state vector of the trajectory or path tracking control system of the automated guided vehicle (AGV), which is used to describe the state of the system at time t (e.g., position, speed, etc.). In AGV trajectory control, the state vector can include the position information, speed, angle, etc. of the AGV, which is used to characterize the motion state of the current system.

[0134] In the formula, x T represents the transpose of the state vector x. In mathematics, the transpose operation transforms a column vector into a row vector, or vice versa. In control systems and optimization problems, x is a column vector (i.e., a column with multiple components, such as a collection of state variables such as position and velocity), and x T Transpose this column vector to a row vector. This representation is used for matrix multiplication, for example x T Q1(t)x, this gives us a scalar (single value) that is used to calculate the weighted sum of the states.

[0135] Where u(t) is the control input of the system, representing the control variable of the AGV at time t. This variable can include the torque or thrust of the motor, which are forces or instructions directly acting on the AGV through the control system to guide it to track the desired trajectory.

[0136] In the formula, u Trepresents the transpose of the control input vector u(t). In the control system, u(t) can be a column vector containing multiple control variables (such as the thrust, torque and other control signals of the AGV). T , it is converted into a row vector.

[0137] Similar to the transpose x of the state vector x(t) T ,u T Q2(t)u is used in conjunction with matrix multiplication, where u T The product of and u is used to compute a weighted sum of the control energy, yielding a scalar (a single number) that measures the magnitude of the control input and its consumption.

[0138] In the expression of control theory and optimal control problems, the time variable t is usually omitted to simplify the expression of the formula. This omission is a common practice because the state and control inputs are essentially time-varying throughout the analysis and calculation process. In order to reduce the redundancy of symbols, simplified symbols are often used in formulas, that is, x and u are used instead of x(t) and u(t).

[0139] In the formula, x T Q1(t)x represents the weighted sum of the state vector x(t) under the state weighting matrix Q1(t), reflecting the magnitude of the error when the system state deviates from the target value. The state weighting matrix Q1(t) enables different state vectors (such as position error and velocity error) to be distinguished according to their importance.

[0140] In the formula, u T Q2(t)u represents the weighted sum of the control input u(t) under the control weighting matrix Q2(t), reflecting the consumption of control energy. The weighting of the control variables by the control weighting matrix Q2(t) determines the energy distribution and consumption in the control process.

[0141] Where, t f is the termination time of the control process. In the AGV trajectory control problem, the termination time t f The time point corresponding to the end of the task, or the stable time point when the AGV reaches the target position.

[0142] These parameters together constitute the quadratic optimal control problem of AGV trajectory control. By adjusting the values ​​of matrices Q1(t) and Q2(t), a balance point can be found between state error and control energy consumption, thereby achieving optimal control of the system.

[0143] The control purpose of the path tracking control system of the automatic guided vehicle is to keep the system in a balanced state. Therefore, the controller design of this embodiment is essentially a linear regulator design problem. During the movement of the automatic guided vehicle, the controller is always in effect, so it can be considered that the system terminal control time t f →∞, so this system is an infinite time linear regulator. At this time, the terminal error term has no meaning, that is, the terminal weighting matrix Q0(t) takes zero.

[0144] Therefore, the performance index function expression adopted by the optimal controller design in this embodiment is:

[0145]

[0146] It can be seen that the performance indicator function consists of the following two parts:

[0147]

[0148] J1 indicates that the square integral of the error that deviates from the equilibrium state is the minimum during the adjustment process. That is, when the system state deviates from the equilibrium state for some reason, the system can be restored to the equilibrium state under the action of the regulator. Specifically, J1 represents the sum of the squares of the errors generated by the system in the process of deviation from the equilibrium state. The smaller this term is, the closer the system state is to the target state.

[0149] J2 limits the control energy to prevent the control action from entering the saturated working area. Because the speed regulation range of the speed regulating mechanism of the automatic guided vehicle is limited, the corresponding input voltage has a certain effective working range. Specifically, J2 represents the energy consumption of the control input during the control process. The smaller this item is, the less energy is consumed during the control process.

[0150] Therefore, the optimal control exists and is unique, namely:

[0151]

[0152] In the formula, u * (t) is the optimal control input of the system at time t, which is the optimal control law obtained by minimizing the performance index J.

[0153] In the formula, is the inverse matrix of the control weight matrix Q2(t), and since Q2(t) is a positive definite matrix, it is invertible.

[0154] In the formula, B T is the transpose of the system input matrix B. The input matrix B describes how the control input u acts on the state x.

[0155] Where P is an n×n dimensional symmetric positive definite matrix that satisfies the Riccati equation, called the Riccati matrix. The Riccati matrix is ​​used to construct the optimal feedback gain matrix K when solving the optimal control problem.

[0156] Where x(t) is the state vector of the system, which describes the current state of the system (such as position, velocity, etc.) at time t.

[0157] Where A is the system state matrix, which describes the dynamic changes of the system state.

[0158] Where Q1(t) is the state weighting matrix, which is used to weight the state error and defines the cost of deviating from the desired state.

[0159] This formula indicates that in the optimal control problem, by solving the Riccati equation, the optimal control input u can be obtained. * (t), thereby achieving optimal control of the system state.

[0160] Where P is an n×n dimensional positive definite constant matrix that satisfies the Riccati matrix algebraic equation.

[0161] In optimal control, the Riccati equation is the key equation for solving the optimal control law. For the linear quadratic regulator (LQR) problem, the expression of the continuous-time algebraic Riccati equation (ARE) is:

[0162]

[0163] Where A is the state matrix of the system, which describes the dynamic characteristics of the system.

[0164] Where B is the control input matrix of the system, which describes the impact of the control input on the system state.

[0165] Where Q1(t) is the state weight matrix or state weight matrix, which is used in quadratic performance indicators to measure the cost of state errors.

[0166] Where Q2(t) is the control weight matrix or control weight matrix, which is used in quadratic performance indicators to measure the cost of control input.

[0167] Where P is the symmetric positive definite matrix to be solved, which is obtained by solving the Riccati equation.

[0168] Solving this algebraic Riccati equation yields the matrix P, which can then be used to compute the optimal feedback gain matrix K, thereby constructing the optimal control law.

[0169]

[0170] In the formula, is the optimal state trajectory of the system, indicating that the system is under the optimal control input u * (t) State evolution under the action of

[0171] In the formula, Or A-BK is the closed-loop system matrix. Under optimal control, this closed-loop system matrix describes the dynamic characteristics of the system. This equation shows that under the optimal control law, the system state will evolve according to the characteristics of the closed-loop system matrix.

[0172] Assuming x(t0) = x0, the optimal trajectory is the solution of the following linear steady-state equation (i.e., the aforementioned linear steady-state mathematical model):

[0173] The minimum value of the quadratic performance functional is:

[0174]

[0175] In the formula, J * (x(t0)) is the minimum value of the performance index J under optimal control.

[0176] Where x(t0) is the initial state vector of the system.

[0177] In the formula, This expression represents the minimum value of the performance functional corresponding to the initial state vector x(t0) under optimal control.

[0178] In the formula, x *T represents the optimal state vector x * The transpose of the column vector x * Convert to a row vector for inner product operations in quadratic performance indicators or matrix operations. * It refers to the optimal state vector of the system under the optimal control law, that is, under the optimal control input u * (t) is the ideal trajectory reached by the system state.

[0179] In the formula, x *T (t0)Px(t0) is used to calculate the minimum value of the performance index under the initial state x(t0).

[0180] This formula shows the minimum performance index that the system can achieve under optimal control in the initial state x(t0).

[0181] Then the state feedback gain matrix of the control system is:

[0182] Through the state feedback gain matrix K, the optimal control law u(t)=-Kx(t) can be obtained.

[0183] Therefore, the closed-loop system of the control system using the infinite-time state regulator is asymptotically stable, that is, the closed-loop system matrix The eigenvalue of has a negative real part, regardless of the eigenvalue of the original controlled system A.

[0184] Embodiment 2

[0185] Figure 2 1 is a functional block diagram of a control system of an automated guided vehicle according to an embodiment of the present invention. Figure 2 As shown, it includes: a controller, a sensor system and an actuator;

[0186] The controller is specifically used for:

[0187] Determine the system state matrix A and control input matrix B of the path tracking control system of the automated guided vehicle AGV;

[0188] Constructing a quadratic performance index function of an automated guided vehicle AGV, and determining a state weighting matrix Q1(t) and a control weighting matrix Q2(t) according to the quadratic performance index function;

[0189] Solving the Riccati equation according to the system state matrix A, the control input matrix B, the state weight matrix Q1(t) and the control weight matrix Q2(t) to obtain a symmetric positive definite matrix P;

[0190] Calculating a state feedback gain matrix K according to the symmetric positive definite matrix P, the control weighting matrix Q2(t) and the control input matrix B, which is used to define the optimal control input;

[0191] Determining an optimal control law according to the state feedback gain matrix K;

[0192] Implementing path tracking control of the AGV according to the optimal control law;

[0193] The sensor system is used to monitor and collect the status information of the automatic guided vehicle AGV in real time, including the position, speed and direction, and feed the status information back to the controller so as to update the state vector x(t) to achieve real-time control;

[0194] The actuator is used to receive the optimal control law generated by the controller and convert the optimal control law into actual operation instructions to drive the automatic guided vehicle AGV to move along a predetermined trajectory to achieve the trajectory tracking control.

[0195] In some embodiments, the sensor system specifically includes the following components:

[0196] Position sensors are used to detect the position information of the AGV in the operating space in real time. Position sensors include laser radar (LiDAR), GPS positioning module and visual positioning sensor. LiDAR can generate two-dimensional or three-dimensional maps around the AGV to achieve high-precision environmental perception and positioning, which is suitable for indoor and outdoor environments. The GPS positioning module is suitable for outdoor environments and provides global location information (latitude and longitude) for the AGV, which facilitates accurate positioning in large-scale scenes. The visual positioning sensor determines the relative position of the AGV in the venue through cameras and image processing algorithms, especially in clearly marked environments, and can provide reliable relative positioning information. Visual positioning combined with deep learning can further improve positioning accuracy and make the AGV's position perception more accurate.

[0197] Speed ​​sensor, which is used to detect the driving speed of AGV during path tracking. Speed ​​sensors include wheel speed sensor and accelerometer. The wheel speed sensor is installed on the driving wheel of AGV, and detects the rotation speed of the wheel through Hall sensor or photoelectric encoder to indirectly obtain the speed of AGV. It is a stable speed measurement method. Accelerometer is used to directly measure acceleration and obtain speed information through integration. It is suitable for accurately obtaining speed changes in a short period of time and can help the control system to perform speed smoothing. The combination of the two sensors can enhance the flexibility and accuracy of AGV speed measurement.

[0198] Direction sensors are used to detect the direction information of the AGV to ensure the heading control in path tracking. Direction sensors include gyroscopes and electronic compasses. The gyroscope provides rotation angle change information by measuring the angular velocity of the AGV, which is suitable for high-frequency angle adjustment and makes the directional control of the AGV more sensitive. The electronic compass can directly measure the absolute heading angle of the AGV, provide an accurate direction reference, and help the controller to correct the direction. The combination of the two can improve the system's responsiveness to rapid direction changes while maintaining directional accuracy.

[0199] Ultrasonic sensors are used to detect the distance of obstacles around the AGV to ensure that there is no collision during path tracking. Ultrasonic sensors determine the distance between objects and the AGV by emitting and receiving ultrasonic signals. They are generally installed in front, behind, left and right sides of the AGV to achieve all-round obstacle avoidance detection. Ultrasonic sensors are suitable for short-range obstacle perception and can identify objects close to the vehicle body, providing safety for path tracking.

[0200] Infrared sensors are used for close-range obstacle detection and boundary recognition. Infrared sensors can detect approaching objects and are suitable for assisting obstacle avoidance within a short range. Infrared sensors can sense subtle changes in obstacles in front of the AGV, or detect at the edge of the path to ensure the accuracy of path tracking, especially in tasks such as precise adjustment and parking, to provide supplementary information to prevent accidental entry into dangerous areas.

[0201] Depth camera, which is used to obtain the depth information of the scene in front of the AGV and generate a three-dimensional environmental image. The depth camera can identify the terrain features of the AGV's path, such as road boundaries, slopes or steps, which helps to achieve safe driving in complex terrain. The depth camera can provide highly accurate environmental information in path tracking control, helping the control system to adjust the path in real time and avoid trajectory deviations caused by terrain changes.

[0202] Inertial measurement unit (IMU), which contains accelerometers and gyroscopes, provides acceleration and angular velocity information of AGV. IMU can detect dynamic information such as vibration and tilt of AGV, providing reference for smooth control of path tracking. IMU data helps to compensate for short-term disturbances in the environment, so that the control system remains stable during path tracking and improves the robustness of trajectory following.

[0203] Boundary detection sensors are used to identify the boundaries of the path or the markings on the track to ensure that the AGV travels within the path. Boundary detection sensors can use magnetic strip detection or color sensors. In scenarios with clear track markings on the ground, they can effectively identify the path and prevent deviation. Such sensors help improve the accuracy of path tracking control, especially in the case of marked paths in specific venues, and can achieve accurate boundary recognition.

[0204] Each sensor component is connected to the input interface of the controller via a cable, and the controller fuses the data from each sensor to form a complete state vector x(t). Sensors such as LiDAR, depth cameras, and IMU can also be equipped with embedded data processing modules to convert the signals collected by the sensors into digital signals that can be processed by the controller and transmit them to the controller through a high-speed communication interface. The power supply for each sensor is uniformly provided by the AGV's power module, and independent regulated power supplies are provided for complex sensors such as LiDAR and depth cameras when necessary. The controller processes all sensor data in real time to generate optimal control inputs, thereby achieving stable and accurate path tracking control.

[0205] In some embodiments, the actuator is a key component in the automatic guided vehicle (AGV) control system, responsible for converting the abstract control signals generated by the controller into actual physical actions. It is an important bridge between the control algorithm and the actual movement of the AGV, directly affecting the movement performance and control accuracy of the AGV. The actuator usually includes multiple subsystems, such as the drive system, steering system, and braking system, which work together to ensure that the AGV can accurately execute control instructions.

[0206] The drive system is the core component of the actuator and is mainly responsible for providing the power output of the AGV. It consists of an electric motor (such as a DC motor, an AC servo motor or a stepper motor), a motor driver and a transmission mechanism (such as a gearbox, a transmission shaft). The drive system receives speed or torque instructions from the controller and realizes the forward, backward and speed adjustment of the AGV by precisely controlling the speed and torque of the motor. A high-performance drive system can provide smooth acceleration and deceleration, reduce vibration and shock, and improve the operating stability of the AGV and the safety of cargo transportation.

[0207] The steering system is an important part of the actuator responsible for controlling the direction of movement of the AGV. Depending on the specific structure of the AGV, the steering system can adopt differential steering, Ackerman steering or omni-directional steering. The steering system receives the steering angle or angular velocity command issued by the controller, and adjusts the steering angle of the tire or the relative speed of the wheel set by controlling the steering motor or hydraulic actuator, thereby changing the direction of movement of the AGV. Accurate steering control is essential for the path tracking of the AGV, especially when performing delicate operations in complex environments.

[0208] The braking system is a key part of the actuator to ensure the safe operation of the AGV. It is responsible for decelerating and stopping the AGV according to the instructions of the controller. The braking system usually includes electronic brakes, mechanical brakes or regenerative braking systems. In normal operation, the braking system can assist the drive system to achieve precise speed control; in emergency situations, it can respond quickly to ensure the safe stop of the AGV. Advanced braking systems can also achieve energy recovery and improve the energy efficiency of AGV.

[0209] The actuator also includes various sensors and feedback devices, such as encoders, current sensors, and position sensors. These devices monitor the working status of the actuator in real time and feed back the actual execution status to the controller. Through this closed-loop feedback mechanism, the control system can adjust the control strategy in time, compensate for errors that may occur during the execution process, and improve the control accuracy and robustness of the AGV.

[0210] In some embodiments, the control system further comprises:

[0211] Path planning module, used to generate the desired path for the AGV;

[0212] The error calculation module is used to calculate the deviation between the current state of the AGV and the expected path and adjust the control signal;

[0213] The controller is further used to adjust the control signal according to the deviation to make the AGV move along the desired path.

[0214] Specifically, the main function of the path planning module is to generate an optimal or feasible desired path for the AGV. This module takes into account a variety of factors, such as the starting and ending points, environmental maps, obstacle locations, AGV motion constraints, etc. The path planning algorithm can use the A* algorithm, RRT (rapid random tree) algorithm, or sampling-based planning methods. The generated desired path is usually composed of a series of path points or reference trajectories, which provides a clear motion target for the AGV and ensures that the AGV can complete the navigation task safely and efficiently.

[0215] The error calculation module continuously monitors the current state of the AGV (position, direction, speed, etc.) and compares it with the desired path generated by the path planning module. By calculating the deviation between the actual state of the AGV and the desired state, such as lateral deviation, heading angle deviation, etc., the error calculation module can accurately quantify the tracking performance of the AGV. This deviation information is the basis for subsequent control signal adjustments, enabling the control system to respond to changes in the motion state of the AGV in a timely manner.

[0216] After receiving the deviation information provided by the error calculation module, the controller will adjust the control signal according to the preset control algorithm (model predictive control, adaptive control, etc.). The adjusted control signal directly acts on the actuators of the AGV, such as the drive motor, steering device, etc., to change the motion state of the AGV. The goal of the controller is to minimize the deviation between the AGV and the desired path and ensure that the AGV can move accurately along the planned path. Through this closed-loop feedback mechanism, the control system can achieve high-precision path tracking of the AGV while being robust to external interference and system uncertainty.

[0217] In addition, an embodiment of the present invention further provides an automatic guided transport vehicle, which includes the control system as described above.

[0218] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0219] Embodiment 3

[0220] Figure 3 is a functional block diagram of a computer-readable storage medium according to an embodiment of the present invention. Figure 3 As shown, the embodiment of the present invention further provides a computer-readable storage medium 300, in which a computer program 310 is stored. When the computer program 310 is executed by a processor, the above method is implemented.

[0221] Based on such understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc. Of course, there are other ways of readable storage media, such as quantum memory, graphene memory, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0222] Embodiment 4

[0223] Figure 4 is a functional block diagram of an electronic device according to an embodiment of the present invention. The present invention also provides an electronic device, please refer to Figure 4 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. The memory may include a memory, such as a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage. Of course, the electronic device may also include hardware required for other services.

[0224] The processor, network interface and memory can be connected to each other through an internal bus, which can be an industrial standard architecture ISA bus, a peripheral component interconnect standard PCI bus or an extended industrial standard architecture EISA bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0225] The memory is used to store programs. Specifically, the program may include program codes, and the program codes include computer operation instructions. The memory may include internal memory and non-volatile memory, and provide instructions and data to the processor. The processor reads the corresponding computer program from the non-volatile memory into the internal memory and then runs it, forming an automated disaster recovery system based on centralized configuration at the logical level. The processor executes the program stored in the memory.

[0226] The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in conjunction with the embodiments of the present invention can be directly embodied as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in a decoding processor. The software module may be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0227] Of course, in addition to software implementation, the electronic device of the present invention does not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc., that is to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices. The system, device, module or unit described in the above embodiment can be implemented by a computer chip or entity, or by a product with a certain function. A typical implementation device is a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, a vehicle-mounted human-computer interaction device, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0228] Although the present invention provides method operation steps as described in the embodiments or flow charts, more or fewer operation steps may be included based on conventional or non-creative means. The order of steps listed in the embodiments is only one way of executing the order of many steps and does not represent the only execution order. When the device or terminal product in practice is executed, it can be executed in sequence or in parallel according to the method shown in the embodiments or the drawings (for example, in a parallel processor or multi-threaded processing environment, or even in a distributed data processing environment).

[0229] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices, and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the process in the flowchart. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0230] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0231] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0232] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0233] Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, electronic device, and readable storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0234] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims

1. A path tracking control method for an automatic guided vehicle AGV, characterized in that: The following steps are involved: S10: Determine the system state matrix A and the control input matrix B of the path tracking control system of the automatic guided vehicle AGV; S20: constructing a quadratic performance index function of the automatic guided vehicle AGV, and determining a state weighted matrix Q1(t) and a control weighted matrix Q2(t) according to the quadratic performance index function; S3 0: Solve the Riccati equation according to the system state matrix A, the control input matrix B, the state weight matrix Q1(t) and the control weight matrix Q2(t) to obtain a symmetric positive definite matrix P; S40: Calculate a state feedback gain matrix K according to the symmetric positive definite matrix P, the control weight matrix Q2(t) and the control input matrix B, which is used to define the optimal control input; S50: Determine the optimal control law according to the state feedback gain matrix K; S60: Implementing path tracking control of the AGV according to the optimal control law.

2. The method according to claim 1, characterized in that The quadratic performance index function is: The quadratic performance index function consists of the following two parts: Among them, J1 represents the sum of squares of errors generated in the process of state deviation from equilibrium state, and J2 represents the energy consumption of control input in the control process; x(t) is the state vector of the path tracking control system of the automated guided vehicle AGV; u(t) is the control input of the path tracking control system of the automated guided vehicle AGV, representing the control variable of the AGV at time t; Q1(t) is the state weight matrix, which is an n×n dimensional semi-positive definite matrix; Q2(t) is the control weight matrix, which is an r×r dimensional positive definite matrix.

3. The method according to claim 1, characterized in that Step S30 specifically includes: S31: Construct the Riccati equation, which is expressed as: Where A is the system state matrix; B is the control input matrix; Q1(t) is the state weight matrix; Q2(t) is the control weight matrix; P is the symmetric positive definite matrix to be solved; S32: Solve the Riccati equation to obtain a symmetric positive definite matrix P.

4. The method according to claim 1, characterized in that Step S40 specifically includes: The state feedback gain matrix of the path tracking control system of the automatic guided vehicle AGV is determined according to the following formula: K = Q2 -1 B T P.

5. The method according to claim 1, characterized in that Step S50 specifically includes: According to the state feedback gain matrix K, the optimal control law is obtained as follows: u(t)=-Kx(t).

6. The method according to claim 1, characterized in that The method further comprises step S70: The stability of the path tracking control system is verified by the characteristic value of the closed-loop system matrix A-BK; If the eigenvalues ​​of the closed-loop system matrix have negative real parts, then the path-following control system is asymptotically stable.

7. A control system for an automated guided vehicle for implementing the method according to any one of claims 1 to 6, characterized in that: include: controllers, sensor systems, and actuators; The controller is specifically used for: Determine the system state matrix A and control input matrix B of the path tracking control system of the automated guided vehicle AGV; Constructing a quadratic performance index function of an automated guided vehicle AGV, and determining a state weighting matrix Q1(t) and a control weighting matrix Q2(t) according to the quadratic performance index function; Solving the Riccati equation according to the system state matrix A, the control input matrix B, the state weight matrix Q1(t) and the control weight matrix Q2(t) to obtain a symmetric positive definite matrix P; Calculating a state feedback gain matrix K according to the symmetric positive definite matrix P, the control weighting matrix Q2(t) and the control input matrix B, which is used to define the optimal control input; Determining an optimal control law according to the state feedback gain matrix K; Implementing path tracking control of the AGV according to the optimal control law; The sensor system is used to monitor and collect the status information of the automatic guided vehicle AGV in real time, including the position, speed and direction, and feed the status information back to the controller so as to update the state vector x(t) to achieve real-time control; The actuator is used to receive the optimal control law generated by the controller and convert the optimal control law into actual operation instructions to drive the automatic guided vehicle AGV to move along a predetermined trajectory to achieve the trajectory tracking control.

8. The control system according to claim 7, characterized in that: Also includes: Path planning module, used to generate the desired path for the AGV; The error calculation module is used to calculate the deviation between the current state of the AGV and the expected path and adjust the control signal; The controller is further used to adjust the control signal according to the deviation to make the AGV move along the desired path.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

10. An automated guided vehicle, characterized in that: Comprising a control system as claimed in claim 7 or 8.

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