Vehicle speed robust control method based on multi-vertex LPV-H infinity control

Abstract

The invention discloses a vehicle speed robust control method based on multi-vertex LPV-H infinity control, and relates to the technical field of vehicle dynamics control and intelligent commercial vehicle automatic driving. The method comprises the following steps: firstly, constructing a linear parameter variable longitudinal dynamical model taking vehicle speed or load mass as a scheduling parameter; solving a corresponding feedback control gain at each working condition point by adopting an H-infinity robust control method, and performing convex combination on multi-vertex gains according to real-time scheduling parameters to obtain a feedback control law changing along with the running state of the vehicle; constructing a feed-forward compensation item by using the reference speed information and the measurable disturbance, and generating a vehicle longitudinal control instruction together with the feedback quantity; finally, the control instruction acts on the actuator, and closed-loop adjustment of the longitudinal speed of the vehicle is achieved. According to the method, high-precision and high-robustness control over the longitudinal speed of the vehicle can be achieved under the conditions of large load mass change, wide speed interval and complex road disturbance, and the system stability and disturbance resistance are improved.

Description

Technical Field

[0001] This invention belongs to the field of vehicle dynamics control technology, specifically relating to a robust vehicle speed control method based on multi-vertex LPV-H∞ control, used to achieve stable tracking of the target vehicle speed under conditions such as large changes in vehicle mass, and applicable to unmanned commercial vehicles using pure electric, hybrid, or other drive forms. Background Technology

[0002] Vehicle longitudinal speed control is a fundamental function in vehicle dynamics control and a prerequisite for various intelligent driving systems such as adaptive cruise control (ACC), automatic emergency braking (AEB), lane keeping assist (LKA), and autonomous driving path tracking. Among these functions, the vehicle's ability to accurately track the target speed directly impacts driving safety, following stability, ride comfort, and energy efficiency. Commercial vehicles, due to their complex application scenarios, have even higher requirements for the precision and robustness of speed control.

[0003] There are already many research results on longitudinal speed tracking control for autonomous vehicles. For example, patent CN115392008A focuses on introducing a high-precision tire model to improve speed tracking accuracy; patent CN110001654B uses traditional PID control as the core, and introduces driver type and comfort preferences through fuzzy logic to adjust control parameters, thereby improving ride comfort and control smoothness; patent CN113296552B further considers the longitudinal slip mechanical characteristics of tires in speed tracking control, and achieves longitudinal speed tracking through tire longitudinal slip characteristic identification and hierarchical control strategies.

[0004] Existing longitudinal speed tracking control methods can achieve certain control effects under specific operating conditions, but they generally rely on fixed parameters or locally effective model assumptions, making it difficult to maintain consistent control performance under significant uncertainties such as load variations, road gradient changes, and air resistance fluctuations. Meanwhile, while some robust control methods possess a certain degree of disturbance rejection capability, they are mostly based on offline design with a fixed reference speed, lacking online scheduling and adaptive capabilities for large-scale speed variations. Therefore, they struggle to meet the comprehensive requirements of autonomous commercial vehicles for control accuracy, robustness, and engineering feasibility in complex operating environments. Summary of the Invention

[0005] This invention addresses the problems of existing technologies by providing a robust vehicle speed control method based on multi-vertex LPV-H∞ control. It can solve the problems of insufficient speed tracking accuracy and decreased robustness in commercial vehicles under conditions of large load variations, wide speed ranges, and complex road disturbances.

[0006] To solve the above technical problems, the present invention provides the following technical solution: a robust vehicle speed control method based on multi-vertex LPV-H∞ control, comprising the following steps:

[0007] Step 1: Establish a vehicle longitudinal dynamics model with variable parameters: By establishing a nonlinear vehicle longitudinal dynamics differential equation, and considering the parameter uncertainties and external disturbances in the system, vehicle mass and speed are selected as key scheduling parameters. Based on this, a state-space form of a vehicle variable parameter linear longitudinal dynamics model is constructed. Then, using polyhedron theory, a parameterized polyhedron LPV model suitable for H∞ controller design is constructed.

[0008] Step 2: Design a parameter-adaptive H∞ robust controller: Construct the performance function and performance weighting function for speed tracking, and convert them into control output. Combine the vehicle variable parameter linear longitudinal dynamics model designed in Step 1 to construct a generalized system, design the constraints that the controller needs to satisfy, and transform the controller solution problem into a convex optimization problem that satisfies the linear matrix inequality constraint through variable substitution. Finally, the H∞ feedback control law of each vertex is obtained.

[0009] Step 3, Feedforward Controller Design and Total Control Quantity Composition: After obtaining the multi-vertex LPV / H∞ feedback controller in Step 2, a feedforward compensation controller is constructed using the measurable external disturbance information in the vehicle model, and superimposed with the feedback control quantity to form the final longitudinal acceleration control command.

[0010] Step 4: Use the desired longitudinal acceleration control command obtained in Step 3 as the output target quantity of the upper-level speed controller to drive the lower-level longitudinal actuator to generate the corresponding driving force or braking force, thereby realizing closed-loop control of the vehicle's longitudinal speed.

[0011] Furthermore, step 1 described above includes the following sub-steps:

[0012] Step 11: Establish the nonlinear longitudinal dynamics equations of the vehicle based on the main forces including driving force, braking force, air resistance, rolling resistance, and gradient resistance: ,

[0013] in, For the overall vehicle quality, For acceleration, For air resistance, For rolling resistance, For ramp resistance; , , , , These represent driving force, braking force, rolling resistance, air resistance, and ramp resistance, respectively.

[0014] Step 12: Select vehicle mass With vehicle speed These key scheduling parameters form the scheduling vector: ,in , ,have , ,

[0015] Define the system state variables as ,definition The external reference disturbance input w is the system. To determine the desired acceleration, the state-space form of the vehicle's parametric linear longitudinal dynamics model is defined as follows:

[0016] ,

[0017] In the formula, , , These are the system state matrix, disturbance input matrix, and control input matrix related to the scheduling parameters, respectively, C1 and D. 11 D 12 These are the system output matrix, disturbance output matrix, and control input / output matrix, respectively.

[0018] Step 13: Using polyhedron theory, construct the vehicle model. Represented as a convex combination of each vertex system, a parameterized polyhedral LPV model suitable for H∞ controller design is constructed:

[0019] ,

[0020] in, , , , To control the number of vertices, For scheduling vector The relevant convex combination coefficients satisfy and C and D represent the system's output matrix and through matrix, respectively, and A j B 1,j B 2,j These represent the system state matrix, disturbance input matrix, and control input matrix at the j-th control vertex, respectively.

[0021] Furthermore, step 2 described above includes the following sub-steps:

[0022] Step 21: Construct a generalized open-loop system ;

[0023] Step 22, the generalized system constructed in step 21 Based on this, the solution constraints for the dynamic output feedback H∞ controller of the multi-vertex LPV system are given;

[0024] Step 23: Solve the controller gain of each vertex offline and store it to form a controller gain library;

[0025] Step 24: Online gain scheduling based on scheduling parameters. During vehicle operation, the scheduling vector is constructed based on the vehicle mass and speed obtained by real-time measurement or estimation. The convex combination weight coefficient of each vertex is calculated using the polyhedral LPV theory. The controller gain matrix of each vertex obtained in step 23 is linearly interpolated based on the weight coefficient to obtain the real-time controller gain under the current scheduling parameters, thereby forming an LPV / H∞ speed robust control law that changes continuously with the scheduling parameters.

[0026] Furthermore, step 21 described above includes the following sub-steps:

[0027] Step 211: Construct the generalized system P, specifically including: defining the tracking error as follows based on the vehicle longitudinal velocity tracking task:

[0028]

[0029] in, The actual speed of the vehicle. For reference speed.

[0030] Step 212: Introduce the error weighting function Weighted function of control input They are respectively:

[0031] ,

[0032] ,

[0033] Step 213: Rewrite the above weighting function in state-space form, and combine it with the vehicle's longitudinal dynamics state. The augmented state vector is obtained by combining the states of each weighted function. , Represents the state variables of the vehicle's longitudinal dynamics system, namely the vehicle's longitudinal velocity; and These represent the error weighting functions respectively. and control input weighting function The internal state of;

[0034] Step 214: Select external input vector The performance output is The control input is The measurement output is ; This represents the error signal after processing by the error weighting function, used to constrain speed tracking performance. This represents the control input signal after processing by the control input weighting function, used to constrain the amplitude and variation characteristics of the control input. Used to represent the error-weighted channel output. Used to represent the weighted channel output of control input;

[0035] Step 215, at each LPV vertex Generalized open-loop system Written as:

[0036]

[0037] in, , , , , , , , , , This represents the input matrix of the external input W to the vehicle's longitudinal dynamics system. This represents the input matrix of the control input U to the vehicle's longitudinal dynamics system. , These represent the error weighting functions respectively. The state matrix and the input matrix, , These represent the control input weighting functions, respectively. The state matrix and input matrix;

[0038] Furthermore, step 22 described above includes the following sub-steps:

[0039] Step 221: Adopt the following state-space form for the dynamic output feedback H∞ controller:

[0040] ,

[0041] in The state vector of the controller It is the controller parameter matrix to be determined;

[0042] Step 222: Obtain the closed-loop system matrix after applying the controller to the system. The closed-loop state vector is Its state equation is:

[0043]

[0044] Wherein, the coefficient matrix , , , ; , , All at the vertex Generalized open-loop system constructed at the location The system matrix.

[0045] Step 223: Construct the following three types of constraints, and linearize them into a set of convex LMI constraints through variable substitution:

[0046] (1) H∞ performance constraint: In order to achieve robust suppression of reference velocity variation and parameter uncertainty, the disturbance input is specified. To performance output The H∞ norm satisfies: ,in, This indicates input from an external source. To performance output Closed-loop transfer matrix, Denotes the H∞ norm of the system. As the upper limit of performance;

[0047] The above performance indicators are transformed into information about the closed-loop matrix. LMI format:

[0048] ,

[0049] The above inequality applies to all vertices. This satisfies both conditions, thus ensuring that the LPV system has consistent H∞ robust performance across the entire range of scheduling parameters.

[0050] (2) Positive definiteness constraint: To ensure that the physical meaning of the Lyapunov function and the H∞ performance index is clear, a symmetric positive definite matrix is ​​introduced for each vertex. And apply positive definite constraints: ,in, Representation matrix transpose;

[0051] (3) Regional pole placement constraint: By restricting the system poles to the left half of the complex plane with a radius of The center is disk Internally, to improve the dynamic and steady-state performance of the system, the following LMI conditions are applied:

[0052] ,

[0053] This constraint applies to all vertices. Simultaneously satisfying these conditions ensures that the closed-loop poles of the multi-vertex LPV system are all located within the preset disk region D throughout the entire scheduling parameter range, thus meeting the comprehensive requirements of the vehicle's longitudinal speed on convergence speed and overshoot.

[0054] Furthermore, step 23 described above includes the following sub-steps:

[0055] Step 231: Using the standard variable replacement method with dynamic output feedback, several intermediate matrix variables are introduced to replace the original nonlinear matrix terms, and a matrix decomposition structure that satisfies the preset consistency constraints is constructed to ensure the linearization of subsequent constraint conditions.

[0056] Step 232: Introduce new matrix variables related to the controller parameters, and transform the regional pole constraints and performance index constraints into a set of linear matrix inequalities about the intermediate matrix variables. The controller design problem is then expressed as finding feasible solutions for all matrix variables under the above linear matrix inequalities.

[0057] Step 233: After obtaining the feasible solution of the matrix variables, calculate the controller gain parameter that satisfies the regional stability constraint and performance constraint according to the preset inverse relationship, and use the controller gain to construct the controller parameter library.

[0058] Furthermore, step 24 described above includes the following sub-steps:

[0059] Step 241: During vehicle operation, a scheduling vector is constructed based on the vehicle mass and speed obtained through real-time measurement or estimation. .

[0060] Step 242: Calculate the convex combination weight coefficients of each vertex using the polyhedron LPV theory. ,in

[0061] ,

[0062] Step 243: Based on this set of weights, perform linear interpolation on the vertex controller gain obtained in Step 23 to obtain the controller gain matrix for the current operating condition:

[0063] ,

[0064] in, , , , Representing vertices respectively The controller parameter matrix.

[0065] Step 244: Obtain the multi-vertex LPV / H∞ velocity robust control law that varies in real time with scheduling parameters:

[0066] ,

[0067] Finally, feedback acceleration control commands are obtained. This enables adaptive and robust tracking control of the vehicle's longitudinal speed parameters.

[0068] Furthermore, in step 3 above, based on the vehicle longitudinal dynamics equations, quantities that significantly affect vehicle speed and can be obtained through onboard sensors or estimation algorithms but are not included in the state matrix are defined as measurable disturbances, including but not limited to: gradient resistance, rolling resistance, and the rate of change of reference speed. Based on vehicle parameters, the measurable disturbances are converted into feedforward acceleration commands, as shown in the following equation:

[0069] ,

[0070] In the formula, This represents the equivalent acceleration term derived from rolling resistance. Indicates the road slope angle. Indicates reference speed;

[0071] The feedback control quantity and the feedforward control quantity are superimposed to form the final longitudinal acceleration control command, as shown in the following formula:

[0072] .

[0073] Furthermore, step 4 mentioned above specifically includes the following steps:

[0074] Step 41: The control unit periodically collects vehicle operating status information, including current vehicle speed, wheel speed, gear information, drive system and braking system status, driver pedal requests, etc., based on the desired acceleration. And vehicle status judgment driving and braking working modes,

[0075] Step 42: After determining the operating mode, set the desired acceleration. Transformed into generalized longitudinal force and will Distribute to each wheel, and receive corresponding instructions for each motor and each wheel's braking pressure;

[0076] Step 43: Based on the inverse dynamics model, convert the desired driving force and desired braking force into corresponding driving torque command and braking pressure command.

[0077] Compared with the prior art, the beneficial technical effects of the present invention using the above technical solution are as follows:

[0078] (1) In the robust vehicle speed control method based on multi-vertex LPV-H∞ control proposed in this invention, by constructing the vehicle longitudinal dynamics model as a multi-vertex linear variable parameter (LPV) model and performing unified control design across the entire scheduling parameter range, it can effectively address the uncertainty of model parameters caused by factors such as large changes in vehicle load and wide operating speed range, and ensure that the vehicle longitudinal speed control performance remains consistent across the entire operating condition range.

[0079] (2) In the robust vehicle speed control method based on multi-vertex LPV-H∞ control proposed in this invention, by adopting a control strategy that combines offline solution of multi-vertex controller with online convex combination, the online computational complexity is significantly reduced while ensuring control performance, avoiding the problem of heavy real-time computational burden of traditional nonlinear control or adaptive control methods, and has good engineering feasibility.

[0080] (3) In the robust vehicle speed control method based on multi-vertex LPV-H∞ control proposed in this invention, by introducing a feedforward compensation mechanism based on measurable disturbance, factors such as road slope, rolling resistance and reference speed change rate are converted into feedforward acceleration commands, which effectively reduces the adjustment pressure of the feedback controller and improves the dynamic response speed and tracking accuracy of the vehicle's longitudinal speed. Attached Figure Description

[0081] Figure 1 This is a schematic diagram of the robust vehicle speed control method of multi-vertex LPV-H∞ control described in this application.

[0082] Figure 2 This is a block diagram of the speed tracking control system described in this application. Detailed Implementation

[0083] To better understand the technical content of the present invention, specific embodiments are described below in conjunction with the accompanying drawings.

[0084] In this invention, various aspects of the invention are described with reference to the accompanying drawings, in which numerous illustrative embodiments are shown. Embodiments of the invention are not limited to those depicted in the drawings. It should be understood that the invention is implemented through any of the various concepts and embodiments described above, as well as the concepts and embodiments described in detail below, because the concepts and embodiments disclosed herein are not limited to any particular implementation. Furthermore, some aspects of the invention disclosed may be used alone or in any suitable combination with other aspects of the invention disclosed.

[0085] like Figure 1-2 As shown, this invention discloses a robust vehicle speed control method based on multi-vertex LPV-H∞ control, the steps of which are as follows:

[0086] Step 1: Establish a vehicle longitudinal dynamics model with variable parameters: By establishing a nonlinear vehicle longitudinal dynamics differential equation, and considering the parameter uncertainties and external disturbances in the system, vehicle mass and speed are selected as key scheduling parameters. Based on this, a state-space form of a vehicle variable parameter linear longitudinal dynamics model is constructed. Then, using polyhedron theory, a parameterized polyhedron LPV model suitable for H∞ controller design is constructed.

[0087] Step 2: Design a parameter-adaptive H∞ robust controller: Construct the performance function and performance weighting function for speed tracking, and convert them into control output. Combine the vehicle variable parameter linear longitudinal dynamics model designed in Step 1 to construct a generalized system, design the constraints that the controller needs to satisfy, and transform the controller solution problem into a convex optimization problem that satisfies the linear matrix inequality constraint through variable substitution. Finally, the H∞ feedback control law of each vertex is obtained.

[0088] Step 3, Feedforward Controller Design and Total Control Quantity Composition: After obtaining the multi-vertex LPV / H∞ feedback controller in Step 2, a feedforward compensation controller is constructed using the measurable external disturbance information in the vehicle model, and superimposed with the feedback control quantity to form the final longitudinal acceleration control command.

[0089] Step 4: Use the desired longitudinal acceleration control command obtained in Step 3 as the output target quantity of the upper-level speed controller to drive the lower-level longitudinal actuator to generate the corresponding driving force or braking force, thereby realizing closed-loop control of the vehicle's longitudinal speed.

[0090] In a preferred embodiment of the present invention, step 1 includes the following sub-steps:

[0091] Step 11: Establish the nonlinear longitudinal dynamics equations of the vehicle based on the main forces including driving force, braking force, air resistance, rolling resistance, and gradient resistance:

[0092] ,

[0093] in, For the overall vehicle quality, For acceleration, For air resistance, For rolling resistance, For ramp resistance; , , , , These represent driving force, braking force, rolling resistance, air resistance, and ramp resistance, respectively.

[0094] Step 12: Select vehicle mass With vehicle speed These key scheduling parameters form the scheduling vector: ,in , ,have , ,

[0095] Define the system state variables as ,definition The external reference disturbance input w is the system. To determine the desired acceleration, the state-space form of the vehicle's parametric linear longitudinal dynamics model is defined as follows:

[0096] ,

[0097] In the formula, , , These are the system state matrix, disturbance input matrix, and control input matrix related to the scheduling parameters, respectively, C1 and D. 11 D 12 These are the system output matrix, disturbance output matrix, and control input / output matrix, respectively.

[0098] Step 13: Using polyhedron theory, construct the vehicle model. Represented as a convex combination of each vertex system, a parameterized polyhedral LPV model suitable for H∞ controller design is constructed:

[0099] ,

[0100] in, , , , To control the number of vertices, For scheduling vector The relevant convex combination coefficients satisfy and C and D represent the system's output matrix and through matrix, respectively, and A j B 1,j B 2,j These represent the system state matrix, disturbance input matrix, and control input matrix at the j-th control vertex, respectively.

[0101] In a preferred embodiment of the present invention, step 2 includes the following sub-steps:

[0102] Step 21: Construct a generalized open-loop system This includes the following sub-steps:

[0103] Step 211: Construct the generalized system P, specifically including: defining the tracking error as follows based on the vehicle longitudinal velocity tracking task:

[0104]

[0105] in, The actual speed of the vehicle. For reference speed.

[0106] Step 212: To enhance the system's frequency domain performance constraints on speed error and control input, an error weighting function is introduced. Weighted function of control input They are respectively:

[0107] ,

[0108] ,

[0109] Step 213: Rewrite the above weighting function in state-space form, and combine it with the vehicle's longitudinal dynamics state. The augmented state vector is obtained by combining the states of each weighted function. , Represents the state variables of the vehicle's longitudinal dynamics system, namely the vehicle's longitudinal velocity; and These represent the error weighting functions respectively. and control input weighting function The internal state of;

[0110] Step 214: Select external input vector The performance output is The control input is The measurement output is ; This represents the error signal after processing by the error weighting function, used to constrain speed tracking performance. This represents the control input signal after processing by the control input weighting function, used to constrain the amplitude and variation characteristics of the control input. Used to represent the error-weighted channel output. Used to represent the weighted channel output of control input;

[0111] Step 215, at each LPV vertex Generalized open-loop system Written as:

[0112]

[0113] in, , , , , , , , , , This represents the input matrix of the external input W to the vehicle's longitudinal dynamics system. This represents the input matrix of the control input U to the vehicle's longitudinal dynamics system. , These represent the error weighting functions respectively. The state matrix and the input matrix, , These represent the control input weighting functions, respectively. The state matrix and input matrix.

[0114] Step 22, the generalized system constructed in step 21 Based on this, for a multi-vertex LPV system, the solution constraints for the dynamic output feedback H∞ controller are given; including the following sub-steps:

[0115] Step 221: Adopt the following state-space form for the dynamic output feedback H∞ controller:

[0116] ,

[0117] in The state vector of the controller It is the controller parameter matrix to be determined;

[0118] Step 222: Obtain the closed-loop system matrix after applying the controller to the system. The closed-loop state vector is Its state equation is:

[0119] ,

[0120] Wherein, the coefficient matrix , , , ; , , All at the vertex Generalized open-loop system constructed at the location The system matrix;

[0121] Step 223: Construct the following three types of constraints, and linearize them into a set of convex LMI constraints through variable substitution:

[0122] (1) H∞ performance constraint: In order to achieve robust suppression of reference velocity variation and parameter uncertainty, the disturbance input is specified. To performance output The H∞ norm satisfies: ,in, This indicates input from an external source. To performance output Closed-loop transfer matrix, Denotes the H∞ norm of the system. As an upper bound for performance; the above performance indicators are transformed into information about the closed-loop matrix. LMI format:

[0123] ,

[0124] The above inequality applies to all vertices. This satisfies both conditions, thus ensuring that the LPV system has consistent H∞ robust performance across the entire range of scheduling parameters.

[0125] (2) Positive definiteness constraint: To ensure that the physical meaning of the Lyapunov function and the H∞ performance index is clear, a stacking matrix is ​​introduced for each vertex. And apply positive definite constraints: , Representation matrix transpose;

[0126] (3) Regional pole placement constraint: By restricting the system poles to the left half of the complex plane with a radius of The center is disk Internally, to improve the dynamic and steady-state performance of the system, the following LMI conditions are applied:

[0127] ,

[0128] This constraint applies to all vertices. Simultaneously satisfying these conditions ensures that the closed-loop poles of the multi-vertex LPV system are all located within the preset disk region D throughout the entire scheduling parameter range, thus meeting the comprehensive requirements of the vehicle's longitudinal speed on convergence speed and overshoot.

[0129] Step 23: Solve the controller gain of each vertex offline and store it to form a controller gain library, including the following sub-steps:

[0130] Step 231: Using the standard variable replacement method with dynamic output feedback, several intermediate matrix variables are introduced to replace the original nonlinear matrix terms, and a matrix decomposition structure satisfying preset consistency constraints is constructed to ensure the linearization of subsequent constraint conditions. Due to the closed-loop system matrix... It contains both the controlled object matrix and the controller matrix. The aforementioned regional extreme point constraints and H∞ constraints are nonlinear matrix inequality constraints, which cannot be solved directly using convex optimization methods.

[0131] Using the standard variable substitution method with dynamic output feedback, two symmetric matrix variables X and Y are introduced, and full-rank matrices M and N are constructed to satisfy:

[0132] ,

[0133] Where M and N are derived from It is obtained through singular value decomposition.

[0134] Step 232: Introduce new matrix variables related to the controller parameters, and transform the regional pole constraints and performance index constraints into a set of linear matrix inequalities concerning the intermediate matrix variables. The controller design problem is then formulated as finding feasible solutions for all matrix variables under the aforementioned linear matrix inequalities. Specifically, this involves introducing new matrix variables related to the controller. And rewrite all region pole constraints and H∞ performance constraints as about Matrix inequalities arising from linear combinations of the controlled object matrix. In this case, the controller design problem can be equivalently transformed into: solving for the matrix variables under LMI conditions satisfying disk region pole constraints, positive definiteness constraints, and H∞ performance constraints. .

[0135] Step 233: After obtaining the feasible solution of the matrix variables, calculate the controller gain parameters that satisfy the regional stability constraints and performance constraints according to the preset inverse relationship, and use the controller gain to construct the controller parameter library. Specifically: the obtained... And matrices M and N, through the inverse transformation as follows, a dynamic output feedback H∞ controller satisfying the region pole constraints, H∞ performance constraints, and positive definiteness constraints is obtained. For all vertices By repeating the above process, a set of vertex controller gain matrices that satisfy all LMI constraints can be obtained. It is stored offline as a controller gain library.

[0136] .

[0137] Step 24: Online gain scheduling based on scheduling parameters. During vehicle operation, a scheduling vector is constructed based on the real-time measured or estimated vehicle mass and speed. The convex combination weight coefficients of each vertex are calculated using polyhedral LPV theory. Linear interpolation is then performed on the controller gain matrix of each vertex obtained in Step 23 based on these weight coefficients to obtain the real-time controller gain under the current scheduling parameters, thereby forming a robust LPV / H∞ speed control law that continuously varies with the scheduling parameters. Specifically, this includes the following sub-steps:

[0138] Step 241: During vehicle operation, a scheduling vector is constructed based on the vehicle mass and speed obtained through real-time measurement or estimation. ,

[0139] Step 242: Calculate the convex combination weight coefficients of each vertex using the polyhedron LPV theory. ,in

[0140] ,

[0141] Step 243: Based on this set of weights, perform linear interpolation on the vertex controller gain obtained in Step 23 to obtain the controller gain matrix for the current operating condition:

[0142] ,

[0143] in, , , , Representing vertices respectively The controller parameter matrix.

[0144] Step 244: Obtain the multi-vertex LPV / H∞ velocity robust control law that varies in real time with scheduling parameters:

[0145] ,

[0146] Finally, feedback acceleration control commands are obtained. This enables adaptive and robust tracking control of the vehicle's longitudinal speed parameters.

[0147] In a preferred embodiment of the present invention, in step 3, based on the vehicle longitudinal dynamics equation, quantities that significantly affect vehicle speed and can be obtained through onboard sensors or estimation algorithms but do not enter the state matrix are defined as measurable disturbances, including but not limited to: gradient resistance, rolling resistance, and the rate of change of reference speed. Based on vehicle parameters, the measurable disturbances are converted into feedforward acceleration commands, as shown in the following formula:

[0148] ,

[0149] In the formula, This represents the equivalent acceleration term derived from rolling resistance. Indicates the road slope angle. The reference velocity is represented by the sum of the feedback control quantity and the feedforward control quantity to form the final longitudinal acceleration control command, as shown in the following formula:

[0150] .

[0151] In a preferred embodiment of the present invention, step 4 specifically includes the following steps:

[0152] Step 41: The control unit periodically collects vehicle operating status information, including current vehicle speed, wheel speed, gear information, drive system and braking system status, driver pedal requests, etc., and determines the desired acceleration. And the vehicle status judgment driving and braking working modes, specifically:

[0153] Based on the kinematic formulas for commercial vehicles, the critical deceleration of commercial vehicles at different longitudinal speeds during coasting in neutral is derived:

[0154]

[0155] in This represents the vehicle's current longitudinal speed.

[0156] Step 42: After determining the working mode, set the desired acceleration. Transformed into generalized longitudinal force Based on vehicle mass, drive force distribution strategy, and braking force distribution strategy, The commands are distributed to each wheel, and corresponding instructions are received from each motor and each wheel regarding braking pressure.

[0157] In drive mode, based on the estimated vehicle mass and drive force distribution strategy, The desired driving force is distributed to the motors to obtain the driving force for each wheel. In braking mode, The braking force is decomposed into the desired braking force for each wheel brake cylinder according to the braking force distribution strategy. The instruction allocation process needs to simultaneously consider the actuator's saturation constraints, rate of change state, and tire-road adhesion conditions.

[0158] Step 43: Based on the inverse dynamics model, convert the desired driving force and desired braking force into corresponding driving torque command and braking pressure command.

[0159] Inverse model of drive motor:

[0160] ,

[0161] in The output torque of the drive motor for the kth wheel, For the tire radius, The total transmission ratio of the transmission system. For transmission efficiency.

[0162] Inverse model of brake:

[0163] ,

[0164] in Let K be the braking pressure of the k-th wheel. Let be the braking force coefficient of the k-th wheel.

[0165] While the present invention has been described above with reference to preferred embodiments, it is not intended to limit the invention. Those skilled in the art can make various modifications and refinements without departing from the spirit and scope of the invention. Therefore, the scope of protection of the present invention shall be determined by the claims.

Claims

1. A robust vehicle speed control method based on multi-vertex LPV-H∞ control, characterized in that, The steps are as follows: Step 1: Establish a vehicle longitudinal dynamics model with variable parameters: By establishing a nonlinear vehicle longitudinal dynamics differential equation, and considering the parameter uncertainties and external disturbances in the system, vehicle mass and speed are selected as key scheduling parameters. Based on this, a state-space form of a vehicle variable parameter linear longitudinal dynamics model is constructed. Then, using polyhedron theory, a parameterized polyhedron LPV model suitable for H∞ controller design is constructed. Step 2: Design a parameter-adaptive H∞ robust controller: Construct the performance function and performance weighting function for speed tracking, and convert them into control output. Combine the vehicle variable parameter linear longitudinal dynamics model designed in Step 1 to construct a generalized system, design the constraints that the controller needs to satisfy, and transform the controller solution problem into a convex optimization problem that satisfies the linear matrix inequality constraint through variable substitution. Finally, the H∞ feedback control law of each vertex is obtained. Step 3, Feedforward Controller Design and Total Control Quantity Composition: After obtaining the multi-vertex LPV / H∞ feedback controller in Step 2, a feedforward compensation controller is constructed using the measurable external disturbance information in the vehicle model, and superimposed with the feedback control quantity to form the final longitudinal acceleration control command. Step 4: Use the desired longitudinal acceleration control command obtained in Step 3 as the output target quantity of the upper-level speed controller to drive the lower-level longitudinal actuator to generate the corresponding driving force or braking force, thereby realizing closed-loop control of the vehicle's longitudinal speed.

2. The robust vehicle speed control method based on multi-vertex LPV-H∞ control according to claim 1, characterized in that, Step 1 includes the following sub-steps: Step 11: Establish the nonlinear longitudinal dynamics equations of the vehicle based on the main forces including driving force, braking force, air resistance, rolling resistance, and gradient resistance: , in, For the overall vehicle quality, For acceleration, For air resistance, For rolling resistance, For ramp resistance; , , , , These represent driving force, braking force, rolling resistance, air resistance, and ramp resistance, respectively. Step 12: Select vehicle mass With vehicle speed These key scheduling parameters form the scheduling vector: ,in , ,have , , Define the system state variables as ,definition The external reference disturbance input w is the system. To determine the desired acceleration, the state-space form of the vehicle's parametric linear longitudinal dynamics model is defined as follows: ， In the formula, , , These are the system state matrix, disturbance input matrix, and control input matrix related to the scheduling parameters, respectively, C1 and D. 11 D 12 These are the system output matrix, disturbance output matrix, and control input / output matrix, respectively. Step 13: Using polyhedron theory, construct the vehicle model. Represented as a convex combination of each vertex system, a parameterized polyhedral LPV model suitable for H∞ controller design is constructed: ， in, , , , To control the number of vertices, For scheduling vector The relevant convex combination coefficients satisfy and C and D represent the system's output matrix and through matrix, respectively, and A j B 1,j B 2,j These represent the system state matrix, disturbance input matrix, and control input matrix at the j-th control vertex, respectively.

3. The robust vehicle speed control method based on multi-vertex LPV-H∞ control according to claim 2, characterized in that, Step 2 includes the following sub-steps: Step 21: Construct a generalized open-loop system ; Step 22, the generalized system constructed in step 21 Based on this, the solution constraints for the dynamic output feedback H∞ controller of the multi-vertex LPV system are given; Step 23: Solve the controller gain of each vertex offline and store it to form a controller gain library; Step 24: Online gain scheduling based on scheduling parameters. During vehicle operation, the scheduling vector is constructed based on the vehicle mass and speed obtained by real-time measurement or estimation. The convex combination weight coefficient of each vertex is calculated using the polyhedral LPV theory. The controller gain matrix of each vertex obtained in step 23 is linearly interpolated based on the weight coefficient to obtain the real-time controller gain under the current scheduling parameters, thereby forming an LPV / H∞ speed robust control law that changes continuously with the scheduling parameters.

4. The robust vehicle speed control method based on multi-vertex LPV-H∞ control according to claim 3, characterized in that, Step 21 includes the following sub-steps: Step 211: Construct the generalized system P, specifically including: defining the tracking error as follows based on the vehicle longitudinal velocity tracking task: ， in, The actual speed of the vehicle. For reference speed; Step 212: Introduce the error weighting function Weighted function of control input They are respectively: ， ， Step 213: Rewrite the above weighting function in state-space form, and combine it with the vehicle's longitudinal dynamics state. The augmented state vector is obtained by combining the states of each weighted function. , Represents the state variables of the vehicle's longitudinal dynamics system, namely the vehicle's longitudinal velocity; and These represent the error weighting functions respectively. and control input weighting function The internal state of; Step 214: Select external input vector The performance output is The control input is The measurement output is ; This represents the error signal after processing by the error weighting function, used to constrain speed tracking performance. This represents the control input signal after processing by the control input weighting function, used to constrain the amplitude and variation characteristics of the control input. Used to represent the error-weighted channel output. Used to represent the weighted channel output of control input; Step 215, at each LPV vertex Generalized open-loop system Written as: ， in, , , , , , , , , , This represents the input matrix of the external input W to the vehicle's longitudinal dynamics system. This represents the input matrix of the control input U to the vehicle's longitudinal dynamics system. , These represent the error weighting functions respectively. The state matrix and the input matrix, , These represent the control input weighting functions, respectively. The state matrix and input matrix.

5. The robust vehicle speed control method based on multi-vertex LPV-H∞ control according to claim 4, characterized in that, Step 22 includes the following sub-steps: Step 221: Adopt the following state-space form for the dynamic output feedback H∞ controller: ， in The state vector of the controller It is the controller parameter matrix to be determined; Step 222: Obtain the closed-loop system matrix after applying the controller to the system. The closed-loop state vector is Its state equation is: ， Wherein, the coefficient matrix , , , ; , , All at the vertex Generalized open-loop system constructed at the location The system matrix; Step 223: Construct the following three types of constraints, and linearize them into a set of convex LMI constraints through variable substitution: (1) H∞ performance constraint: In order to achieve robust suppression of reference velocity variation and parameter uncertainty, the disturbance input is specified. To performance output The H∞ norm satisfies: ,in, This indicates input from an external source. To performance output Closed-loop transfer matrix, Denotes the H∞ norm of the system. As the upper limit of performance; The above performance indicators are transformed into information about the closed-loop matrix. LMI format: ， The above inequality applies to all vertices. This ensures that the LPV system has consistent H∞ robust performance across the entire range of scheduling parameters. (2) Positive definiteness constraint: To ensure that the physical meaning of the Lyapunov function and the H∞ performance index is clear, a symmetric positive definite matrix is ​​introduced for each vertex. And apply positive definite constraints: ,in, Representation matrix transpose; (3) Regional pole placement constraint: By restricting the system poles to the left half of the complex plane with a radius of The center is disk Internally, to improve the dynamic and steady-state performance of the system, the following LMI conditions are applied: ， This constraint applies to all vertices. Simultaneously satisfying these conditions ensures that the closed-loop poles of the multi-vertex LPV system are all located within the preset disk region D throughout the entire scheduling parameter range, thus meeting the comprehensive requirements of the vehicle's longitudinal speed on convergence speed and overshoot.

6. The robust vehicle speed control method based on multi-vertex LPV-H∞ control according to claim 4, characterized in that, Step 23 includes the following sub-steps: Step 231: Using the standard variable replacement method with dynamic output feedback, several intermediate matrix variables are introduced to replace the original nonlinear matrix terms, and a matrix decomposition structure that satisfies the preset consistency constraints is constructed to ensure the linearization of subsequent constraint conditions. Step 232: Introduce new matrix variables related to the controller parameters, and transform the regional pole constraints and performance index constraints into a set of linear matrix inequalities about the intermediate matrix variables. The controller design problem is then expressed as finding feasible solutions for all matrix variables under the above linear matrix inequalities. Step 233: After obtaining the feasible solution of the matrix variables, calculate the controller gain parameter that satisfies the regional stability constraint and performance constraint according to the preset inverse relationship, and use the controller gain to construct the controller parameter library.

7. The robust vehicle speed control method based on multi-vertex LPV-H∞ control according to claim 4, characterized in that, Step 24 includes the following sub-steps: Step 241: During vehicle operation, a scheduling vector is constructed based on the vehicle mass and speed obtained through real-time measurement or estimation. ; Step 242: Calculate the convex combination weight coefficients of each vertex using the polyhedron LPV theory. ,in ， Step 243: Based on this set of weights, perform linear interpolation on the vertex controller gain obtained in Step 23 to obtain the controller gain matrix for the current operating condition: ， in, , , , Representing vertices respectively The controller parameter matrix; Step 244: Obtain the multi-vertex LPV / H∞ velocity robust control law that varies in real time with scheduling parameters: ， Finally, feedback acceleration control commands are obtained. This enables adaptive and robust tracking control of the vehicle's longitudinal speed parameters.

8. The robust vehicle speed control method based on multi-vertex LPV-H∞ control according to claim 7, characterized in that, In step 3, based on the vehicle's longitudinal dynamics equations, quantities that significantly affect vehicle speed and can be obtained through onboard sensors or estimation algorithms but are not included in the state matrix are defined as measurable disturbances, including but not limited to: gradient resistance, rolling resistance, and the rate of change of reference speed. Based on vehicle parameters, these measurable disturbances are converted into feedforward acceleration commands, as shown in the following equation: ， In the formula, This represents the equivalent acceleration term derived from rolling resistance. Indicates the road slope angle. Indicates reference speed; The feedback control quantity and the feedforward control quantity are superimposed to form the final longitudinal acceleration control command, as shown in the following formula: 。 9. The robust vehicle speed control method based on multi-vertex LPV-H∞ control according to claim 7, characterized in that, Step 4 specifically includes the following steps: Step 41: The control unit periodically collects vehicle operating status information, including current vehicle speed, wheel speed, gear information, drive system and braking system status, driver pedal requests, etc.; based on the desired acceleration... And vehicle status judgment driving and braking working modes, Step 42: After determining the working mode, set the desired acceleration. Transformed into generalized longitudinal force and will Distribute to each wheel, and receive corresponding instructions for each motor and each wheel's braking pressure; Step 43: Based on the inverse dynamics model, convert the desired driving force and desired braking force into corresponding driving torque command and braking pressure command.

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