Vehicle suspension system multi-rate event triggering fault-tolerant control method and device and storage medium

Abstract

The invention discloses a vehicle suspension system multi-rate event triggering fault-tolerant control method and device and a storage medium, and the method comprises the steps: constructing a vehicle suspension system dynamic model through combining with the Newton second law; obtaining a vehicle active suspension system based on the vehicle suspension system dynamics model and the physical constraint condition; based on a vehicle active suspension system, a Markov process-based random actuator fault model is constructed, and then a fault-tolerant controller related to a fault mode is designed; based on the vehicle active suspension system, designing a multi-rate event triggering scheduling protocol to obtain a closed-loop vehicle active suspension system; based on the Lyapunov theory, a sufficiency condition is established to ensure the random stability and H infinity performance index of the closed-loop vehicle active suspension system; and calculating a controller gain to achieve multi-rate sampling event-triggered fault-tolerant control of the vehicle active suspension system. According to the invention, network resource consumption is reduced, and adaptability and fault-tolerant capability of the vehicle suspension system under complex working conditions are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of modeling and control of vehicle suspension systems, and particularly to a multi-rate event-triggered fault-tolerant control method, device, terminal device, and computer-readable storage medium for vehicle suspension systems. Background Art

[0002] In recent years, with the rapid development of the automotive industry and system control theory, the research on vehicle suspension systems has attracted much attention. At present, automotive suspension systems are mainly divided into two categories: passive and active. The active suspension system actively cancels vibrations by introducing actuators and shows significant advantages compared with passive systems, thus attracting much attention.

[0003] In practical applications, a single sensor is difficult to meet the control requirements under complex working conditions due to limitations such as insufficient information collection and hardware constraints. To overcome these limitations, multi-sensor network technology has been widely used. By deploying multiple sensors to obtain system state information, the environmental adaptability and control reliability of the system have been significantly improved. However, due to the differences in the dynamic characteristics of different state variables, each sensor often needs to use different sampling frequencies for data collection, which has given rise to in-depth research on multi-rate sampling control theory. Multi-rate sampling technology effectively reduces system resource consumption while ensuring control performance by optimizing the sampling periods of each sensor.

[0004] With the continuous improvement of vehicle intelligence and networking, the optimal utilization of communication resources has become a key factor restricting the performance of control systems. In this context, a sampling-based event-triggered control protocol has emerged. Its core idea is to design reasonable triggering conditions and perform data transmission and controller update only when the system state meets specific conditions, thereby significantly reducing the communication load. However, most existing studies are limited to single-rate sampling scenarios and have not fully explored the co-optimization mechanism of multi-rate sampling and event-triggered control. Especially in multi-sensor networks, how to design an efficient event-triggered strategy to adapt to the sampling characteristics of different sensors while ensuring the control performance and stability of the system remains a challenging technical problem.

[0005] In addition, the vehicle suspension system also faces the risk of actuator failures during actual operation, and these failures may lead to a decline in system performance or even instability, seriously threatening driving safety.

[0006] To ensure the stability and dynamic performance of the system, it is crucial to design a fault-tolerant control strategy. Based on the above analysis, in-depth research on a multi-rate sampling event-triggered fault-tolerant control method for actuator failures has important theoretical significance and engineering practice value. Summary of the Invention

[0007] To solve the deficiencies of the above-mentioned existing technologies, the present invention provides a multi-rate event-triggered fault-tolerant control method, device, terminal device, and computer-readable storage medium for a vehicle suspension system, which improves the adaptability and fault-tolerant ability of the vehicle suspension system under complex working conditions (actuator faults and multi-sensor networks) while reducing network resource consumption.

[0008] The first object of the present invention is to provide a multi-rate event-triggered fault-tolerant control method for a vehicle suspension system.

[0009] The second object of the present invention is to provide a multi-rate event-triggered fault-tolerant control device for a vehicle suspension system.

[0010] The third object of the present invention is to provide a terminal device.

[0011] The fourth object of the present invention is to provide a computer-readable storage medium.

[0012] The first object of the present invention can be achieved by adopting the following technical solutions:

[0013] A multi-rate event-triggered fault-tolerant control method for a vehicle suspension system, the method comprising:

[0014] Combined with Newton's second law, a dynamic model of the vehicle suspension system is constructed; based on the dynamic model of the vehicle suspension system and physical constraint conditions, an active vehicle suspension system is obtained;

[0015] Based on the active vehicle suspension system, a stochastic actuator fault model based on a Markov process is constructed to solve the problem of stochastic actuator faults; based on the stochastic actuator fault model, a fault-tolerant controller related to the fault mode is designed;

[0016] Based on the active vehicle suspension system, a multi-rate event-triggered scheduling protocol is designed to solve the problems of sampling rate differences and limited communication resources existing in the multi-sensor network;

[0017] Based on the stochastic actuator fault model and the multi-rate event-triggered scheduling protocol, a closed-loop active vehicle suspension system is obtained; based on Lyapunov theory, a sufficiency condition is established to ensure the stochastic stability and H ∞ performance index of the closed-loop active vehicle suspension system; and the controller gain is calculated to achieve the multi-rate sampling event-triggered fault-tolerant control of the active vehicle suspension system.

[0018] Further, the designing of the multi-rate event-triggered scheduling protocol based on the active vehicle suspension system includes:

[0019] Assume that there are p sensors, denoted as {S1, S2,..., S p}; the sampling period of the i-th sensor is h i, the measured state component information is where

[0020] Assume that the measured state components of each sensor do not overlap. The state components {x1, x2, x3, x4} are divided into p groups, which are respectively associated with p sensors, specifically:

[0021]

[0022] where is the sparse representation of the measured state component of the i-th sensor, only storing the corresponding measured state component, and other elements are all zero;

[0023] By introducing the state extraction matrix E i associate the sparse vector χ i (t) with the system state x(t), which is specifically expressed as follows:

[0024] χ i (t) = E i x(t)

[0025] where is a diagonal matrix, and the positions where the diagonal is 1 correspond to the measured state components of the i-th sensor, satisfying When i ≠ j, E i × E j = 0;

[0026] For the i-th sensor channel, the transmission decision of the data packet is determined by the following event trigger conditions:

[0027]

[0028] where is the error variable of the i-th sensor, h i is the sampling period of the i-th sensor, is the trigger time of the i-th sensor, δ i > 0 is the threshold parameter, Φ i is the weighting matrix to be designed;

[0029] Divide the trigger interval into a union of several sub-intervals where In addition, define the delay function: satisfying 0 ≤ d i (t) ≤ h i ;

[0030] In a multi-sensor network, the triggering moments of each event trigger are independent of each other; define the global triggering moment sequence That is, the set of moments when all event triggers release data packets, and it satisfies

[0031] To ensure that the controller can make decisions based on the latest state information, the data processing center is responsible for updating the system state according to the time stamp; at time s j The new signal generated by the data processing center is:

[0032]

[0033] Furthermore, the stochastic actuator fault model is u F (t) = F r(t) u(t), where F r(t) is the fault failure factor and u(t) is the control input under ideal conditions;

[0034] The closed-loop vehicle active suspension system obtained based on the stochastic actuator fault model and the multi-rate event-triggered scheduling protocol includes:

[0035] Define the error variable Then, under the action of the data processing center and the zero-order hold, the actual controller input u F (t) is:

[0036]

[0037] where K r(t) is the controller gain related to the fault;

[0038] Let r(t) = m; substitute the control input u F (t) into the vehicle active suspension system to obtain the dynamic equation of the closed-loop vehicle active suspension system.

[0039] Furthermore, the constructed stochastic actuator fault model is u F (t) = F r(t) u(t), where u(t) is the control input under ideal conditions, and:

[0040] {r(t), t > 0} is a right-continuous Markov process that takes values in the finite space and the transition rate matrix of r(t) is The corresponding transition probability is:

[0041]

[0042] In the formula, Δ > 0 represents an infinitesimal time interval that satisfies π mnDenote the transition rate from mode m at time t to mode n at time t+Δ. When m≠n, π mn ≥0, when m=n

[0043] F r(t) is the fault failure factor and satisfies is a constant scalar, representing the lower and upper bounds of the actuator fault respectively.

[0044] Furthermore, based on the stochastic actuator fault model, the fault-tolerant controller related to the fault mode is designed as:

[0045] u F (t)=F r(t) K r(t) x(t)

[0046] where, K r(t) is the controller gain related to the fault;

[0047] For the following three cases of the actuator, there are three different actuator working states:

[0048] The actuator fails completely, then

[0049] No fault occurs in the actuator, then

[0050] There is a partial fault in the actuator, then

[0051] Furthermore, based on the Lyapunov theory, sufficient conditions are established, including:

[0052] Given the threshold parameter δ i auxiliary parameter κ, sampling period h i , free parameter ε>0 and performance index parameter γ, for any mode If there exist positive definite matrices and and any matrix and diagonal matrix such that the following conditions hold, then the closed-loop vehicle active suspension system is stochastically stable and satisfies the H ∞ performance index:

[0053]

[0054] where:

[0055]

[0056]

[0057] where m s is the unsprung mass, m u is the sprung mass, k s is the suspension stiffness, k u is the tire stiffness, c s is the suspension damping, c u is the tire damping, z s is the displacement of the sprung mass, z u is the displacement of the unsprung mass, z r is the road disturbance input; x1 represents the suspension deflection, x2 represents the tire deflection, x3 represents the velocity of the sprung mass, and x4 represents the velocity of the unsprung mass; and are the upper and lower bounds of the actuator fault respectively; is a diagonal matrix, and the positions on the diagonal with 1 correspond to the i-th sensor measurement state component, satisfying K m is the controller gain related to the fault.

[0058] Furthermore, the vehicle suspension system dynamics model is as follows:

[0059]

[0060] where m s is the sprung mass, m u is the unsprung mass, k s is the suspension stiffness, k u is the tire stiffness, c s is the suspension damping, c u is the tire damping, z s is the displacement of the sprung mass, z u is the displacement of the unsprung mass, z r is the road disturbance input, u F (t) is the control input;

[0061] Define the following state variables:

[0062]

[0063] where x1 represents the suspension deflection, x2 represents the tire deflection, x3 represents the velocity of the sprung mass, and x4 represents the velocity of the unsprung mass;

[0064] Define the disturbance input and the system state variable x = col{x1,x2,x3,x4}, then the vehicle suspension system model is rewritten as:

[0065]

[0066] where:

[0067]

[0068] The physical constraint conditions set for the vehicle suspension system are as follows:

[0069]

[0070] Among them, z max is the set threshold;

[0071] In order to meet the physical constraint conditions, the following parameters are selected as the constraint output:

[0072]

[0073] Then, the state - space expression of the vehicle active suspension system is obtained as follows:

[0074]

[0075] Among them:

[0076]

[0077] The second objective of the present invention can be achieved by adopting the following technical solutions:

[0078] A multi - rate event - triggered fault - tolerant control device for a vehicle suspension system, the device includes:

[0079] A first construction module, which is used to construct a dynamic model of the vehicle suspension system in combination with Newton's second law; based on the dynamic model of the vehicle suspension system and the physical constraint conditions, obtain the vehicle active suspension system;

[0080] A second construction module, which is used to construct a stochastic actuator fault model based on the Markov process for the vehicle active suspension system to solve the problem of stochastic actuator faults; based on the stochastic actuator fault model, design a fault - tolerant controller related to the fault mode;

[0081] A design module, which is used to design a multi - rate event - triggered scheduling protocol based on the vehicle active suspension system to solve the problems of sampling rate differences and limited communication resources existing in the multi - sensor network;

[0082] A stability and fault - tolerance module, which is used to obtain a closed - loop vehicle active suspension system based on the stochastic actuator fault model and the multi - rate event - triggered scheduling protocol; based on Lyapunov theory, establish sufficient conditions to ensure the stochastic stability and H ∞ performance index of the closed - loop vehicle active suspension system; and calculate the controller gain to achieve the multi - rate sampling event - triggered fault - tolerant control of the vehicle active suspension system.

[0083] The third object of the present invention can be achieved by adopting the following technical solutions:

[0084] A terminal device includes a processor and a memory for storing programs executable by the processor. When the processor executes the programs stored in the memory, the multi-rate event-triggered fault-tolerant control method for the vehicle suspension system described above is implemented.

[0085] The fourth object of the present invention can be achieved by adopting the following technical solutions:

[0086] A computer-readable storage medium stores a program. When the program is executed by a processor, the multi-rate event-triggered fault-tolerant control method for the vehicle suspension system described above is implemented.

[0087] The present invention has the following beneficial effects compared with the prior art:

[0088] 1. The present invention organically combines multi-rate sampling control and event-triggering mechanism, effectively solving the coordination problem of different sampling frequencies in a multi-sensor network; by designing appropriate triggering rules, while ensuring control accuracy, it significantly reduces the occupancy of communication resources and realizes the efficient utilization of system resources.

[0089] 2. For the possible random faults of actuators, the present invention uses a Markov process to construct a random fault model. Compared with traditional fault models, the Markov model has stronger universality and flexibility. It can not only accurately reflect the fault characteristics under actual working conditions, but also adapt to different operating environments and fault modes by adjusting the state transition probability matrix. Description of the Drawings

[0090] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on the structures shown in these drawings.

[0091] Figure 1 It is a flowchart of the multi-rate event-triggered fault-tolerant control method for the vehicle suspension system in Embodiment 1 of the present invention.

[0092] Figure 2 It is a schematic diagram of the dynamic model of the vehicle suspension system in Embodiment 1 of the present invention.

[0093] Figure 3 It is a structural schematic of the networked vehicle suspension system based on the multi-rate sampling event-triggering mechanism in Embodiment 1 of the present invention.

[0094] Figure 4For the suspension deflection, tire deflection, sprung mass velocity, and unsprung mass velocity over time in the active and passive suspension cases of Embodiment 1 of the present invention.

[0095] Figure 5 For the release time and interval variation of the multi-rate sampling event-triggering mechanism in Embodiment 1 of the present invention.

[0096] Figure 6 For the switching process of the fault mode in Embodiment 1 of the present invention.

[0097] Figure 7 For the trajectory curves of the suspension deflection and sprung mass velocity of the vehicle suspension system under the single-sensor network, multi-sensor multi-rate event-triggering, and multi-sensor single-rate event-triggering protocols in Embodiment 1 of the present invention.

[0098] Figure 8 For the schematic diagram of the simulation results of the release time and interval variation of the event-triggering mechanism under the single-sensor network, multi-sensor multi-rate event-triggering, and multi-sensor single-rate event-triggering protocols in Embodiment 1 of the present invention.

[0099] Figure 9 For the structural block diagram of the multi-rate event-triggering fault-tolerant control device for the vehicle suspension system in Embodiment 2 of the present invention.

[0100] Figure 10 For the structural block diagram of the terminal device in Embodiment 3 of the present invention. Detailed implementation manners

[0101] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention. It should be understood that the described specific embodiments are only used to explain the present application and are not used to limit the present application.

[0102] Embodiment 1:

[0103] As Figure 1 shown, this embodiment provides a multi-rate event-triggering fault-tolerant control method for a vehicle suspension system, including the following steps:

[0104] S101. Combine Newton's second law to construct a dynamic model of the vehicle suspension system; based on the dynamic model of the vehicle suspension system and physical constraint conditions, obtain the vehicle active suspension system.

[0105] See Figure 2, the specific expression of the vehicle suspension system dynamics model is as follows:

[0106]

[0107] Among them, m s is the unsprung mass, m u is the sprung mass, k s is the suspension stiffness, k u is the tire stiffness, c s is the suspension damping, c u is the tire damping, z s is the displacement of the sprung mass, z u is the displacement of the unsprung mass, z r is the road disturbance input, u F (t) is the control input.

[0108] Next, define the following state variables:

[0109]

[0110] Among them, x1 represents the suspension deflection, x2 represents the tire deflection, x3 represents the velocity of the sprung mass, and x4 represents the velocity of the unsprung mass.

[0111] By defining the disturbance input and the system state variable x = col{x1, x2, x3, x4}, the vehicle suspension system model is rewritten as:

[0112]

[0113] Among them:

[0114]

[0115] Since the vertical acceleration of the vehicle body is a key index to measure ride comfort, a smaller acceleration corresponds to a better comfort performance. Therefore, minimizing the vertical acceleration of the vehicle body is taken as the primary goal. At the same time, to ensure the safety of the vehicle structure, the suspension deflection should be strictly controlled within the design allowable range. In addition, to ensure the driving stability of the vehicle, the dynamic load of the tire should be less than its static load value. Based on the above analysis, the following physical constraint conditions are set for the vehicle suspension system in this embodiment:

[0116]

[0117] Among them, z max is the set threshold.

[0118] To meet the above requirements, the following parameters are selected as the constraint outputs in this embodiment:

[0119]

[0120] Therefore, based on the above constraints, the state - space expression of the vehicle active suspension system is established as follows:

[0121]

[0122] Where:

[0123]

[0124] S102. Based on the vehicle active suspension system, construct a stochastic actuator fault model based on the Markov process; based on the stochastic actuator fault model, design a fault - tolerant controller related to the fault mode.

[0125] Based on the vehicle active suspension system, construct a stochastic actuator fault model based on the Markov process for the problem of random actuator faults; based on the stochastic actuator fault model, use the transition probability matrix to characterize the evolution characteristics of the fault mode, and design a fault - tolerant controller related to the fault mode.

[0126] In the case of actuator faults, the stochastic actuator fault model is constructed as:

[0127] u F (t) = F r(t) u(t)

[0128] Where, {r(t), t > 0} represents a right - continuous Markov process, which takes values in the finite space The transition rate matrix of r(t) is The corresponding transition probability is:

[0129]

[0130] In the formula, Δ > 0 represents an infinitesimal time interval, satisfying π mn represents the transition rate from mode m at time t to mode n at time t + Δ; when m ≠ n, π mn ≥ 0; when m = n,

[0131]

[0132] Where, F r(t) is defined as the fault failure factor and satisfies Where and are constant scalars, representing the lower bound and upper bound of the actuator fault respectively.

[0133] Under the above stochastic actuator faults, the fault - tolerant controller related to the mode is designed as:

[0134] uF F(t) = F r(t) K r(t) x(t)

[0135] where K r(t) is the controller gain related to the fault.

[0136] Consider the following three cases, corresponding to three different actuator operating states respectively:

[0137] The actuator fails completely, i.e.,

[0138] No fault occurs in the actuator, i.e.,

[0139] There is a partial fault in the actuator, i.e.,

[0140] S103. Based on the vehicle active suspension system, design a multi-rate event-triggered scheduling protocol for the problems of sampling rate difference and communication resource limitation existing in the multi-sensor network, and obtain a closed-loop vehicle active suspension system.

[0141] In practical applications, a single sensor has limitations such as insufficient information and hardware limitations, which affect control accuracy and reliability. To overcome these limitations, this embodiment uses multiple sensors to measure different types of system state variable information to improve the adaptability and reliability of the system.

[0142] Refer to Figure 3 , assume that there are p sensors, denoted as {S1, S2,..., S p p}. The sampling period of the i-th sensor is h i to obtain the state of the system at the following time, and the measured state component information is where To optimize the use of network resources and clarify the functions of each sensor, assume that the measured state components of each sensor do not overlap. The state components {x1, x2, x3, x4} will be divided into p groups, which are respectively associated with p sensors, specifically:

[0143]

[0144] where is the sparse representation of the measured state component of the i-th sensor, only storing the corresponding measured state component, and other elements are all zero. By introducing the state extraction matrix E i , the sparse vector χ i (t) can be associated with the system state x(t), and the specific representation is as follows:

[0145] χ i (t) = E i x(t)

[0146] Among them, is a diagonal matrix, and the positions on the diagonal with 1 correspond to the measurement state components of the i-th sensor, satisfying When i≠j, E i ×E j = 0.

[0147] In addition, to avoid excessive consumption of limited communication resources of the vehicle suspension system, a multi-sensor multi-rate sampling event-triggered protocol is introduced. While retaining the desired suspension performance, only necessary sampling data is transmitted. For the i-th sensor channel, the transmission decision of the data packet is determined by the following event-triggering condition:

[0148]

[0149] Among them, is the error variable of the i-th sensor, h i is the sampling period of the i-th sensor, is the triggering moment of the i-th sensor, δ i > 0 is the threshold parameter, and Φ i is the weighting matrix to be designed.

[0150] Divide the triggering interval into the union of several sub-intervals where Among them

[0151] In addition, define the delay function: Satisfying 0 ≤ d i (t) ≤ h i .

[0152] In the multi-sensor network, the triggering moments of each event trigger are independent of each other. To ensure that the controller can make decisions based on the latest state information, the data processing center (DPPC) is responsible for updating the system state according to the time stamp. Define the global triggering moment sequence That is, the set of moments when all event triggers release data packets, and satisfy At the moment of s j , the new signal generated by the DPPC is:

[0153]

[0154] Define the error variable Then, under the action of the DPPC and the zero-order hold, the actual controller input is:

[0155]

[0156] To simplify the representation, let r(t) = m. Substitute the control input u F (t) into the vehicle suspension system, and the dynamic equation of the closed-loop vehicle active suspension system is obtained as follows:

[0157]

[0158] S104. Based on the Lyapunov theory, establish sufficient conditions to ensure the stochastic stability and H ∞ performance index of the closed-loop vehicle active suspension system; and calculate the controller gain to achieve the multi-rate sampling event-triggered fault-tolerant control of the vehicle active suspension system.

[0159] This step is based on the Lyapunov theory to establish sufficient conditions to ensure the stochastic stability and H ∞ performance index of the vehicle active suspension system, and further calculate the controller gain.

[0160] Given the threshold parameter δ i , the auxiliary parameter κ, the sampling period h i , the free parameter ε > 0, and the performance index parameter γ, for any mode If there exist a positive definite matrix and any matrix and the diagonal matrix such that the following conditions hold, then the vehicle active suspension system is stochastically stable and satisfies the H ∞ performance index:

[0161]

[0162] Where:

[0163]

[0164]

[0165] The specific calculation method of the gain matrix is as follows:

[0166] Perform simulation analysis on the control method provided in this embodiment. Solve the linear matrix inequality by writing a Matlab program to obtain the controller gain, and verify the effectiveness of the proposed multi-rate sampling event-triggered control method in the vehicle active suspension system:

[0167] Consider a 1 / 4 vehicle active suspension system, and select the following system parameters: m s = 350 kg, m u = 40 kg,, k s = 18000 N / m, k u = 200000 N / m, c s= 1000 Ns / m, c s = 10 Ns / m, assuming the maximum allowable suspension displacement is z max = 0.1 m, the vehicle speed is set to V = 45 km / h, and the road displacement input simulates a scenario with local bumps on a flat road surface. The mathematical model of its disturbance input v(t) can be described as:

[0168]

[0169] where H = 60 mm represents the height of the obstacle; L = 5 m represents the length of the obstacle.

[0170] Consider a sensor network consisting of two sensors, i.e., p = 2. Among them, assume that the sampling period of sensor S1 is h1 = 0.02 s, and the sampling period of sensor S2 is h2 = 0.03 s. To achieve the optimal allocation of state variables, the system state components x1, x2, x3 are allocated to sensor S1, while the state component x4 is allocated to sensor S2. Based on the above state allocation scheme, the following state extraction matrix can be obtained:

[0171]

[0172] Next, consider the following three possible actuator failure modes:

[0173] (i) The actuator is normal,

[0174] (ii) The actuator has a partial failure,

[0175] (iii) The actuator fails completely,

[0176] Assume the transition probability matrix is:

[0177] Select the performance index parameter γ = 32, the threshold parameters δ1 = 0.08, δ2 = 0.15 in the event-triggering mechanism, and other parameters κ = 0.5, ε = 0.15. Substitute these parameters into the linear matrix inequality in step S104, and the gain of the controller can be obtained as:

[0178] K1 = 10 3 ×[-2.8324 8.5879 0.1113 0.0004],

[0179] K2 = 10 3 ×[2.0611 2.5581 0.0146 -0.0003],

[0180] K3 = [0 0 0 0].

[0181] Table 1 Packet transmission volume under different sampling event triggering schemes

[0182]

[0183] The simulation results are as Figures 4 to 8 shown. Figure 4 The suspension deflections, tire deflections, sprung mass velocities, and unsprung mass velocities of the active suspension and passive suspension systems over time were compared. The results show that under the action of the event-triggered fault-tolerant controller, the state curves of the active suspension system can quickly converge to a stable state, fully demonstrating the significant advantages of the active suspension system in terms of shock absorption effect and stability. Figure 5 and Figure 6 respectively show the release moments and interval changes of the multi-rate sampling event-triggering mechanism and the switching process of the fault modes. In addition, to verify the effectiveness of the proposed multi-rate sampling event-triggering protocol, this embodiment further compares it with the single-sensor network and multi-sensor single-rate event-triggering schemes. Table 1 lists the packet transmission volumes under different sampling event-triggering schemes. It can be seen that the multi-sensor multi-rate sampling event-triggering protocol is significantly lower than the single-sensor single-rate and multi-sensor single-rate event-triggering schemes in terms of packet transmission volume. Figure 7 and Figure 8 respectively show the dynamic response curves of the suspension deflections and sprung mass velocities under the three schemes, as well as the release moments and interval changes of the event-triggering mechanism. The comparison results show that the multi-sensor multi-rate sampling event-triggering control scheme can not only achieve fast stability, but also significantly reduce the packet transmission volume, effectively reducing the network bandwidth occupancy.

[0184] Based on the above simulation results, the following conclusions can be drawn: The vehicle active suspension system control scheme based on the multi-rate sampling event-triggering mechanism proposed in this embodiment has significant advantages, specifically reflected in aspects such as fast response, control effect, and saving communication resources, providing an effective solution for the control of vehicle active suspension systems.

[0185] Those skilled in the art can understand that all or part of the steps in the methods of the above embodiments can be completed by instructing relevant hardware through a program, and the corresponding program can be stored in a computer-readable storage medium.

[0186] It should be noted that although the method operations of the above embodiments are described in a specific order in the drawings, this does not require or imply that these operations must be performed in that specific order, or that all the shown operations must be performed to achieve the desired result. On the contrary, the described steps can be changed in the execution order. Additionally or alternatively, some steps can be omitted, multiple steps can be combined into one step for execution, and / or one step can be decomposed into multiple steps for execution.

[0187] Example 2:

[0188] As Figure 9 shown, this embodiment provides a multi-rate event-triggered fault-tolerant control device for a vehicle suspension system. The device includes a first construction module 901, a second construction module 902, a design module 903, and a stability and fault-tolerant module 904, where:

[0189] The first construction module 901 is used to construct a dynamic model of the vehicle suspension system in combination with Newton's second law; based on the dynamic model of the vehicle suspension system and physical constraint conditions, obtain the vehicle active suspension system;

[0190] The second construction module 902 is used to construct a stochastic actuator fault model based on the Markov process to solve the problem of stochastic actuator faults based on the vehicle active suspension system; based on the stochastic actuator fault model, design a fault-tolerant controller related to the fault mode;

[0191] The design module 903 is used to design a multi-rate event-triggered scheduling protocol based on the vehicle active suspension system to solve the problems of sampling rate differences and limited communication resources existing in multi-sensor networks;

[0192] The stability and fault-tolerant module 904 is used to obtain a closed-loop vehicle active suspension system based on the stochastic actuator fault model and the multi-rate event-triggered scheduling protocol; based on the Lyapunov theory, establish sufficient conditions to ensure the stochastic stability and H ∞ performance index of the closed-loop vehicle active suspension system; and further calculate the controller gain to achieve multi-rate sampling event-triggered fault-tolerant control of the vehicle active suspension system.

[0193] For the specific implementation of each module in this embodiment, reference can be made to the above-mentioned Embodiment 1, which will not be elaborated here one by one; it should be noted that the device provided in this embodiment is only illustrated by the above-mentioned division of each functional module. In practical applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure can be divided into different functional modules to complete all or part of the functions described above.

[0194] Example 3:

[0195] This embodiment provides a terminal device, which can be a computer, such as Figure 10As shown in the figure, it includes a processor 1002, a memory, an input device 1003, a display 1004, and a network interface 1005 connected through a system bus 1001. The processor is used to provide computing and control capabilities. The memory includes a non-volatile storage medium 1006 and an internal memory 1007. The non-volatile storage medium 1006 stores an operating system, a computer program, and a database. The internal memory 1007 provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. When the processor 1002 executes the computer program stored in the memory, it implements the multi-rate event-triggered fault-tolerant control method for the vehicle suspension system in the above-mentioned Embodiment 1 as follows:

[0196] Combined with Newton's second law, a dynamic model of the vehicle suspension system is constructed; based on the dynamic model of the vehicle suspension system and physical constraint conditions, an active vehicle suspension system is obtained;

[0197] Based on the active vehicle suspension system, a stochastic actuator fault model based on the Markov process is constructed to solve the problem of stochastic actuator faults; based on the stochastic actuator fault model, a fault-tolerant controller related to the fault mode is designed;

[0198] Based on the active vehicle suspension system, a multi-rate event-triggered scheduling protocol is designed to solve the problems of sampling rate differences and limited communication resources in the multi-sensor network; based on the multi-rate event-triggered scheduling protocol, a closed-loop active vehicle suspension system is obtained;

[0199] Based on Lyapunov theory, a sufficient condition is established to ensure the stochastic stability and H ∞ performance index of the closed-loop active vehicle suspension system; and the controller gain is calculated to achieve the multi-rate sampling event-triggered fault-tolerant control of the active vehicle suspension system.

[0200] Embodiment 4:

[0201] This embodiment provides a computer-readable storage medium that stores a computer program. When the computer program is executed by a processor, it implements the multi-rate event-triggered fault-tolerant control method for the vehicle suspension system in the above-mentioned Embodiment 1 as follows:

[0202] Combined with Newton's second law, a dynamic model of the vehicle suspension system is constructed; based on the dynamic model of the vehicle suspension system and physical constraint conditions, an active vehicle suspension system is obtained;

[0203] Based on the active vehicle suspension system, a stochastic actuator fault model based on the Markov process is constructed to solve the problem of stochastic actuator faults; based on the stochastic actuator fault model, a fault-tolerant controller related to the fault mode is designed;

[0204] Based on the vehicle active suspension system, a multi-rate event-triggered scheduling protocol is designed to solve the problems of sampling rate differences and limited communication resources existing in multi-sensor networks; based on the multi-rate event-triggered scheduling protocol, a closed-loop vehicle active suspension system is obtained.

[0205] Based on the Lyapunov theory, sufficient conditions are established to ensure the stochastic stability and H ∞ performance index of the closed-loop vehicle active suspension system; and the controller gain is calculated to achieve the multi-rate sampling event-triggered fault-tolerant control of the vehicle active suspension system.

[0206] It should be noted that the computer-readable storage medium in this embodiment can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0207] In summary, the present invention first constructs a dynamic model of the vehicle suspension system based on Newton's second law, and then obtains the vehicle active suspension system; secondly, aiming at the actuator fault problem, a stochastic actuator fault model based on the Markov process is constructed, and a fault-tolerant controller related to the fault mode is designed; then, aiming at the heterogeneous characteristics of the multi-sensor network, an event-triggered mechanism for multi-rate sampling is designed to achieve efficient data transmission of each sensor through different sampling rates; finally, based on the Lyapunov theory, sufficient conditions are established to ensure the stochastic stability and performance index of the vehicle active suspension system. Compared with the traditional method, the control strategy proposed by the present invention shows stronger adaptability and fault-tolerant ability under complex working conditions, and the designed event-triggered protocol can effectively reduce the consumption of network resources, further enhancing the robustness and reliability of the system.

[0208] The above is only a preferred embodiment of the present invention for patent, but the protection scope of the present invention for patent is not limited thereto. Any person skilled in the art within the scope disclosed by the present invention for patent, according to the technical solution and inventive concept of the present invention for patent, makes equivalent substitutions or changes, all belong to the protection scope of the present invention for patent.

Claims

1. A multi-rate event-triggered fault-tolerant control method for a vehicle suspension system, characterized in that: The method comprises: Combined with Newton's second law, the dynamic model of the vehicle suspension system is constructed; based on the dynamic model of the vehicle suspension system and physical constraints, the vehicle active suspension system is obtained; Based on the vehicle active suspension system, a random actuator fault model based on Markov process is constructed to solve the problem of random actuator failure; based on the random actuator fault model, a fault-tolerant controller related to the fault mode is designed; Based on the vehicle active suspension system, a multi-rate event-triggered scheduling protocol is designed to solve the sampling rate differences and communication resource constraints in multi-sensor networks. Based on the random actuator failure model and the multi-rate event-triggered scheduling protocol, a closed-loop vehicle active suspension system is obtained. Based on the Lyapunov theory, sufficient conditions are established to ensure the random stability and H stability of the closed-loop vehicle active suspension system. ∞ performance indicators; and calculate controller gains to achieve multi-rate sampling event-triggered fault-tolerant control of the vehicle active suspension system.

2. The vehicle suspension system multi-rate event-triggered fault-tolerant control method according to claim 1, characterized in that: The multi-rate event-triggered scheduling protocol is designed based on the vehicle active suspension system, including: Assume that there are p sensors, denoted as {S1,S2,...,S p }; The sampling period of the i-th sensor is h i , the measured state component information is in Assuming that the measured state components of each sensor do not overlap, the state components {x1, x2, x3, x4} are divided into p groups, which are associated with p sensors respectively, specifically: in, It is a sparse representation of the measurement state component of the i-th sensor, which only stores the corresponding measurement state component, and all other elements are zero; By introducing the state extraction matrix E i The sparse vector χ i (t) is associated with the system state x(t), which is specifically expressed as follows: χ i (t)=E i x(t) in, It is a diagonal matrix, and the positions with 1 on the diagonal correspond to the measured state components of the i-th sensor, satisfying When i≠j, E i ×E j =0; For the i-th sensor channel, the data packet transmission decision is determined by the following event trigger conditions: in, is the error variable of the ith sensor, h i is the sampling period of the ith sensor, is the triggering time of the i-th sensor, δ i >0 is the threshold parameter, Φ i is the weight matrix to be designed; Will trigger the interval Divide into several sub-intervals The union of in Additionally, define the delay function: Satisfy 0≤d i (t)≤h i ; In a multi-sensor network, the triggering time of each event trigger is independent of each other; define the global triggering time sequence That is, the set of times when all event triggers release data packets, and satisfy To ensure that the controller can make decisions based on the latest status information, the data processing center is responsible for updating the system status based on the timestamp; j At time , the new signal generated by the data processing center is:

3. The vehicle suspension system multi-rate event-triggered fault-tolerant control method according to claim 2, characterized in that: The random actuator failure model is u F (t) = F r(t) u(t), F r(t) is the fault failure factor, u(t) is the control input under ideal conditions; The closed-loop vehicle active suspension system is obtained based on the random actuator failure model and the multi-rate event-triggered scheduling protocol, including: Defining the error variable Then under the action of the data processing center and the zero-order holder, the actual controller input u F (t) is: Among them, K r(t) is the controller gain associated with the fault; Let r(t) = m; set the control input u F Substitute (t) into the vehicle active suspension system to obtain the closed-loop dynamic equation of the vehicle active suspension system.

4. The vehicle suspension system multi-rate event-triggered fault-tolerant control method according to any one of claims 1 to 3, characterized in that: The constructed random actuator failure model is u F (t) = F r(t) u(t), u(t) are the control inputs under ideal conditions, where: {r(t),t>0} is a right-continuous Markov process, which has a finite space The transfer rate matrix of r(t) is The corresponding transition probability is: Where Δ>0 represents an infinitesimal time interval, satisfying π mn It represents the transfer rate from mode m at time t to mode n at time t+Δ. When m≠n, π mn ≥0, when m=n F r(t) is the failure factor and satisfies and are constant scalars, representing the lower and upper bounds of actuator failure, respectively.

5. The vehicle suspension system multi-rate event-triggered fault-tolerant control method according to claim 4, characterized in that: Based on the random actuator fault model, the fault-tolerant controller related to the fault mode is designed as follows: u F (t)=F r(t) K r(t) x(t) Among them, K r(t) is the controller gain associated with the fault; For the following three situations of the actuator, there are three different actuator working states: If the actuator fails completely, If there is no fault in the actuator, If there is a partial failure in the actuator, 6. The vehicle suspension system multi-rate event-triggered fault-tolerant control method according to claim 1, characterized in that: Based on Lyapunov theory, sufficient conditions are established, including: Given a threshold parameter δ i , auxiliary parameter κ, sampling period h i , free parameter ε>0 and performance index parameter γ, for any mode If there exists a positive definite matrix and and any matrix and the diagonal matrix Z makes the following conditions hold, then the closed-loop vehicle active suspension system is stochastically stable and satisfies H ∞ Performance indicators: in: In the formula, m s is the sprung mass, m u is the unsprung mass, k s is the suspension stiffness, k u is the tire stiffness, c s is the suspension damping, c u is the tire damping, z s is the sprung mass displacement, z u is the unsprung mass displacement, z r is the road disturbance input; x1 represents the suspension deflection, x2 represents the tire deflection, x3 represents the sprung mass velocity, and x4 represents the unsprung mass velocity; and are the upper and lower bounds of actuator failure, respectively; It is a diagonal matrix. The position with 1 on the diagonal corresponds to the measurement state component of the i-th sensor, satisfying K m is the fault-related controller gain.

7. The vehicle suspension system multi-rate event-triggered fault-tolerant control method according to any one of claims 1 to 3, 5 and 6, characterized in that: The vehicle suspension system dynamics model is: Among them, m s is the sprung mass, m u is the unsprung mass, k s is the suspension stiffness, k u is the tire stiffness, c s is the suspension damping, c u is the tire damping, z s is the sprung mass displacement, z u is the unsprung mass displacement, z r is the road disturbance input, u F (t) is the control input; Define the following state variables: Among them, x1 represents the suspension deflection, x2 represents the tire deflection, x3 represents the sprung mass velocity, and x4 represents the unsprung mass velocity; Defining the disturbance input and system state variables x = col{x1,x2,x3,x4}, the vehicle suspension system model is rewritten as: in: The physical constraints set for the vehicle suspension system are: Among them, z max To set the threshold; In order to meet the physical constraints, the following parameters are selected as constraint outputs: Then the state space expression of the vehicle active suspension system is obtained as: in:

8. A multi-rate event-triggered fault-tolerant control device for a vehicle suspension system, characterized in that: The device comprises: The first building module is used to build a vehicle suspension system dynamics model in combination with Newton's second law; based on the vehicle suspension system dynamics model and physical constraints, the vehicle active suspension system is obtained; The second building block is used to build a random actuator fault model based on a Markov process based on the vehicle active suspension system to solve the problem of random actuator failure; based on the random actuator failure model, a fault-tolerant controller related to the fault mode is designed; Design module for designing multi-rate event-triggered scheduling protocols based on vehicle active suspension systems to solve the sampling rate differences and communication resource constraints in multi-sensor networks; Stability and fault tolerance module, which is used to obtain a closed-loop vehicle active suspension system based on a random actuator failure model and a multi-rate event-triggered scheduling protocol; based on the Lyapunov theory, sufficient conditions are established to ensure the random stability and H tolerance of the closed-loop vehicle active suspension system. ∞ performance indicators; and calculate controller gains to achieve multi-rate sampling event-triggered fault-tolerant control of the vehicle active suspension system.

9. A terminal device, comprising a processor and a memory for storing a program executable by the processor, characterized in that: When the processor executes the program stored in the memory, the multi-rate event-triggered fault-tolerant control method for the vehicle suspension system according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the multi-rate event-triggered fault-tolerant control method for a vehicle suspension system according to any one of claims 1 to 7 is implemented.

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