CN-121989610-A - Non-periodic sampling fault-tolerant control method and system for vehicle active suspension system

CN121989610ACN 121989610 ACN121989610 ACN 121989610ACN-121989610-A

Abstract

The invention provides a non-periodic sampling fault-tolerant control method and a system for a vehicle active suspension system, which relate to the technical field of vehicle chassis control, wherein the method comprises the steps of establishing a dynamic equation of the vehicle active suspension system and converting the dynamic equation into a state space model; based on a state space model, introducing constraint conditions of actuator saturation, tire dynamic load and suspension maximum travel, constructing a system output model containing the constraint conditions, combining the system output model with an input time-lag model and a Markov jump linear system model, and reconstructing the state space model to obtain the vehicle active suspension system input time-lag model containing random faults. The invention constructs a unified data processing framework, integrates multi-source data through the system and realizes the full-flow closed-loop design, and finally achieves the efficient non-periodic sampling fault-tolerant control of the vehicle active suspension system.

Inventors

LIN WENJUAN
LI ZHIZE
LV XIAOXIAO
KONG HUI
YU JINPENG

Assignees

青岛大学

Dates

Publication Date: 20260508
Application Date: 20260323

Claims (10)

1. An aperiodic sampling fault-tolerant control method for an active suspension system of a vehicle, which is characterized by comprising the following steps: step 100, establishing a dynamic equation of a vehicle active suspension system, and converting the dynamic equation into a state space model; Step 200, based on a state space model, introducing constraint conditions of actuator saturation, tire dynamic load and suspension maximum travel, and constructing a system output model containing the constraint conditions; step 300, combining the system output model with an input time-lag model and a Markov jump linear system model, and reconstructing the state space model to obtain the input time-lag model of the vehicle active suspension system containing random faults; Step 400, designing an aperiodic sampling fault-tolerant controller structure based on the input time-lag model of the vehicle active suspension system and combining an input time-lag method; Step 500, constructing a bilateral closed loop Lyapunov-kelasofos functional for the non-periodic sampling fault-tolerant controller structure, and introducing The performance index is used for inhibiting the road surface displacement disturbance, and matrix inequality conditions for enabling the closed loop system to be asymptotically stable are deduced by applying the Lyapunov-Kerasaviyl stability theory; step 600, solving the problem of meeting the specified requirement by adopting a linear matrix inequality method based on the matrix inequality condition Non-periodic sampling fault tolerant controller gain for performance indicators; and 700, performing non-periodic sampling fault-tolerant control on the vehicle active suspension system by using the gain of the non-periodic sampling fault-tolerant controller so as to verify the effectiveness and superiority of the control method.
2. The method according to claim 1, wherein the step 100 of establishing a dynamic equation of the vehicle active suspension system and converting the dynamic equation into a state space model includes: based on a quarter vehicle active suspension model, establishing a dynamic equation describing the motion of the sprung mass and the unsprung mass; According to the dynamics equation, defining a system state variable taking suspension deflection, tire deflection, sprung mass speed and unsprung mass speed as components; and re-expressing the dynamic equation as a state space model by using the system state variables.
3. The method according to claim 2, wherein the step 200 of introducing constraints of actuator saturation, tire dynamic load and suspension maximum travel based on the state space model to construct a system output model including the constraints comprises: based on the state space model, respectively establishing constraint inequality representing the saturation of an actuator, the dynamic load of a tire and the maximum travel of a suspension; normalizing the constraint inequality to construct a constraint output vector comprising suspension deflection constraint, tire dynamic load constraint and actuator saturation constraint; And integrating the constraint output vector into a part of system output on the basis of the state space model, thereby constructing a system output model containing multiple constraint conditions.
4. The method of claim 3, wherein the step 300 of combining the system output model with an input time-lag model and a markov jump linear system model to reconstruct the state space model to obtain the vehicle active suspension system input time-lag model including random faults comprises: based on the system output model, introducing random parameter fluctuation described by a continuous time Markov process to represent random jump characteristics of a suspension stiffness coefficient and a damping coefficient relative to respective nominal values thereof so as to establish a modal-dependent active suspension system state equation; based on the state equation of the active suspension system depended on by the mode, introducing a diagonal form fault gain matrix to quantitatively represent the continuous failure degree of the actuator from complete failure to normal operation, and constructing a controlled input containing random parameter jump and actuator faults; based on the controlled input, defining the time difference between the current time and the last sampling time as a time-varying input delay in combination with a time sequence of non-periodic sampling; and reconstructing the system state equation into a Markov jump system model containing the delay by utilizing the time-varying input delay, so as to obtain the vehicle active suspension system input time-lag model containing random faults.
5. The method according to claim 4, wherein the step 400 of designing the non-periodic sampling fault tolerant controller structure based on the vehicle active suspension system input time lag model and in combination with the input time lag method comprises: Based on the input time lag model of the vehicle active suspension system, determining that the control input of the controller is a function of the system state at the non-periodic sampling moment; According to the control input being a function of the system state at the non-periodic sampling moment, constructing a controller structure taking the system state at the sampling moment as input and feeding back by a gain matrix related to a system mode; Substituting the controller structure into the vehicle active suspension system input time lag model to obtain a closed loop system equation containing the to-be-solved controller gain, system mode and input delay, and completing the design of the non-periodic sampling fault-tolerant controller structure.
6. The method according to claim 5, wherein the step 500 is performed to construct a double-sided closed-loop lyapunov-kelasofos function for the non-periodic sampling fault-tolerant controller structure, and introducing The performance index is used for inhibiting the disturbance of the pavement displacement, and matrix inequality conditions for enabling the closed loop system to be asymptotically stable are deduced by applying the Lyapunov-Kerasaviyl stability theory, and the matrix inequality conditions comprise: constructing a bilateral closed-loop Lyapunov-kelasofos functional based on the closed-loop system equation containing the gain, the system mode and the input delay of the controller to be solved; introducing a double-sided closed loop Lyapunov-kelasofos functional into the double-sided closed loop Lyapunov-kelasofos functional Performance indexes are used for establishing gain constraint relation to be met between system output and road surface displacement disturbance derivative; Based on the gain constraint relation, pair introduction Solving an infinite small operator of the Lyapunov-kelasofos functional of the performance index along the track of the closed-loop system to obtain a differential result; processing the differential result using integral inequality and matrix inequality theory to derive a solution that ensures asymptotically stable closed loop system and satisfies the condition Matrix inequality conditions for performance metrics.
7. The method according to claim 6, wherein the step 600 is to solve the problem that the specification is satisfied by using a linear matrix inequality method based on the linear matrix inequality condition An aperiodic sampling fault tolerant controller gain for a performance indicator, comprising: Defining a series of matrix variables to be solved related to the lyapunov-kelasofos functional and controller gain based on the linear matrix inequality condition; Performing contract transformation on the matrix inequality condition containing the matrix variables to be solved, and converting the matrix inequality condition into a matrix inequality form suitable for numerical solution to obtain a matrix inequality form subjected to contract transformation; applying a Shull's complement theory to the contractual transformed matrix inequality form to eliminate nonlinear terms therein, thereby deriving a set of linear matrix inequality sets for the matrix variables to be solved; Solving the linear matrix inequality group to obtain a feasible solution of matrix variables to be solved, so as to calculate and obtain the required specification Aperiodic sampling fault tolerant controller gain for performance metrics.
8. The method according to claim 7, wherein the step 700 of using the gain of the non-periodic sampling fault tolerant controller performs non-periodic sampling fault tolerant control on the vehicle active suspension system to verify the effectiveness and superiority of the control method comprises: based on the gain of the non-periodic sampling fault-tolerant controller, and combining preset parameters of a vehicle active suspension system and set random fault parameters, constructing a closed-loop simulation system for implementing control; Setting a plurality of road surface input conditions including bumpy road surface excitation and random road surface excitation for the closed loop simulation system; Based on the multiple pavement input conditions, operating the closed-loop simulation system, and acquiring state response and constraint output response of the system under non-periodic sampling fault-tolerant control; based on the state response and the constraint output response, and the response result obtained by simulating the preset comparison control method under the same condition, performing comparison analysis to generate performance comparison data, and verifying the effectiveness and superiority of the control method in the aspects of meeting system constraint, inhibiting road disturbance and saving communication resources based on the performance comparison data.
9. A vehicle active suspension system aperiodic sampling fault tolerant control system implementing the method of any one of claims 1 to 8, comprising: the modeling module is used for establishing a dynamic equation of the vehicle active suspension system and converting the dynamic equation into a state space model; The constraint modeling module is used for introducing constraint conditions of actuator saturation, tire dynamic load and suspension maximum travel based on the state space model, and constructing a system output model containing the constraint conditions; the reconstruction module is used for combining the system output model with an input time-lag model and a Markov jump linear system model, and reconstructing the state space model to obtain the input time-lag model of the vehicle active suspension system containing random faults; the structural design module is used for designing a non-periodic sampling fault-tolerant controller structure based on the input time-lag model of the vehicle active suspension system and combining an input time-lag method; the stability and performance synthesis module is used for constructing a bilateral closed-loop Lyapunov-Kelasofos functional aiming at the structure of the non-periodic sampling fault-tolerant controller, and introducing The performance index is used for inhibiting the road surface displacement disturbance, and matrix inequality conditions for enabling the closed loop system to be asymptotically stable are deduced by applying the Lyapunov-Kerasaviyl stability theory; A solving module for solving the problem of meeting the specified requirement by adopting a linear matrix inequality method based on the matrix inequality condition Non-periodic sampling fault tolerant controller gain for performance indicators; and the verification module is used for utilizing the gain of the non-periodic sampling fault-tolerant controller to implement non-periodic sampling fault-tolerant control on the vehicle active suspension system so as to verify the effectiveness and superiority of the control method.
10. A computing device, comprising: One or more processors; Storage means for storing one or more programs which when executed by the one or more processors cause the one or more processors to implement the method of any of claims 1 to 8.

Description

Non-periodic sampling fault-tolerant control method and system for vehicle active suspension system Technical Field The invention relates to the technical field of vehicle chassis control, in particular to a non-periodic sampling fault-tolerant control method and system for a vehicle active suspension system. Background With the popularization of vehicle-mounted network technology, a control strategy based on sampling data has become a key for improving the performance of an active suspension system of a vehicle. However, the existing method has a plurality of defects on the data processing level, and further improvement of system performance and reliability is restricted. Firstly, in the data acquisition and transmission links, the fixed period sampling mechanism and the limited network resources are widely adopted, the fixed high sampling rate possibly causes network congestion and resource waste, the key dynamic information is lost when the sampling rate is reduced, the control quality is damaged, secondly, in the modeling layer of the data characteristics, the existing method fails to accurately describe the nonideal characteristic in the actual system, the nonideal characteristic is affected by network delay and task scheduling, the actual sampled data flow has aperiodicity and random jitter, most of the controller designs are still based on ideal period sampling assumption, the robustness in the actual network environment is possibly reduced, furthermore, in the utilization layer of the data information, the traditional stability analysis method possibly has the problem of insufficient information mining, for example, the conventional input time lag method and the Liposofv-Kelasofuji functional method are usually only utilized, the relevance of state data between adjacent sampling intervals and complete evolution information in intervals are ignored, finally, the existing scheme lacks a unified frame to cooperatively process the heterogeneous data, the system needs to simultaneously meet the requirements of an executor, the saturation constraint of the system, the random constraint of the physical constraint of the system can be easily and stably controlled, or the fault tolerance of the system can be easily and stably processed, and the fault tolerance can be easily and stably controlled at the same time. Disclosure of Invention The invention aims to solve the technical problem of providing a non-periodic sampling fault-tolerant control method and system for a vehicle active suspension system, which construct a unified data processing frame, integrate multi-source data through the system and realize full-flow closed-loop design, and finally achieve efficient non-periodic sampling fault-tolerant control for the vehicle active suspension system. In order to solve the technical problems, the technical scheme of the invention is as follows: In a first aspect, a method for aperiodic sampling fault tolerant control of an active suspension system of a vehicle, the method comprising: establishing a dynamic equation of a vehicle active suspension system, and converting the dynamic equation into a state space model; Based on a state space model, introducing constraint conditions of actuator saturation, tire dynamic load and suspension maximum travel, and constructing a system output model containing the constraint conditions; Combining the system output model with an input time-lag model and a Markov jump linear system model, and reconstructing the state space model to obtain an input time-lag model of the vehicle active suspension system containing random faults; Based on the input time lag model of the vehicle active suspension system, designing an aperiodic sampling fault-tolerant controller structure by combining an input time lag method; Aiming at the structure of the non-periodic sampling fault-tolerant controller, constructing a bilateral closed-loop Lyapunov-Kelasofos functional, and introducing The performance index is used for inhibiting the road surface displacement disturbance, and matrix inequality conditions for enabling the closed loop system to be asymptotically stable are deduced by applying the Lyapunov-Kerasaviyl stability theory; based on the matrix inequality condition, solving the condition meeting the specification by adopting a linear matrix inequality method Non-periodic sampling fault tolerant controller gain for performance indicators; and utilizing the gain of the non-periodic sampling fault-tolerant controller to implement non-periodic sampling fault-tolerant control on the vehicle active suspension system so as to verify the effectiveness and superiority of the control method. In a second aspect, a vehicle active suspension system non-periodic sampling fault tolerant control system includes: the modeling module is used for establishing a dynamic equation of the vehicle active suspension system and converting the dynamic equation into a state space model; The constraint modeling mo