An intelligent vehicle steering system fault-tolerant control method based on event-triggered communication mechanism

Abstract

The application discloses a fault-tolerant control method for a smart vehicle steering system based on an event-triggered communication mechanism, and researches a fault-tolerant control problem of a steer-by-wire system existing in a remote control vehicle, wherein the steer-by-wire system is limited in wireless network communication bandwidth and has a steering motor actuator fault. First, in view of the two core problems of the limited wireless network communication bandwidth and the steering motor actuator fault, an active fault-tolerant controller is designed for the steer-by-wire system in combination with an event-triggered communication mechanism. In the controller design, the event-triggered communication mechanism is used to realize "communication on demand", so that the wireless network communication resources between a client and the remote vehicle are effectively saved, and meanwhile, a corresponding fault-tolerant control strategy is adopted to ensure that the system can still obtain satisfactory control performance when the actuator is faulty. Compared with existing control methods, the application not only solves the problem of limited communication bandwidth in remote control, but also effectively deals with the actuator fault, and significantly improves the reliability and practicability of the steer-by-wire system of the remote control vehicle.

Description

Technical Field

[0001] This invention belongs to the field of steering angle tracking control of intelligent vehicle steering systems. It addresses the problems of limited communication bandwidth and high transmission energy consumption in wireless data transmission of remotely controlled vehicles by designing a fault-tolerant controller based on event-triggered communication mechanism. Background Technology

[0002] With the rapid iteration of intelligent driving technology, remote vehicle control is increasingly widely used in special scenarios such as engineering operations and hazardous materials handling. However, in the actual application of remote vehicle control, wireless network data transmission has inherent technical bottlenecks: On the one hand, wireless network transmission itself has energy-consuming characteristics and limited communication bandwidth resources. Traditional time-triggered communication methods use fixed-period data transmission, which will continuously execute communication tasks regardless of whether the current system operation status requires it. This can easily lead to excessive energy consumption and bandwidth saturation in wireless communication, resulting in data transmission delays, packet loss, and other problems. This seriously affects the control accuracy and response speed of the steer-by-wire system and may even threaten vehicle driving safety. On the other hand, scenarios such as remote control of engineering vehicles and hazardous materials handling vehicles have extremely high requirements for system reliability. As the core execution component, the actuator of the steer-by-wire system is prone to failure during long-term high-load operation. Once the actuator fails and cannot be compensated in time, the vehicle will lose its steering control ability or its steering performance will drop significantly, failing to meet basic driving needs and potentially causing serious safety accidents and property damage.

[0003] Event-triggered mechanisms have gained widespread attention and application due to their ability to significantly conserve communication resources in networked control systems. The basic idea of ​​this communication mechanism is "on-demand" execution; that is, control and communication tasks are only executed when the system meets certain conditions, thus avoiding redundant communication caused by traditional time-triggered mechanisms. Furthermore, fault-tolerant control methods can guarantee the basic performance of the system when actuators fail. Therefore, fault-tolerant control methods combining event-triggered communication mechanisms can solve the problems of communication bandwidth limitations and actuator failures in networked vehicles. Currently, research on event-triggered fault-tolerant control has made some progress: some studies have designed event-triggered fault-tolerant control strategies for nonlinear systems with weak model uncertainty and actuator failures; other studies have combined fuzzy logic systems to design event-triggered fault-tolerant controllers for nonlinear systems with model uncertainty, actuator failures, and input saturation; in addition, such controllers have been applied in spacecraft, surface ships, and underactuated vehicles. However, existing technologies still have significant limitations and are difficult to adapt to the actual operational requirements of remote-controlled vehicle steer-by-wire systems: First, most existing event-triggered fault-tolerant control strategies only consider the conservation of channel communication resources between the controller and the actuator, without taking into account the channel resource limitations between the sensor and the controller. This makes it impossible to achieve optimized utilization of communication resources across the entire link, and local bandwidth limitations may still occur. Second, some event-triggered fault-tolerant controllers using sliding mode control rely on the premise that the system control gain is known. However, remote-controlled vehicle steer-by-wire systems have complex model uncertainties, making it difficult to accurately obtain the control gain. Furthermore, the discontinuous sign function in the sliding mode controller can cause high-frequency chattering in the controlled system, severely affecting the control stability and lifespan of the steer-by-wire system. In addition, some event-triggered fault-tolerant controllers that combine fuzzy logic systems also require a known upper bound on the control gain, making them unsuitable for real-world scenarios where the control gain is unknown in steer-by-wire systems, thus limiting their applicability.

[0004] In summary, current control methods for remotely controlled vehicle steer-by-wire systems cannot effectively balance the demands for conserving wireless network communication resources, actuator fault tolerance, model uncertainty adaptation, and control stability. These methods suffer from limited applicability and insufficient control performance. Therefore, developing a control method that adapts to the characteristics of steer-by-wire systems—model uncertainty, actuator susceptibility to failure, and limited communication bandwidth—and achieves coordinated control that conserves communication resources and provides fault tolerance, along with high control stability and strong adaptability, is of great significance for promoting the industrial application of remotely controlled vehicles and improving their operational safety and reliability. This is also the core starting point of this patent research. Summary of the Invention

[0005] This invention addresses the limitations of existing technologies and ensures stable system operation in steer-by-wire systems with limited network communication bandwidth, uncertain models, and actuator failures. It designs a fault-tolerant controller based on an event-triggered communication mechanism.

[0006] This controller integrates core technologies: it adopts sliding mode controller dynamic gain technology to eliminate the impact of actuator failure, external disturbances and unknown control gain on control performance; it approximates the uncertain nonlinear terms of the system through an interval type II fuzzy logic system; and it uses filter technology to eliminate chattering caused by discontinuous control terms of the sliding mode controller.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] This invention discloses a fault-tolerant control method for an intelligent vehicle steering system based on an event-triggered communication mechanism, comprising the following steps:

[0009] S1. Establish a mathematical model of the steer-by-wire system that takes into account network communication constraints, model uncertainties, and actuator failures;

[0010] S2. An interval-type II fuzzy logic system is used to approximate and model the uncertain nonlinear terms in the steer-by-wire system online.

[0011] S3. Design an event-triggered mechanism based on the event-triggered communication principle to save network communication resources;

[0012] S4. Construct the Lyapunov function;

[0013] S5. A fault-tolerant controller based on event-triggered communication mechanism was designed.

[0014] As a preferred technical solution, in step S1, the mathematical model of the steer-by-wire system is:

[0015] (1)

[0016] In the formula: For the system's state variables, and These are the system's input and output, respectively. This indicates a nonlinearity including the restoring torque and the frictional torque. To control the gain, This indicates an external time-varying disturbance. and These represent the steering angle and angular velocity of the front wheel, respectively.

[0017] Furthermore, the steering motor is highly sensitive to electromagnetic interference and electronic malfunctions. When the actuator fails, the steering motor cannot generate sufficient torque to drive the front wheels to track its reference steering angle according to the designed control signal, which may cause the autonomous driving system to deviate from its reference path. Therefore, the controller design needs to consider the impact of actuator failure. Considering the actuator failure phenomenon, the input of the steer-by-wire system can be expressed as:

[0018] (2)

[0019] In the formula: and These represent partial failure and floating failure, respectively. When and This indicates that there is no actuator failure. Combining equations (1) and (2), the steer-by-wire system model considering actuator failure and other phenomena can be expressed as:

[0020] (3)

[0021] In the formula: .

[0022] As a preferred technical solution, in step S2...

[0023] For any continuous function In close proximity If so, then there must exist an optimal parameter vector and a zero-order interval type-II fuzzy logic system that satisfies:

[0024] (4)

[0025] In the formula: Let be the membership function of the type-two fuzzy logic system, where and These are the left and right limits of a type-II fuzzy logic system, respectively. This is the upper bound of the approximation error of the type II fuzzy logic system.

[0026] As a preferred technical solution, in step S3...

[0027] Considering the limitations of wireless network transmission and communication resources, an event-triggered mechanism is adopted for data communication. The designed event-triggered communication mechanism is as follows:

[0028] (5)

[0029] In the formula: , , , , ,

[0030] , , , , and For the design of positive constants.

[0031] As a preferred technical solution, in step S4...

[0032] Construct the following Lyapunov function:

[0033] (6)

[0034] As a preferred technical solution, in step S5...

[0035] The sliding surface is designed as follows:

[0036] (7)

[0037] The designed controller can be represented as:

[0038] (8)

[0039] In the formula: and , and Unknown parameters , and The estimation results, i.e.

[0040] (9)

[0041] (10)

[0042] (11)

[0043] And the function , and They are defined as follows:

[0044] (12)

[0045] (13)

[0046] (14)

[0047] In the formula: and For the design of positive constants, is the Lipschitz constant of the basis functions of the fuzzy logic system. and Let be the parameter vector and basis vector of the type-II fuzzy logic system, respectively. and Let represent the left basis vector and right basis vector of the type-II fuzzy logic system, respectively.

[0048] design , and The adaptive law is:

[0049] (15)

[0050] (16)

[0051] (17)

[0052] In the formula: , , , , and All are positive constants from the design. Parameter vector of the fuzzy logic system. The adaptive rate design is as follows:

[0053] (18)

[0054] In the formula: , and All are positive constants in the design.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] 1. The core function of the event triggering mechanism proposed in this invention is "on-demand communication." Compared with the traditional time-triggered method, it eliminates the need for continuous data transmission at fixed intervals, significantly reducing the invalid transmission of redundant data and effectively saving communication resources of wireless networks and CAN buses. This fundamentally avoids problems such as signal congestion and delays caused by limited communication bandwidth. At the same time, by reasonably setting the trigger threshold, it can ensure the timely and accurate transmission of key control commands, perfectly adapting to the communication needs of remotely controlled vehicles and improving the reliability and real-time performance of remote steering control.

[0057] 2. The controller designed in this invention exhibits strong adaptive capabilities and robustness in the face of complex operating conditions and actuator failures. In this controller design, the sliding mode controller dynamic gain technology is employed to effectively eliminate the adverse effects of actuator failures, external disturbances, and unknown control gain on the control performance of the steer-by-wire system. Simultaneously, an interval-type II fuzzy logic system is used to approximate the uncertain nonlinear terms in the steer-by-wire system. Furthermore, the introduction of filter technology effectively suppresses the chattering phenomenon caused by discontinuous control terms of the sliding mode controller on the steer-by-wire system, further ensuring the stability and smoothness of the system control. Attached Figure Description

[0058] Figure 1 This is a schematic diagram of a steer-by-wire system with actuator failure and network communication features.

[0059] Figure 2 This is a schematic diagram illustrating the tracking performance of the front wheel steering angle of the invented steer-by-wire system.

[0060] Figure 3 This is a schematic diagram of the tracking error of the front wheel steering angle of the invented steer-by-wire system.

[0061] Figure 4 This is a schematic diagram comparing the system input and the designed control quantity of the invented steer-by-wire system.

[0062] Figure 5 This is a schematic diagram showing the event trigger count results of the steer-by-wire system of the invention.

[0063] Figure 6 For the invention A schematic diagram of the vector estimation results.

[0064] Figure 7 This is a schematic diagram showing the estimation results of unknown parameters in the steer-by-wire system of the invention. Detailed Implementation

[0065] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments, but the present invention is not limited to the following embodiments.

[0066] Example

[0067] Considering the limitations of network communication bandwidth, model uncertainty, and actuator failure, the schematic diagram of the steer-by-wire system is as follows: Figure 1 As shown. The fault-tolerant control method for intelligent vehicle steering systems based on event-triggered communication mechanism, its detailed implementation process includes:

[0068] S1. Establish a mathematical model of the steer-by-wire system that takes into account network communication constraints, model uncertainties, and actuator failures;

[0069] (1)

[0070] In the formula: For the system's state variables, and These are the system's input and output, respectively. This indicates a nonlinearity including the restoring torque and the frictional torque. To control the gain, This indicates an external time-varying disturbance. and These represent the steering angle and angular velocity of the front wheel, respectively.

[0071] Furthermore, the steering motor is highly sensitive to electromagnetic interference and electronic malfunctions. When the actuator fails, the steering motor cannot generate sufficient torque to drive the front wheels to track its reference steering angle according to the designed control signal, which may cause the autonomous driving system to deviate from its reference path. Therefore, the controller design needs to consider the impact of actuator failure. Considering the actuator failure phenomenon, the input of the steer-by-wire system can be expressed as:

[0072] (2)

[0073] In the formula: and These represent partial failure and floating failure, respectively. When and This indicates that there is no actuator failure. Combining equations (1) and (2), the steer-by-wire system model considering actuator failure and other phenomena can be expressed as:

[0074] (3)

[0075] In the formula: .

[0076] In this example, the model parameters of the selected steer-by-wire system are as follows:

[0077] Friction torque of steer-by-wire system and the restoring torque The simulation models selected are as follows:

[0078] (4)

[0079] (5)

[0080] in, and This can be obtained through the following two-degree-of-freedom model, namely...

[0081] (6)

[0082] The parameters for the steer-by-wire system are selected as follows:

[0083] , , , , , , , , , , , The initial values ​​of the state variables are chosen as follows: , The simulation step size was set to 0.001s. The actuator fault information and external disturbances selected in the simulation were:

[0084] Table 1. Selected actuator fault information and external disturbances in the simulation.

[0085]

[0086] S2. An interval-type II fuzzy logic system is used to approximate and model the uncertain nonlinear terms in the steer-by-wire system online.

[0087] For any continuous function In close proximity If so, then there must exist an optimal parameter vector and a zero-order interval type-II fuzzy logic system that satisfies:

[0088] (7)

[0089] In the formula: Let be the membership function of the type-two fuzzy logic system, where and These are the left and right limits of a type-II fuzzy logic system, respectively. This is the upper bound of the approximation error of the type II fuzzy logic system.

[0090] S3. Design an event-triggered mechanism based on the event-triggered communication principle to save network communication resources;

[0091] Considering the limitations of wireless network transmission and communication resources, an event-triggered mechanism is adopted for data communication. The designed event-triggered communication mechanism is as follows:

[0092] (8)

[0093] In the formula: , , , , ,

[0094] , , , , and For the design of positive constants, , , , , and .

[0095] S4. Construct the Lyapunov function;

[0096] In step S4, the following Lyapunov function is constructed:

[0097] (9)

[0098] S5. A fault-tolerant controller based on event-triggered communication mechanism was designed;

[0099] In step S5, the sliding surface is designed as follows:

[0100] (10)

[0101] The designed controller can be represented as:

[0102] (11)

[0103] In the formula: and , and Unknown parameters , and The estimation results, i.e.

[0104] (12)

[0105] (13)

[0106] (14)

[0107] And the function , and They are defined as follows:

[0108] (15)

[0109] (16)

[0110] (17)

[0111] In the formula: and For the design of positive constants, and . Let Lipschitz be the basis function of the fuzzy logic system. . and Let be the parameter vector and basis vector of the type-II fuzzy logic system, respectively. and Let represent the left basis vector and right basis vector of the type-II fuzzy logic system, respectively.

[0112] design , and The adaptive law is:

[0113] (18)

[0114] (19)

[0115] (20)

[0116] In the formula: , , , , and All are positive constants of the design. , , , , , and .

[0117] Parameter vector of fuzzy logic system The adaptive rate design is as follows:

[0118] (twenty one)

[0119] In the formula: , and All are positive constants of the design. , and .

[0120] Figure 2 This is a schematic diagram illustrating the tracking performance of the front wheel steering angle of the invented steer-by-wire system. Figure 3 This is a schematic diagram illustrating the tracking error of the front wheel steering angle in the steer-by-wire system of the invention. Figure 2 and Figure 3 It can be seen that satisfactory tracking results can still be obtained even in the case of actuator failure and event-triggered communication mechanism. Figure 4 The diagram shows a comparison between the system input and the designed control quantity of the proposed steer-by-wire system. It can be seen that the designed control quantity can adjust the controller output according to actuator fault information and external disturbances, and the control quantity is only transmitted at the moment the event is triggered. Figure 5 The diagram shows the event trigger count of the invented steer-by-wire system. It can be seen that the number of event triggers is limited during the 100s simulation, and it saves 2 / 5 of the communication volume compared to time-triggered events. Figure 6 For the invention A schematic diagram of the vector estimation results. Figure 7 Unknown parameters in the steer-by-wire system of the invention , A schematic diagram of the estimation results, from Figure 6 and Figure 7 The parameter vector of the fuzzy system can be seen and unknown parameters , It can be adjusted in real time.

[0121] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. An event-triggered communication mechanism-based fault-tolerant control method for a smart vehicle steering system, characterized in that, It includes the following steps: S1. Establish a mathematical model of the steer-by-wire system that takes into account network communication constraints, model uncertainties, and actuator failures; S2. An interval-type II fuzzy logic system is used to approximate and model the uncertain nonlinear terms in the steer-by-wire system online. S3. Design an event-triggered mechanism based on the event-triggered communication principle to save network communication resources; S4. Construct the Lyapunov function; S5. A fault-tolerant controller based on event-triggered communication mechanism was designed.

2. The fault-tolerant control method for intelligent vehicle steering system based on event-triggered communication mechanism according to claim 1, characterized in that: In step S1, the mathematical model of the steer-by-wire system is: (1) In the formula: For the system's state variables, and These are the system's input and output, respectively. This indicates a nonlinearity including the restoring torque and the frictional torque. To control the gain, This indicates an external time-varying disturbance. and These represent the steering angle and angular velocity of the front wheels, respectively. Furthermore, the steering motor is highly sensitive to electromagnetic interference and electronic malfunctions. When the actuator fails, the steering motor cannot generate sufficient torque to drive the front wheels to track its reference steering angle according to the designed control signal, which may cause the autonomous driving system to deviate from its reference path. Therefore, the controller design needs to consider the impact of actuator failure. Considering the actuator failure phenomenon, the input of the steer-by-wire system can be expressed as: (2) In the formula: and These represent partial failure and floating failure, respectively; when and This indicates that there is no actuator failure; combining equations (1) and (2), the steer-by-wire system model considering actuator failure and other phenomena can be expressed as: (3) In the formulae: .

3. The fault-tolerant control method for intelligent vehicle steering system based on event-triggered communication mechanism according to claim 1, characterized in that: In step S2, For any continuous function On a compact set There must exist an optimal parameter vector and zero-order interval bivalent fuzzy logic system, which satisfies: (4) wherein: is a membership function of the bi-type fuzzy logic system, wherein and are left and right limits of the bi-type fuzzy logic system, respectively, is an upper bound of approximation error of the bi-type fuzzy logic system.

4. The fault-tolerant control method for intelligent vehicle steering system based on event-triggered communication mechanism according to claim 1, characterized in that: In step S3, Considering the limitations of wireless network transmission and communication resources, an event-triggered mechanism is adopted for data communication. The designed event-triggered communication mechanism is as follows: (5) In the formulae: , , , , , , , , , and For the design of positive constants.

5. The fault-tolerant control method for intelligent vehicle steering system based on event-triggered communication mechanism according to claim 1, characterized in that: In step S4, Construct the following Lyapunov function: (6)。 6. The fault-tolerant control method for intelligent vehicle steering system based on event-triggered communication mechanism according to claim 1, characterized in that: In step S5, The sliding surface is designed as follows: (7) The designed controller can be represented as: (8) wherein: and , and are the estimates of the unknown parameters , and respectively, i.e. (9) (10) (11) and the functions , and are defined as: (12) (13) (14) In the formula: and For the design of positive constants, The Lipschitz constant is the basis function of the fuzzy logic system. and Let be the parameter vector and basis vector of the type-II fuzzy logic system, respectively. and Let represent the left basis vector and right basis vector of the type-II fuzzy logic system, respectively; The adaptive law for the design of , and is given by (15) (16) (17) In the formula: , , , , and All are positive constants in the design; parameter vectors of the fuzzy logic system. The adaptive rate design is as follows: (18) wherein: , and are all designed normal numbers.

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