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CN-121995965-A - Nonlinear non-strict feedback multi-intelligent system fixed time fault-tolerant control system

CN121995965ACN 121995965 ACN121995965 ACN 121995965ACN-121995965-A

Abstract

The application discloses a nonlinear non-strict feedback multi-intelligent system fixed time fault-tolerant control system, which belongs to the technical field of intelligent system cooperative control, and comprises a multi-intelligent system model, a coordinate transformation and command filtering module, an error compensation signal module, a step recurrence module, a neural network module, an adaptive law module and an adaptive fault-tolerant controller module, wherein the application constructs an error compensation signal by the coordinate transformation and command filtering module, the method is used for compensating the influence of sensor faults on the consistency of the system, ensuring that the system converges in a fixed time, enabling the system to realize output consistency tracking in a time upper bound which is predetermined and irrelevant to an initial state when faults such as sensor deviation and gain occur, obviously improving the reliability and the safety of the system and solving the technical problems of state feedback information distortion and system cooperation precision and stability reduction caused by the sensor faults of an intelligent agent in the prior art.

Inventors

  • DONG GUOWEI
  • Xu Peitong
  • LI KEWEN

Assignees

  • 辽宁工业大学

Dates

Publication Date
20260508
Application Date
20260119

Claims (9)

  1. 1. A nonlinear non-critical feedback multi-agent system fixed time fault tolerant control system, characterized by being applied to a nonlinear non-critical feedback multi-agent system comprising a plurality of agents, comprising: a multi-agent system model (10) for receiving an output of the agent and outputting status information; A coordinate transformation and command filtering module (20) for receiving the state information and outputting a virtual control signal and an output signal generated by the virtual control signal through a filter; An error compensation signal module (30) for receiving the virtual control signal and the output signal and outputting an error compensation signal; A step recurrence module (40) for receiving the error compensation signal and outputting a signal reflecting the rate of change of the system stability; a neural network module (50) for receiving the status information and outputting a corrected error variable; The self-adaptive law module (60) is used for receiving the state information, the output of the intelligent agent, the corrected error variable and a signal reflecting the system stability change rate, and outputting a parameter self-adaptive law by combining an ideal weight value of a radial basis function neural network; And the adaptive fault-tolerant controller module (70) is used for receiving the parameter adaptive law and a signal reflecting the change rate of the system stability and acquiring the output of the intelligent agent.
  2. 2. The nonlinear non-critical feedback multi-intelligent system fixed time fault tolerant control system of claim 1, wherein the characterization formula of the multi-intelligent system model (10) comprises: ; ; ; Wherein, the A first derivative of an h state variable that is an i-th agent in the multi-agent system; H+1th state variable of the ith agent in the multi-agent system; The state vector of the ith intelligent agent in the multi-intelligent agent system; An unknown smooth nonlinear function of an h state variable of an i-th agent in the multi-agent system; Time is; external disturbances for an h state variable of an i-th agent in a multi-agent system; a first derivative of an nth state variable that is an ith agent in the multi-agent system; The output of the ith intelligent agent in the multi-intelligent agent system; An unknown smooth nonlinear function of an nth state variable of an ith agent in the multi-agent system; external disturbances for an nth state variable of an ith agent in a multi-agent system; is the input of the ith agent in the multi-agent system; 1 st state variable for the i-th agent in a multi-agent system Is a fault in the offset gain of the sensor of (a).
  3. 3. The nonlinear non-critical feedback multiple intelligent system fixed time fault tolerant control system of claim 2, wherein said characterization formula for offset gain failure comprises: ; Wherein, the Sensor fault parameters for an ith agent in a multi-agent system; a1 st state variable for an i-th agent in a multi-agent system; unknown smooth perturbations for the ith agent in the multi-agent system.
  4. 4. The non-linear non-critical feedback multi-intelligent system fixed time fault tolerant control system of claim 1, wherein the characterization formula of the coordinate transformation and command filtering module (20) comprises: ; ; ; ; ; Wherein, the As a dimension of the state of the system, , ; Index of the agent in the intelligent system model for nonlinear and non-strict feedback; is a tracking error; a neighbor agent set for an ith agent in the multi-agent system; the ith agent in the neighbor agent set An agent; Is an adjacency matrix element of a communication topology; Input for the ith agent; adjoining agent that is the ith agent Is input to the computer; The connection weight between the ith agent and the leader; Is a leader signal; The ith agent Error variables; is the ith agent in the multi-agent system A plurality of state variables; The ith agent The step virtual control signal is an output signal generated by a command filter; The ith agent Step 3, virtual control signals; Is a design parameter; The ith agent Correction values of the individual error variables; The ith agent Error variables; The ith agent Error compensation signals for the respective error variables.
  5. 5. The nonlinear non-critical feedback multiple intelligent system fixed time fault tolerant control system of claim 1, wherein the characterization formula of the error compensation signal module (30) comprises: ; ; ; ; Wherein, the The ith agent A compensation signal for the respective error variable, , As the number of error variables, Is 2 to A number therebetween; , As an adjacency matrix element of the communication topology, For a neighbor agent set of an ith agent in a multi-agent system, The ith agent in the neighbor agent set The intelligent agent is used for controlling the intelligent agent, The connection weight between the ith agent and the leader; The ith agent The step virtual control signal is an output signal generated by a command filter; The ith agent Step 3, virtual control signals; The ith agent The step virtual control signal is an output signal generated by a command filter; The ith agent Step 3, virtual control signals; positive design parameters for the p-th error variable of the i-th agent; The ith agent The step virtual control signal is an output signal generated by a command filter; The ith agent Step virtual control signals.
  6. 6. The non-linear non-critical feedback multi-intelligent system fixed time fault tolerant control system of claim 1, wherein the characterization formula of the anti-step recurrence module (40) comprises: ; wherein, the A signal reflecting the rate of change of system stability; is the number of agents; index of the agent in the intelligent system model for nonlinear and non-strict feedback; Is the number of error variables; order number for error variable; is a positive design parameter; A kth error variable that is an ith agent; is a positive design parameter; is a positive design parameter; is a positive design parameter; Is an estimation error; is a positive design parameter; is a positive design parameter; An nth error variable that is an ith agent; The output of the ith intelligent agent in the multi-intelligent agent system; An unknown smooth nonlinear function of an nth state variable of an ith agent in the multi-agent system; The state vector of the ith intelligent agent in the multi-intelligent agent system; The ith agent A compensation signal for each error variable; The ith agent A compensation signal for each error variable; positive design parameters for the nth error variable of the ith agent; The ith agent Step virtual control signals are generated by commanding a first derivative of the output signal generated by the filter; external disturbances for an nth state variable of an ith agent in a multi-agent system; Time is; N-1 error variable for the ith agent; is a positive design parameter; is a positive design parameter; In order to estimate the error of the signal, , Is adaptive to parameters Is a function of the estimated value of (2); is a positive design parameter; , Positive design parameters for the n-1 th error variable of the i-th agent, Is a constant; is a positive design parameter; In order to estimate the error of the signal, Is adaptive to parameters Is used for the estimation of the estimated value of (a).
  7. 7. The nonlinear non-critical feedback multiple intelligent system fixed time fault tolerant control system of claim 1, wherein the characterization formula of the neural network module (50) comprises: ; ; ; Wherein, the Index of the agent in the intelligent system model for nonlinear and non-strict feedback; As a dimension of the state of the system, , Is a nonlinear dynamic function; is the corrected error variable; Is an external disturbance; Is a sensor fault parameter; Is a state variable Is the first derivative of (a); Is smooth disturbance; a neighbor agent set for an ith agent in the multi-agent system; the ith agent in the neighbor agent set An agent; Is an adjacency matrix element of a communication topology; ideal weights for radial basis function neural networks; is a basis function; As a smooth nonlinear function; is an approximation error; To compensate the signal; Is a state vector.
  8. 8. The non-linear non-critical feedback multiple intelligent system fixed time fault tolerant control system of claim 1, wherein the characterization formula of the adaptive law module (60) comprises: ; ; Wherein, the As a dimension of the state of the system, ; Index of the agent in the intelligent system model for nonlinear and non-strict feedback; And Is a parameter self-adaptive law; Is that Is used for the estimation of (a), , Ideal weights for radial basis function neural networks; Is that Is used for the estimation of (a), , Ideal weights for radial basis function neural networks; 、 、 、 、 、 、 、 And Is a positive design parameter; is the corrected error variable; , , And Is a basis function.
  9. 9. The non-linear non-critical feedback multi-intelligent system fixed time fault tolerant control system of claim 1, wherein the characterization formula of the adaptive fault tolerant controller module (70) comprises: ; Wherein, the The output of the ith intelligent agent in the multi-intelligent agent system; The ith agent Error variables; The ith agent Error variables; is a positive design parameter; is the corrected error variable; Is that Is used for the estimation of (a), , Ideal weights for radial basis function neural networks; , is a basis function; The ith agent Step 3, virtual control signals; is a positive design parameter; is a positive design parameter; is a positive design parameter; Is a positive design parameter.

Description

Nonlinear non-strict feedback multi-intelligent system fixed time fault-tolerant control system Technical Field The application belongs to the technical field of nonlinear multi-intelligent system (MASs) cooperative control, and particularly relates to a nonlinear non-strict feedback multi-intelligent system fixed time fault-tolerant control system. Background With the wide application of multi-agent systems in the fields of unmanned aerial vehicle formation, intelligent transportation, distributed sensing networks and the like, the problems of system reliability and safety are increasingly prominent. In actual operation, the intelligent body sensor is easy to deviate, gain change and other faults due to environmental interference, aging or hardware damage, so that state feedback information is distorted, and the cooperative precision and stability of the system are seriously affected. The prior art faces the following challenges in dealing with such problems: First, conventional fault-tolerant control methods have focused on actuator faults, and relatively inadequate research on sensor faults. Sensor failure can directly disrupt the state feedback loop and in multi-agent systems, failure information can diffuse through the communication network, resulting in overall degradation of the collaborative performance. The existing method is more than assumed to have single or known fault modes, and is difficult to adapt to the faults of the time-varying and compound sensors. Second, most of the existing control strategies are based on strict feedback system structural design, and are difficult to directly popularize to non-strict feedback systems. Nonlinear items of all subsystems in the non-strict feedback structure are mutually coupled, so that the nonlinear items are difficult to decouple step by step in the design of the controller, and the complexity of system analysis and the difficulty of the design of the controller are increased. Third, although the finite time control method can achieve rapid convergence, the convergence time depends on the initial state of the system, and in practical application, accurate initial information is often difficult to obtain in advance, so that the practicability is limited. In addition, the traditional design based on the back-stepping method needs to repeatedly derive a virtual control law, so that the problems of 'calculation explosion' and 'singularity' are easily caused, and the instantaneity and the engineering feasibility of the algorithm are restricted. Disclosure of Invention The application aims to develop a nonlinear non-strict feedback multi-intelligent system fixed time fault tolerance control system, and aims to solve the technical problems of state feedback information distortion and system coordination precision and stability reduction caused by sensor faults of an intelligent body in the prior art. The application provides a nonlinear non-strict feedback multi-agent system fixed time fault-tolerant control system, which is applied to a nonlinear non-strict feedback multi-agent system comprising a plurality of agents, and comprises: the multi-intelligent system model is used for receiving the output of the intelligent body and outputting state information; the coordinate transformation and command filtering module is used for receiving the state information and outputting a virtual control signal and an output signal generated by the virtual control signal through a filter; The error compensation signal module is used for receiving the virtual control signal and the output signal and outputting an error compensation signal; the step recurrence module is used for receiving the error compensation signal and outputting a signal reflecting the change rate of the system stability; the neural network module is used for receiving the state information and outputting a corrected error variable; The self-adaptive law module is used for receiving the state information, the output of the intelligent agent, the corrected error variable and a signal reflecting the system stability change rate, and outputting a parameter self-adaptive law by combining an ideal weight value of a radial basis neural network; and the self-adaptive fault-tolerant controller module is used for receiving the parameter self-adaptive law and a signal reflecting the change rate of the system stability and obtaining the output of the intelligent body. In some embodiments, the characterization formula of the multi-agent system model includes: ; ; ; Wherein, the A first derivative of an h state variable that is an i-th agent in the multi-agent system; H+1th state variable of the ith agent in the multi-agent system; The state vector of the ith intelligent agent in the multi-intelligent agent system; An unknown smooth nonlinear function of an h state variable of an i-th agent in the multi-agent system; Time is; external disturbances for an h state variable of an i-th agent in a multi-agent system