CN-121978901-A - Fuzzy multi-unmanned aerial vehicle fault-tolerant formation control method based on reduced-order intermediate observer

CN121978901ACN 121978901 ACN121978901 ACN 121978901ACN-121978901-A

Abstract

The invention provides a fuzzy multi-unmanned aerial vehicle fault-tolerant formation control method based on a reduced-order intermediate observer, which comprises the steps of constructing a multi-unmanned aerial vehicle system model containing a follower and a leader, wherein the follower adopts a T-S fuzzy model to represent dynamics, the system state output control input executor faults and parameter uncertainty are included, the leader model does not contain faults and uncertainty, defining a time-varying formation tracking condition to be that the follower state is equal to expected formation offset in a leader convex hull, deducing a follower reduced-order dynamic model, designing a reduced-order intermediate observer to reconstruct system state, fault and uncertainty information, combining a distributed control protocol, fusing state error feedback, fault compensation and self-adaptive gain adjustment, and ensuring the robustness of time-varying formation tracking control. Finally, the invention obviously improves the fault tolerance capability and formation stability of the multi-unmanned aerial vehicle in a complex scene, and provides reliable guarantee for successful execution of tasks.

Inventors

SHI JIANTAO
ZHAO LEI
Xing Shuangqing
CHEN CHUANG
YUE DONGDONG
BAO DAN
QIAN MOSHU
FENG LIHANG

Assignees

南京工业大学

Dates

Publication Date: 20260505
Application Date: 20260126

Claims (8)

1. A fuzzy multi-unmanned aerial vehicle fault-tolerant formation control method based on a reduced-order intermediate observer is characterized by comprising the following steps: S1, constructing a multi-unmanned aerial vehicle system model containing a follower and a leader, wherein the follower adopts a T-S fuzzy model to represent dynamics, and the system state, the output, the control input, the actuator fault and the parameter uncertainty are included, and the leader model does not contain the fault and the uncertainty; S2, defining a time-varying formation tracking condition that the follower state is equal to the expected formation offset in the leader convex hull, deriving a follower reduced-order dynamic model, and defining virtual output through a left inverse matrix and a zero-space orthogonal basis to form a state fault uncertainty derivative augmentation reduced-order system; S3, designing a reduced intermediate observer, defining intermediate variables to eliminate virtual output derivatives, building an observer dynamic structure, building an observer equation based on the intermediate variables and measurable output, and solving the state fault uncertainty of the observation gain reconstruction system through a linear matrix inequality; and S4, designing a distributed formation control protocol based on the reconstruction information, defining a formation consistency error between the followers and a tracking error between the followers and a leader, integrating the total formation error, controlling input fusion estimation state error feedback fault uncertainty, compensating self-adaptive gain, deriving a gain update law through a Lyapunov function, and fusing T-S fuzzy weights to form final input.
2. The fuzzy multi-unmanned aerial vehicle fault-tolerant formation control method based on the reduced-order intermediate observer according to claim 1, wherein in the step S1, a system matrix and fuzzy weights are determined according to the fuzzy rule of each T-S fuzzy model, wherein the fuzzy weights meet normalization and sum conditions, and the system matrix is controllable and observable; Constructing a Laplace matrix to represent the system topology of the multiple unmanned aerial vehicles, wherein the follower is divided into an information complete unmanned aerial vehicle and an information incomplete unmanned aerial vehicle, and each information incomplete unmanned aerial vehicle and the information complete unmanned aerial vehicle have a direct undirected path; the fault signal and the parameter uncertainty are micro-borderable signals, and the derivative bordering condition is satisfied.
3. The fuzzy multi-unmanned aerial vehicle fault-tolerant formation control method based on the reduced-order intermediate observer according to claim 2, wherein in step S2, the output matrix in the dynamic model of the follower unmanned aerial vehicle is full-rank, and a matrix and a zero-space orthogonal basis which meet the left zero-space condition are determined; defining a virtual output and combining a reduced order model to obtain a reduced order form of dynamic conversion of the initial follower; And defining the augmentation state variable as the uncertainty derivative of the system state fault to form an ith follower augmentation and reduction system.
4. The fuzzy multi-unmanned aerial vehicle fault-tolerant formation control method based on the reduced-order intermediate observer according to claim 3, wherein the specific process of constructing the intermediate observer in the step S3 is as follows: a. Based on a reduced-order augmentation system, taking a derivative of fault and parameter uncertainty into an augmentation state based on a reduced-order dynamic model derived from a system model to form a 'state-fault-uncertainty' integrated augmentation system, so that an observer can estimate three types of core information simultaneously; b. Constructing intermediate variables, namely eliminating the non-measurable output derivative, converting the 'non-measurable output derivative' in the augmentation system into a 'derivable item containing fault/uncertainty information', and avoiding introducing non-measurable signals into an observer; c. and constructing a dynamic structure of the observer, namely associating an augmentation state estimated value and an uncertainty auxiliary estimated value, and ensuring the continuity of uncertainty estimation.
5. The fuzzy multi-unmanned aerial vehicle fault-tolerant formation control method based on the reduced intermediate observer according to claim 1, wherein in step S3, an intermediate variable is defined as the product of the observer output estimated value and a selected scalar, and the first derivative of the intermediate variable is expressed by combining an intermediate coefficient matrix under a fuzzy rule; Constructing an observer dynamic equation to eliminate virtual output derivative, and introducing new variable association augmentation state estimation and uncertainty auxiliary estimation; and defining an observation error to obtain a compact form of an observer error system, wherein the observer gain meets the error asymptotic convergence under the fuzzy rule combination.
6. The fuzzy multi-unmanned aerial vehicle fault-tolerant formation control method based on the reduced intermediate observer according to claim 1, wherein in step S4, a formation consistency error is defined as a product of an adjacency matrix element and a desired formation offset vector, and a tracking error is defined as a product of a follower leader communication adjacency element; integrating the total formation error, and calculating negative feedback by an error feedback term based on the estimated state; The compensation term counteracts the reconstruction fault uncertainty by using the fault distribution matrix pseudo-inverse, and the self-adaptive gain is updated along with the integral of the total formation error.
7. The fuzzy multi-unmanned aerial vehicle fault-tolerant formation control method based on the reduced intermediate observer, according to claim 1, wherein in step S4, a lyapunov function containing gain quadratic terms is selected, and the derived derivative is positive and line consistency based on the real part of the Laplace matrix eigenvalue; and defining diagonal elements of the diagonal matrix as characteristic values of the topology matrix, and converting the characteristic values into a symmetrical positive definite matrix form to ensure convergence of the total formation error.
8. The fuzzy multi-unmanned aerial vehicle fault-tolerant formation control method based on the reduced intermediate observer, according to claim 1, wherein in step S4, a T-S fuzzy weight coefficient is finally input and fused, and a system dynamic equation is converted into a follower integration form; when the observed error converges to the total formation error approaches zero, the follower state is achieved to be equal to the expected formation offset within the leader's convex hull.

Description

Fuzzy multi-unmanned aerial vehicle fault-tolerant formation control method based on reduced-order intermediate observer Technical Field The invention relates to the technical field of intelligent control, in particular to a fuzzy multi-unmanned aerial vehicle fault-tolerant formation control method based on a reduced-order intermediate observer. Background In view of the complexity of the system architecture and network interconnection, multiple unmanned aerial vehicle systems are extremely prone to failure. In a multi-unmanned system, information interaction between unmanned aerial vehicles can lead to the rapid propagation of faults of one unmanned aerial vehicle to the whole network, reducing system performance and even leading to serious consequences of complete breakdown of the system. In addition, aging, wear, and external disturbances and uncertainties caused by diverse operations can further impact the stability and reliability of the system. Existing multi-unmanned aerial vehicle system control researches are mostly focused on a single-leader scene, and in practical application, the situation of multiple leaders is more common. Meanwhile, although the Takagi-Sugeno (T-S) fuzzy model is a powerful tool for processing nonlinear systems, its research application in the field of multi-unmanned aerial vehicle systems is not yet sufficient. In terms of system safety and reliability, conventional fault estimation and fault tolerance control methods (such as an unknown input observer, an adaptive observer and the like) generally depend on observer matching conditions, which are often difficult to meet in actual engineering, so that the application range of the fault estimation and fault tolerance control methods is limited. Although the intermediate variable observer method in recent years can avoid the severe conditions, most of the existing designs are full-order observers, the calculation complexity is high, and the research of using the observer to solve the formation control problem of the multi-unmanned aerial vehicle system is still lacking. Disclosure of Invention Aiming at the defects of the prior art, the problem of formation control of a fuzzy multi-unmanned aerial vehicle system influenced by actuator faults and parameter uncertainty is solved. When the multiple unmanned aerial vehicle system described by the T-S fuzzy model simultaneously has an actuator fault, parameter uncertainty and a plurality of leaders, an effective control strategy is designed to ensure that the follower unmanned aerial vehicle group can maintain a desired time-varying formation and can be converged into a convex hull formed by a plurality of leader states. Therefore, the fuzzy multi-unmanned aerial vehicle fault-tolerant formation control method based on the reduced-order intermediate variable observer is provided, unmanned aerial vehicle states, actuator faults and parameter uncertainties are reconstructed at the same time, and formation control with high precision and high reliability is realized under the condition that all system states cannot be directly obtained and faults and uncertainties occur in real time. The method comprises the following steps of constructing a multi-unmanned aerial vehicle system model comprising a follower and a leader, wherein the follower adopts a T-S fuzzy model to represent dynamics and comprises a system state output control input executor fault and parameter uncertainty, the leader model does not contain faults and uncertainties, defining time-varying formation tracking conditions to enable the follower state to be equal to expected formation offset in a leader convex hull, deducing a follower reduced-order dynamic model, defining virtual output through a left inverse matrix and a zero space orthogonal basis to form a state fault uncertainty derivative augmentation order reduction system, designing a reduced-order intermediate observer, defining an intermediate variable elimination virtual output derivative, constructing the observer to enable the control input measurable output intermediate variable to serve as an input augmentation state estimation uncertainty auxiliary estimation to serve as a core, solving observation gain through a linear matrix inequality to reconstruct system state fault uncertainty, defining a distributed formation control protocol based on reconstruction information, defining a follower-to-follower formation consistency error follower leader tracking error integration error total formation error, controlling input fusion estimation state error feedback uncertainty compensation self-adaption gain function, and finally deducing an input fuzzy gain fusion function through a Lyapunov weight update. The T-S fuzzy model comprises a system matrix and fuzzy weights, wherein the fuzzy weights meet normalization and harmony conditions, the system matrix is controllable and observable, a Laplace matrix is constructed to represent the system topology of the multiple