Fault tolerant consensus of multiple nonholonomic chained‐form systems with actuator and communication faults

Abstract

This article investigates the fault tolerant consensus problem of multiple nonholonomic chained‐form systems with both actuator and communication faults. The systems are enabled to follow a reference trajectory and realize practical consensus, by applying distributed reference state observers and robust adaptive fault tolerant control (FTC) strategies. The proposed state observers are robust to communication faults and can estimate the actual reference trajectory for each system. Based on such an observer, a robust adaptive FTC scheme is further provided to address actuator faults. A sine function is injected into the FTC law to relax the persistent excitation condition to avoid uncontrollability of the two‐input chained‐form system when one of reference control input tends to zero. Simulation result of wheeled mobile robots shows the effectiveness of proposed FTC methods.

Fully distributed secure consensualization for leaderless nonlinear multiagent systems with communication link attacks

“…Yang et al 31 designed a cascaded estimator network, which can effectively estimate the practical leader dynamics and state, and an adaptive distributed control protocol was given to achieve secure control for heterogeneous MASs under the condition that weights of the directed graph are time‐varying. For multiple nonholonomic chained‐form systems and microgrid control systems, the SCC analysis and the design problem with the communication fault or attack were investigated in References 32‐34. Note that the appropriate leader state observer for the followers in References 30‐34 were designed, which adaptively adjust the weights according to the obtained neighbor states to achieve secure tracking control with the uncertain time‐varying weights.…”

“…For multiple nonholonomic chained‐form systems and microgrid control systems, the SCC analysis and the design problem with the communication fault or attack were investigated in References 32‐34. Note that the appropriate leader state observer for the followers in References 30‐34 were designed, which adaptively adjust the weights according to the obtained neighbor states to achieve secure tracking control with the uncertain time‐varying weights. In References 30‐34, the secure consensus problem was transformed into a stabilization problem by constructing the tracking error variables, which adopted the nonsingular M‐matrix theory for the stability analysis directly.…”

“…Note that the appropriate leader state observer for the followers in References 30‐34 were designed, which adaptively adjust the weights according to the obtained neighbor states to achieve secure tracking control with the uncertain time‐varying weights. In References 30‐34, the secure consensus problem was transformed into a stabilization problem by constructing the tracking error variables, which adopted the nonsingular M‐matrix theory for the stability analysis directly. However, for leaderless MASs, since there exists no a specified leader, the associated Laplacian matrix is singular, and the nonsingular M‐matrix theory cannot be applied to solve the stabilization problem of leaderless MASs directly.…”

“…Based on graph theory, the scale relationship between the quadratic function associated with the Laplacian matrix and the local neighborhood state error is determined, which shows that the scale factor is larger than zero. Second, the previous research works [30][31][32][33][34] mostly focus on the SCC problem for leader-follower MASs, where the consensus problem was transformed into a stabilization problem and SCC was achieved by the nonsingular M-matrix theory. However, for leaderless MASs, the associated Laplacian matrix is singular, and the nonsingular M-matrix theory is no longer valid.…”

Reconfiguration Control Design of UAV against Actuator Faults Based on Control Allocation Method

“…There are many fault diagnosis and fault-tolerant control methods for sensors and actuators of flight control system. As early as 1990, as discussed by Professor Frank [4], international authority on fault diagnosis of control system divided fault diagnosis methods into three categories: analytical model-based methods, signal process-based methods, and knowledge-based methods, which have been accepted by many scholars [5,6].…”