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This paper mainly investigates the fault-tolerant consensus problem for leader-following heterogeneous multi-agent systems with actuator faults. The model of multi-agent systems is reconstructed and the lumped faults, including external disturbances and actuator faults, are constructed. A new adaptive super-twisting sliding mode observer (ASTSMO) is constructed to estimate the undetectable lumped faults. The time-varying gain is introduced to solve the initial error problem and peak value problem. Then, based on the estimated results, an integral terminal sliding mode controller (ITSMC) is designed by integrating the fixed-time stability theory, which can effectively eliminate the nonlinear term and lumped faults. Finally, simulations are provided to illustrate the effectiveness and benefits of the proposed theoretical results. [ABSTRACT FROM AUTHOR]

This paper introduces a novel intelligent sliding mode predictive fault-tolerant control method based on the Dynamic Information Exchange Coyote Optimization Algorithm (DIECOA), which is applied to a quad-rotor UAV system with multi-delay and sensor fault. First, the system nonlinearity and sensor fault are dealt with by means of interpolation transformation and system state expansion, and an equivalent system is obtained. Second, the quasi-integral sliding mode surface is used to construct the prediction model so that the initial state of the system is located on the sliding mode surface, and the global robustness is guaranteed. Third, this paper introduces an improved fault and disturbance compensation term, which effectively weakens the adverse effect of time delays and enhances the FTC performance of the system. Fourth, the Dynamic Information Exchange (DIE) strategy is designed to further improve the coyote individual replacement mechanism and speeds up the solution and convergence speed of the method in this paper. Finally, the simulation is carried out on the fault-tolerant simulation platform of the quad-rotor Unmanned Aerial Vehicle (UAV), and the results show the rationality of the method. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive sliding mode fault-tolerant control scheme based on prescribed performance control and neural networks is developed for an Unmanned Aerial Vehicle (UAV) quadrotor carrying a load to deal with actuator faults. First, a nonsingular fast terminal sliding mode (NFTSM) control strategy is presented. In virtue of the proposed strategy, fast convergence and high robustness can be guaranteed without stimulating chattering. Secondly, to obtain correct fault magnitudes and compensate the failures actively, a radial basis function neural network-based fault estimation scheme is proposed. By combining the proposed fault estimation strategy and the NFTSM controller, an active fault-tolerant control algorithm is established. Then, the uncertainties caused by load variation are explicitly considered and compensated by the presented adaptive laws. Moreover, by synthesizing the proposed sliding mode control and prescribed performance control (PPC), an output error transformation is defined to deal with state constraints and provide better tracking performance. From the Lyapunov stability analysis, the overall system is proven to be uniformly asymptotically stable. Finally, numerical simulation based on a quadrotor helicopter is carried out to validate the effectiveness and superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]