Adaptive neural fault-tolerant tracking control for state-constrained systems subject to multiple power drift faults

The adaptive neural fault-tolerant control (FTC) for state-constrained systems containing novel sensor and actuator faults is investigated in this article. This work considers not only common actuator bias and gain faults, but also a novel type of fault caused by the power drift of system, namely the power drift faults. In addition, sensor faults in the form of unknown power drifts are also considered in this work. To compensate the impact of multiple power drift faults, a novel controller is established by introducing new auxiliary signals. The radial basis function neural networks (RBFNNs) are employed to resolve some uncertain functions and reduce the computational complexity. By combining the backstepping approach and barrier Lyapunov functions, a new adaptive FTC algorithm is developed. Based the presented controller, all signals in this system remain semi-globally bounded and the control error is guided to a small range near zero. Simultaneously, system constraints are not violated. At last, a simulation experiment is performed to confirm the validity and feasibility of the developed algorithm.