Adaptive Neural Quantized Control of MIMO Nonlinear Systems Under Actuation Faults and Time-Varying Output Constraints

In this article, a neural network (NN)-based robust adaptive fault-tolerant control (FTC) algorithm is proposed for a class of multi-input multi-output (MIMO) strict-feedback nonlinear systems with input quantization and actuation faults as well as asymmetric yet time-varying output constraints. By introducing a key nonlinear decomposition for quantized input, the developed control scheme does not require the detailed information of quantization parameters. By imposing a reasonable condition on the gain matrix under actuation faults, together with the inherent approximation capability of NN, the difficulty of FTC design caused by anomaly actuation can be handled gracefully, and the normally used yet rigorous assumption on control gain matrix in most existing results is significantly relaxed. Furthermore, a brand new barrier function is constructed to handle the asymmetric yet time-varying output constraints such that the analysis and design are extremely simplified compared with the traditional barrier Lyapunov function (BLF)-based methods. NNs are used to approximate the unknown nonlinear continuous functions. The stability of the closed-loop system is analyzed by using the Lyapunov method and is verified through a simulation example.