Adaptive neural network fault-tolerant control for uncertain non-strict feedback nonlinear system with actuator faults and state constraints

This article studies a neural network (NN)-based adaptive fault-tolerant control (FTC) scheme for uncertain non-strict feedback systems with time-varying state constraints and actuator faults. The introduction of asymmetric Barrier-Lyapunov function (BLF) makes controller design more difficult due to the emergence of actuator faults and state constraints. Therefore, this article designs a fault-tolerant controller with constraint compensation information under the backstepping control design framework to solve the state constraint asymmetry problem caused by actuator failure. By designing an improved asymmetric time-varying BLF, the design of the state-constrained controller will become more realistic and the constraints will be weakened. In the design process, the characteristics of the radial basis function neural network are used to avoid the algebraic ring problem. Actuator failure in this article considers deviation failure and loss of effectiveness. Based on the properties of the exponential function, the improved BLF can make the bounds of the state constraints smaller and smaller, and the bounds of the constraints can change with the desired trajectory. Simulation verified the feasibility of this control method.