Finite time adaptive fault‐tolerant controller based on neural networks for switched stochastic nonlinear systems with actuator failures and disturbance

Abstract

This article investigates the novel finite time adaptive neural fault-tolerant controller (FTC) for strict-feedback switched stochastic systems under arbitrary switching signals and takes into actuator failures including loss of effectiveness faults and bias faults consideration concurrently. Neural networks are utilized to approximate the unknown external disturbance and internal dynamics. On the basis of Itô differential equation and backstepping technique, an adaptive neural finite time FTC method is put forward. It is attested that the closed-loop systems are semiglobal practical finite time stable in probability and the tracking effects are great. Finally, to further demonstrate the high efficiency of proposed control method, two simulation examples are given.

Robust adaptive finite‐time control design for the generation of stable oscillations in the strict‐feedback form non‐linear systems with input dead‐zone non‐linearity

“…Theorem 1. Consider the uncertain non-linear system (8) with input dead-zone non-linearity (9). The actual control input (67) ensures that all variables in the closed-loop system remain bounded and guarantees that the trajectory of the closed-loop system converges to the 𝜀-neighbourhood of the desired limit cycle in a finite time where T reach and 𝜀 are as follows:…”

“…According to (9), it is obvious that the dead zone function is non-differentiable and relatively complex which can be FIGURE 2 The schematic of the non-symmetric dead-zone non-linearity expressed as below [24]:…”

“…Such advantages have led to receiving a great deal of attention during the past years. Some impressive and important research has been reported on the finite-time control of various classes of dynamical systems such as multi-agent systems, switch systems, time-delay systems, stochastic systems, MIMO systems, and systems with input nonlinearities [6][7][8][9][10]. These studies have led to the achievement of valuable results in the control of different dynamical systems.…”