Adaptive neural network-based fault estimation for nonlinear systems with actuator faults in simultaneous multiplicative and additive forms

Non-linearities and actuator faults often exist in practical systems which may degrade system performance or even lead to catastrophic accidents. In this article, a fault-tolerant compensation control strategy is proposed for a class of non-linear systems with actuator faults in simultaneous multiplicative and additive forms. First, radial basis function neural network is employed to approximate the system non-linearity. The approximation is achieved by only one adaptive parameter, which simplifies the computation burden. Then, by means of the backstepping technique, an adaptive neural controller is developed to cope with the adverse effects brought by the system non-linearity and actuator faults in multiplicative and additive forms. Meanwhile, the proposed control design scheme can guarantee that the considered closed-loop system is stable. The novelty of the article lies in that the system non-linearity, the additive actuator faults, and the multiplicative actuator faults that often exist in practical engineering are catered for simultaneously. Furthermore, compared with some existing works, the approximation of the system non-linearity is achieved by only one adaptive parameter for the purpose of reducing the computation burden. Therefore, its applicability is more general. Finally, a numerical simulation and a comparative simulation are carried out to show the effectiveness of the developed controller