Neural Network-Based Adaptive Control for Spacecraft Under Actuator Failures and Input Saturations

In this article, we develop attitude tracking control methods for spacecraft as rigid bodies against model uncertainties, external disturbances, subsystem faults/failures, and limited resources. A new intelligent control algorithm is proposed using approximations based on radial basis function neural networks (RBFNNs) and adopting the tunable parameter-based variable structure (TPVS) control techniques. By choosing different adaptation parameters elaborately, a series of control strategies are constructed to handle the challenging effects due to actuator faults/failures and input saturations. With the help of the Lyapunov theory, we show that our proposed methods guarantee both finite-time convergence and fault-tolerance capability of the closed-loop systems. Finally, benefits of the proposed control methods are illustrated through five numerical examples

Asymptotic tracking control for a more representative class of uncertain nonlinear systems with mismatched uncertainties

This paper is mainly focusing on the problem of high-accuracy tracking control design for a class of nonlinear systems subject to mismatched uncertainties. A novel asymptotic control framework is presented. This is achieved by developing an estimator-based controller with an observer-based estimator, which is applied to precisely estimate all the system uncertainties. It is proved that the overall tracking system can be asymptotically stabilized. The estimation error of the system uncertainties is also ensured to be asymptotically stable. The main contribution of this paper is that the proposed solution can control a more representative class of nonlinear systems. Another key feature of this control framework is that the incorporated observer-based estimator can eliminate the assumption that system uncertainties should vary slowly or even have no variation in the existing estimators for uncertainties. This superior tracking control property of the scheme is validated by a robotic manipulator example