Fuzzy Supervised Learning-Based Model-Free Adaptive Fault-Tolerant Spacecraft Attitude Control With Deferred Asymmetric Constraints
Xiaoyun Sun, Qiang Shen, Shufan Wu
Abstract
Aiming at solving the model-free fault-tolerant spacecraft attitude control problem, a data-driven adaptive control scheme is proposed to the spacecraft in the presence of actuator faults and performance constraints. First, the discrete-time attitude dynamic model is transformed into an affine nonlinear system based on local dynamic linearization. Then, a fuzzy logic-based lazily adapted constant kinky inference rule is introduced to predict the arbitrarily continuous nonlinear actuator faults and model uncertainties by supervised learning with insufficient prior knowledge. To satisfy time-varying deferred asymmetric constraints of the attitude tracking error, a virtual control law is proposed in the attitude control loop using the back-stepping approach, which is derived from a deferred switching transformation and barrier Lyapunov function. The stability of the data-driven adaptive fault-tolerant attitude control for the nonlinear discrete-time spacecraft attitude dynamics is analyzed rigorously with the aid of contraction mapping principle and discrete-time Lyapunov theory. Compared with existing methods, the proposed one considers nominal nonglobal Lipschitz nonlinear system with arbitrarily continuous unmodeled uncertainties and time-varying actuator faults, and achieves smaller prediction error bound than general kinky inference scheme and better closed-loop performance by estimating and compensating unknown dynamics. Finally, numerical simulation verifies the effectiveness of the proposed control scheme.