BLS-based adaptive fault tolerant control for a class of space unmanned systems with time-varying state constraints and input nonlinearities

In this paper, a Broad Learning System (BLS) based adaptive full state constrained controller is investigated for a class of Space Unmanned Systems (SUSs) subjected to the actuator faults and input nonlinearities. In order to guarantee the time-varying state constraints and reduce the control complexity simultaneously, two nonlinear error transformations are utilized in this work. By estimating the lower boundary of the nonlinear actuator effectiveness, the instable dynamic caused by the actuator faults and input nonlinearities can be overcome. With the aid of the universal approximation ability of the BLS, the unknown nonlinear terms existing in the SUS attitude dynamic model can be handled. Furthermore, benefiting from the nodes dynamic adjusting mechanism of BLS, the control response speed and accuracy can be improved. The simulation results are presented to demonstrate the effectiveness and advantages of the proposed BLS-based adaptive full state constrained control method.