Fixed-time disturbance observer-based nearly optimal control for reusable launch vehicle with input constraints

In this paper, a fixed-time disturbance observer-based nearly optimal control (FTDO-NOC) scheme is proposed for reusable launch vehicle (RLV) subject to model uncertainties, input constraints, and unknown mismatched/matched disturbances. The dynamics of RLV attitude motion are divided into outer-loop subsystem and inner-loop subsystem. For the outer-loop subsystem, to address the problems of unknown mismatched disturbances and model uncertainties, a novel adaptive-gain multivariable generalized super-twisting (AMGST) controller is proposed. Two modified gain-adaptation laws are derived for tuning the control gains of AMGST controller, which attenuates chattering efficiently. For the inner-loop subsystem, considering the effect of unknown matched disturbances, a fixed-time disturbance observer (FTDO) is utilized to estimate the matched disturbances and the time derivative of virtual control input. Incorporated with the designed FTDO, a nearly optimal controller (NOC), which is based on the critic-actor neural networks (NNs), is utilized to generate the approximate optimal control moments satisfying the input constraints. The tracking errors of inner-loop subsystem and the weight estimation errors of the critic-actor NNs are proved to be uniformly ultimately bounded (UUB) via Lyapunov technique. Finally, we provide simulation results to validate the effectiveness and superiority of the proposed control scheme.