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. •A novel adaptive-gain multivariable generalized super-twisting is proposed.•Two modified gain-adaptation laws are designed for state- and time-dependent disturbances.•A fixed-time disturbance observer is adopted to provide estimation values.•A nearly optimal controller (NOC) is designed to generate approximate control moments.•In the designed NOC, the requirement of initial stabilizing control is alleviated.