Learning‐based super‐twisting sliding‐mode control for space circumnavigation mission with suboptimal reaching under input constraints

Abstract

AbstractThe problem of attitude‐orbit synchronous finite‐time control for the space circumnavigation (SCN) mission with input constraints is investigated in this research article. A novel terminal sliding‐mode (TSM) manifold with nonsingular first derivative is developed to ensure that the sliding‐mode reduced‐order system is practical finite‐time stable. Then, the learning‐based adaptive dynamic programming (ADP) technique is adopted to design a super‐twisting sliding‐mode control scheme, so that the proposed TSM is suboptimal reached. Especially, benefited from the introduction of two nonlinear performance indexes, the proposed sliding‐mode control strategy satisfies the saturation constraint of the propulsion system, and the weight update rate of the actor‐critic (AC) network will not be too large, even if the initial value of the system is too large. Finally, the simulation results show that both attitude and orbital tracking errors in the SCN mission are forced to converge to a small neighborhood containing the origin synchronously under input saturation constraints.