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

Abstract

The 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.

An Output Cooperative Controller for a Hydraulic Support Multi-Cylinder System Based on Neural Network Compensation

“…Sliding mode control (SMC) was designed to suppress disturbances and unmodeled dynamics through robust terms, and it has been widely applied in many control systems due to its simple structure. To improve the chattering problem of sliding mode control, many advanced methods have been introduced into the traditional sliding mode design, such as terminal, super-twisting and online learning methods [9,10]. Adaptive robust control (ARC) combined the advantages of adaptive control and robust control to achieve good performance by adjusting the model parameters online and suppressing uncertain disturbances [11].…”