High performance super-twisting sliding mode control for a maritime autonomous surface ship (MASS) using ADP-Based adaptive gains and time delay estimation

Abstract

This research addresses two kinds of problems related to optimal trajectory tracking of a Maritime Autonomous Surface Ship (MASS): those caused by the time-varying external disturbances including winds, waves and ocean currents as well as those resulting from inherent dynamical uncertainties. As the paper shows, an accurate and robust optimal controller can successfully deal with both issues. An improved Optimal Adaptive Super-Twisting Sliding Mode Control (OAST-SMC) algorithm is proposed here as a robust optimal adaptive strategy. In this strategy, in order to improve performance of the standard super-twisting approach, we apply an Approximate Dynamic Programming (ADP)-based optimal tuning of gains and an underlying concept based on Time Delay Estimation (TDE). An ADP algorithm is implemented using an actor-critic neural network to deal with the curse of dimensionality in Hamilton–Jacobi–Bellman (HJB) equation. The critical role of TDE part in this algorithm is estimating the impact of disturbances and uncertainties on the MASS model. The results have shown that OAST-TDE significantly outperforms the ST-TDE and AST-TDE algorithm in terms of the optimal control efforts. Also, compared with a Nonlinear Model Predictive Control (NMPC), proposed controller meets the optimal control efforts and accurate tracking concurrently.