Model Predictive Super-Twisting Sliding Mode Control for an Autonomous Surface Vehicle

Abstract

This paper presents a new robust Model Predictive Control (MPC) algorithm for trajectory tracking of an Autonomous Surface Vehicle (ASV) in presence of the time-varying external disturbances including winds, waves and ocean currents as well as dynamical uncertainties. For fulfilling the robustness property, a sliding mode control-based procedure for designing of MPC and a super-twisting term are adopted. The MPC algorithm has been known as an effective approach for the implementation simplicity and its fast dynamic response. The proposed hybrid controller has been implemented in MATLAB / Simulink environment. The results for the combined Model Predictive Super-Twisting Sliding Mode Control (MP-STSMC) algorithm have shown that it significantly outperforms conventional MPC algorithm in terms of the transient response, robustness and steady state response and presents an effective chattering attenuation in comparison with the Super-Twisting Sliding Mode Control (STSMC) algorithm.

Automatic Control of Ship Motion Conducting Search in Open Waters

“…Nowadays, considering structural and model parametric uncertainties in marine systems, some nonlinear adaptive techniques are being tested. In recent years, systems such as artificial neural networks [4, 23], sliding mode control [9,19], predictive control [8,25], fuzzy control [29], backstepping control [26] and control applying a nonlinear observer [31] have been tested to achieve high quality regulation. The advantages of the IMC for the control of the ship motion are presented in [28].…”

Method of Cooperative Formation Control for Underactuated USVS Based on Nonlinear Backstepping and Cascade System Theory

“…An unmanned surface vehicle (USV) is an intelligent autonomous surface vessel, of a type that has played an indispensable role in several fields such as science, economics and the military [1][2][3]. The problem of cooperative formation tracking control of multiple USVs has attracted increasing amounts attention from researchers from all over the world over recent years, since a team of USVs working together is often more effective than a single vehicle for challenging missions such as surveillance, hydrographic surveys, autonomous exploration of ocean resources, reconnaissance, rescue operations and perimeter security [4][5][6]. It is well-known that the control system of an USV is generally underactuated, since the number of control inputs is less than the degrees of freedom and there is an unintegrable acceleration constraint on the system.…”

“…A robust control scheme was developed for the time-varying formation of multiple underactuated autonomous underwater vehicles (AUVs) with environment disturbances and input saturation in [25]. A new robust model predictive control (MPC) algorithm for trajectory tracking of an autonomous surface vehicle (ASV) in the presence of time-varying external disturbances was proposed in [5], and a high-performance super-twisting sliding mode control method for a maritime autonomous surface ship (MASS) using approximate dynamic programming (ADP)-based adaptive gains and time delay estimation was presented in [26]. Although MPC is a superior method for motion control of a ship, especially when the model is unknown, the design process of constraint conditions is strict and the calculation of the system is complex in some cases.…”

Control of Dynamic Positioning System with Disturbance Observer for Autonomous Marine Surface Vessels

“…The further development of control algorithms used in dynamic positioning systems was associated with the emergence of alternative control strategies such as robust control [55][56][57][58][59][60][61][62][63], modal control [64,65], adaptive control [66] and model predictive control [46,[67][68][69]. Other solutions for control systems were related to the developments taking place in non-linear control [70,71] using methods such as backstepping [72][73][74][75][76][77][78], dynamic surface control [79,80], active direct surface control [81], nonlinear PID control [18,82,83], port-Hamiltonian framework [84] and sliding mode control [85][86][87]. A hybrid DP system using supervisory switching control logic to change between the bank of controllers and observers was also proposed [88][89][90].…”