Multi‐verse optimizer based PI‐type active disturbance rejection generalized predictive control parameter tuning for ship motion

Abstract

The motion control of ships is the crucial of the transportation industry. The ship is an under‐actuated system with large inertia and nonlinearity, it also will be influenced by various interference during the voyaging. This work proposed a multi‐verse optimizer (MVO) tuned PI‐type active disturbance rejection generalized predictive control (PI‐ADRGPC) method. The PI‐ADRGPC is used to control the ship motion, and the MVO is employed to tune the parameters of PI‐ADRGPC. To verify the performance of the proposed method, a set of simulations with different interference are tested. The results indicate that compared with particle swarm optimization, dragonfly algorithm and moth‐flame optimization, the MVO tuned PI‐ADRGPC has better performance under different interference in the ship motion control. The Monte Carlo test also indicates that the robustness of the system is strong.

Data‐based learning control for optimization of nonlinear systems

“…A systematic online adaptive dynamic programming control framework is proposed for smart buildings control to ensure hard constraints to be satisfied 2 . A multi‐verse optimizer tuned PI‐type active disturbance rejection generalized predictive control method is described for the motion control problems of ships 3 . The sufficient optimality conditions for the optimal controls are established under some convexity assumptions 4 .…”

“…The data-based methods are effective solutions for the optimal control of nonlinear systems, which motivate this special issue.This special issue aims to collect and present original research dealing with data-based learning and their applications for optimization and control problems. The first group of papers [1][2][3][4][5][6][7] focuses on data-based control theory, approaches, and applications. A fuzzy model predictive control approach is proposed for stick-slip type piezoelectric actuator to realize the precise control of the end effector.…”

“…2 A multi-verse optimizer tuned PI-type active disturbance rejection generalized predictive control method is described for the motion control problems of ships. 3 The sufficient optimality conditions for the optimal controls are established under some convexity assumptions. 4 A receding-horizon reinforcement learning algorithm is proposed for near-optimal control of continuous-time systems under control constraints.…”