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Corrigendum to "Nonlinear model predictive control (NMPC) based trajectory tracking on EAST Articulated Maintenance Arm (EAMA)" [Fusion Eng. Des. [Extracted from the article]

• A nonlinear model predictive control based trajectory tracking controller is formulated. • Kinematics model and collision avoidance constraints are both automatically satisfied. • A novel two-step recursive algorithm is developed for efficient solving of NMPC. EAST Articulated Maintenance Arm (EAMA) is a 8-DOF redundant articulated serial manipulator utilized to conduct maintenance tasks in EAST (Experimental Advanced Superconducting Tokamak). Due to the redundancy and total length (8.7395 m) of manipulator and the narrow space in the CASK and the curved vacuum vessel(VV), it is difficult to perform online collision-free end effector trajectory tracking on EAMA. Current solution is to record a feasible trajectory offline by the operator after thousands of trials and errors as the reference for EAMA to track, which is time-consuming, inefficient, not friendly to operators, absence of resistance to external disturbances and difficult to be combined with other maintenance tasks like visual servoing. This paper proposes a Nonlinear Model Predictive Control (NMPC) based online trajectory tracking method, which is easy to use and flexible. The trajectory tracking problem is elegantly formulated as an optimization problem to minimize the end effector's tracking error while satisfying nonlinear constraints consisting of boundaries of state, output and control input, kinematic model of EAMA and collision avoidance constraints. Besides, a two-step recursive solver is developed to speed up the solving of NMPC problem to guarantee real-time control. The effectiveness and good performance of this method are demonstrated by an inspection simulation where the end effector tracks a semicircular trajectory. [ABSTRACT FROM AUTHOR]

• The general mathematical model of mover was established by using physics-based method. • A state error feedback-based state observer was established. • Controller based on model predictive control (MPC) was designed. The control matrix was solved based on the primal-dual method and Hildreth iterative method to improve the calculation efficiency and the stability of the actual operation. • The results showed that the control error of the MPC controller was reduced by 70 % compared to the PI controller. Motion control of Blanket Remote Maintenance Mover (BRMM) is studied in the maintenance and transportation process of blanket in the China Fusion Engineering Test Reactor (CFETR). Firstly, the mathematical model of mover is established in state space function type to provide model basement of model predictive control (MPC) controller. Secondly, system state observer was designed to provide system states to controller. Thirdly, to realize high accuracy motion control of BRMM, MPC controller was solved by using innovative primal-dual method and Hildreth iterative method. Finally, simulation and experiment were conducted to verify the control effectiveness of proposed controller. The results showed that control error was reduced by 70 % by using proposed controller. This work also provides application experience for blanket maintenance in the future. [ABSTRACT FROM AUTHOR]