. (2016). The study of model predictive control algorithm based on the force/position control scheme of the 5-DOF redundant actuation parallel robot. Robotics and Autonomous Systems. DOI: 10.1016DOI: 10. /j.robot.2016 Citing this paper Please note that where the full-text provided on King's Research Portal is the Author Accepted Manuscript or Post-Print version this may differ from the final Published version. If citing, it is advised that you check and use the publisher's definitive version for pagination, volume/issue, and date of publication details. And where the final published version is provided on the Research Portal, if citing you are again advised to check the publisher's website for any subsequent corrections. General rightsCopyright and moral rights for the publications made accessible in the Research Portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognize and abide by the legal requirements associated with these rights.•Users may download and print one copy of any publication from the Research Portal for the purpose of private study or research.•You may not further distribute the material or use it for any profit-making activity or commercial gain •You may freely distribute the URL identifying the publication in the Research Portal Take down policy If you believe that this document breaches copyright please contact librarypure@kcl.ac.uk providing details, and we will remove access to the work immediately and investigate your claim. AbstractRedundant actuated parallel robot is a multi-input and multi-output (MIMO) system which usually works in an uncertain environment. In this paper, the force/position hybrid control structure of 6PUS-UPU redundant actuation parallel robot is designed, proportional-integral (PI) and model predictive control (MPC) cascade control strategy are used in the redundant branch of 6PUS-UPU redundant actuation parallel robot. The MPC algorithm is used in the current loop of the permanent magnet synchronous motor (PMSM) to restrain the motor parameter uncertainty and external disturbances influence on motor control. The MATLAB/ADAMS joint simulation method based on virtual 6PUS-UPU redundant actuation parallel robot prototype is used to test the performance of the proposed control strategy. The performance of proposed PI-MPC control strategy is compared with the traditional PI-PI control strategy. The simulation results show that the MPC controller can improve the tracking ability of the motor torque, guarantee the system robustness under the parameter variations and load disturbance environment.

This paper presents a fuzzy identification method for the dynamic model of the 6PUS-UPU redundantly actuated parallel robot. The T-S fuzzy model is the model of the whole system, the input is the pose of the moving platform and the output is the driving force. The fuzzy model is regarded as the feedback loop between the moving platform and the force branch. The dynamic model is built by Kane's method, and a novel closed-loop force/position hybrid control structure with the pose error for a complex multi-DOF spatial redundantly actuated parallel robot is developed. A proportional-integral force controller is presented based on the structure to obtain an optimal solution under the model identification. The proposed procedure is verified by Matlab/Adams simulation with a 6PUS-UPU simulation platform. The simulation results show that the proposed method is valid for designing the 5-DOF redundantly actuated parallel robot with the movable platform pose error. This paper designs the Smith predictor to solve the delay problem of the redundant force control branch.

In this article, two new algorithms of the redundant force branch of 6-PUS/UPU parallel robot are proposed. They are model predictive control combining with proportional, integral, and differential algorithm and fuzzy combining with model predictive control algorithm. The shortcoming of the traditional model predictive control algorithm is complex adjustment, large amount of calculation, the dynamic performance effect of the system. The proposed PID model predictive control algorithm can make the controller parameters adjustment more convenient. However, PID model predictive control algorithm can't obtain good control performance under sudden change in situation. Combining model predictive control algorithm with fuzzy theory, fuzzy model predictive control algorithm has better anti-interference ability than PID model predictive control algorithm and can reduce predictive horizon length as possible as it can. Simulation results show that fuzzy model predictive control algorithm can effectively improve real-time performance of control system, the dynamic tracking performance and robustness than the traditional model predictive control and PID model predictive control algorithm.