PARALLEL PLATFORM CONTROLLER BASED ON ADAPTIVE DIFFERENCE ALGORITHM – PART 2

There are two main approaches to motion control on parallel platforms: joint space control and workspace control. Joint space control is an easy-to-implement semi-closed-loop strategy, but its control effect could be better. The workspace control is to obtain the real-time position of the parallel platform through the forward solution and close the speed and position loop of the parallel platform in the workspace. This paper uses a Model Predictive Controller (MPC) to control the parallel platform with workspace control as the research goal. The loss function is constructed based on the swarm intelligence optimization idea, and the adaptive difference algorithm is used to optimize the parameters of MPC. This part uses MATLAB to perform simulation experiments to complete the S-shaped velocity trajectory planning algorithm. In addition, the control effect of MPC and position-loop PI controller in a robust disturbance environment is compared. Experiments show that MPC has the advantages of low energy consumption and high control accuracy.