Optimal design of model predictive controller based on transient search optimization applied to robotic manipulators

Due to nonlinearity and uncertainty of the robotic manipulator, the design of the robot controller has a crucial impact on its performance of motion and trajectory tracking. In this paper, the linear parameter varying (LPV) model predictive controller (MPC) of a two-link robot manipulator is established and then the controller???s optimal parameters are determined via a newly developed meta heuristic algorithm, transient search optimization (TSO). The proposed control method is verified by set point and nonlinear trajectory tracking. In the test of set-point tracking, the LPV-MPC scheme optimized by TSO has better performance compared to the computed torque controller (CTC) schemes tuned by TSO or other metaheuristic algorithms. In addition, good performances can also be observed in the tests of nonlinear trajectory tracking via the LPV-MPC scheme by TSO. Moreover, the robustness of the method to structural uncertainty is verified by setting a large system parameter deviation. Results reveal that we achieved some improvements in the optimization of MPC of the robot manipulator by employing the proposed method.

Automated summary

The paper proposes a design method for an LPV-MPC controller and determines the optimal parameters using the TSO algorithm. Experimental results show that the method performs well in optimizing the controller performance of robot manipulators.