Unconstrained MPC and PID evaluation for motion profile tracking applications

Abstract

Motion porfile tracking control forms an integral part of most industrial applications ranging from manufacturing, where planned motion robots perform assebly tasks, medical applications for position patients and moving scanners across patient bodies and unmanned vehicle applications such as automated parking. In all these applications, the challanges are keeping the tracking error as low as possible, using as low a control effort as can be afforded. Numerous control methods ranging from PID, to Sliding Mode Control (SMC) have been researched in this regard, some with great succes. With the objectives and aspects of motion profile tracking, specifically a desired trajectory that is known apriori, it is intuitive to assume that Model Predictive Control (MPC) which is heavily rooted on the use of future information would be a good candidate for such applications. In this paper we test optimally tunned MPC and PID controllers for motion profile tracking applications, and draw comparisons based on Pareto optimality. Optimal controller tunning is performed and simulation as well of experimental tests of some of the pareto optimal points are undertaken for comparisons. The results for PID control are reasonable and MPC gives unanticipated outcomes.