Model Predictive Control of a Highly Dynamic Parallel SCARA Robot

Abstract

Mechatronic application operating in dynamic and unstructured environment can benefit greatly from use of online optimization i.e. non-linear model predictive control (NLMPC). Unfortunately, the deterministic time implementation of NL-MPC on typical industrial automation hardware remains an open challenge, as well as guaranteeing performance in full operational behaviour. This article documents an implementation of an NL-MPC tool-chain. The developed methods are used on a highly dynamic parallel SCARA robot as a performant example that can benefit from the use of the proposed approach. Through us of NL-MPC, energy optimal path planning is demonstrated to operate robustly in all defined experimental conditions, while not violating the prescribed computational time. The resulting system performance is then benchmarked to the conventional industrial automation solution, and the improvement in performance is highlighted showing an improvement of up to 36% in energy efficiency.