Constrained Online Optimization Using Evolutionary Operation: A Case Study About Energy-Optimal Robot Control

Optimization of full‐scale processes during regular production is a challenge that is often encountered in practice, requiring specialized approaches that only introduce small perturbations so that production does not need to be interrupted. Based on a case study, we discuss the potential of Evolutionary Operation (EVOP) derived methods. The case study relates to a badminton robot that has to perform point‐to‐point motions during a fixed time interval, based on two operation modes: time‐optimal motion, which ensures maximum precision but highest energy consumption, and energy‐optimal motion, which decreases the energy consumption, but as a trade‐off also lowers the precision. The current standard mode of operation is the energy‐optimal mode that is constructed from off‐line optimization on simulations. An online EVOP steepest ascent optimization to further reduce the energy consumption by fine‐tuning the implemented energy‐optimal mode was implemented. The constrained nature of the problem, where energy needs to be minimized subject to a time constraint, was transformed to an unconstrained single‐objective optimization using Derringer desirability functions. Two important contributions were made: (i) the online optimization of the energy‐optimal motion lowered the energy consumption by 4.7% while keeping the precision constant and (ii) the more stringent time‐constraints implemented in desirability functions lead to an operation mode with maximum precision and 51.7% less energy consumption than the current time‐optimal motion.