Adaptive Controller Tuning Method Based on Online Multiobjective Optimization: A Case Study of the Four-Bar Mechanism

The efficient speed regulation of four-bar mechanisms is required for many industrial processes. These mechanisms are hard to control due to the highly nonlinear behavior and the presence of uncertainties or disturbances. In this paper, different Pareto-front approximation search approaches in the adaptive controller tuning based on online multiobjective metaheuristic optimization are studied through their application in the four-bar mechanism speed regulation problem. Dominance-based, decomposition-based, metric-driven, and hybrid search approaches included in the algorithms, such as nondominated sorting genetic algorithm II, multiobjective evolutionary algorithm based on decomposition and differential evolution, S-metric selection evolutionary multiobjective algorithm, and nondominated sorting genetic algorithm III, respectively, are considered in this paper. Also, a proposed metric-driven algorithm based on the differential evolution and the hypervolume indicator (HV-MODE) is incorporated into the analysis. The comparative descriptive and nonparametric statistical evidence presented in this paper shows the effectiveness of the adaptive controller tuning based on online multiobjective metaheuristic optimization and reveals the advantages of the metric-driven search approach.