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This paper presents a method based on a multi-objective self-adaptive differential evolution (MOSaDE) algorithm to improve the parametric reconfiguration feature in the optimal design of a parallel robot. We propose a MOSaDE algorithm, in which both trial vector generation strategies and their associated control parameter values are gradually self-adapted by learning from their previous experiences in generating promising solutions. Consequently, a more suitable generation strategy along with its parameter settings can be determined adaptively to match different phases of the search process. Furthermore, a constraint-handling mechanism is added to bias the search to the feasible region of the search space. The obtained solution will be a set of optimal geometric parameters and optimal PID control gains. The results obtained in a set of experiments performed mechatronic system show the effectiveness of the proposed approach.

Structure-Control Design of a Parallel Robot Based on Multi-Objective Self-Adaptive Differential Evolution Algorithm