Application of multi-objective genetic algorithms to the mechatronic design of a four bar system with continuous and discrete variables

This paper deals with a multi-objective optimization of a mechatronic system. The objective functions to minimize are the motor torque and the fluctuation of the system velocity. These goals are achieved by simultaneously finding the best motor in a list, to drive the system and the best distribution of the inertia of the mechanical system parts. This led us to formulate a global optimization problem where all the inertia parameters of the mechanism and the different motors are considered simultaneously. The problem is then presented as a multi-objective optimization one with continuous and discrete variables. A second generation Multi-Objective-Genetic Algorithm method, called Non-dominated Sorting GA-II (NSGA-II), was used to solve this problem. The obtained solutions form what is called a “Pareto front”. They are analyzed for several different design conditions. We showed, in particular, that the proposed method, compared to electromechanical design strategy, proved to be more efficient in finding the optimal combination of the mechanical system and the driving motor besides minimizing the power consumption without the need of sophisticated controllers.