Concurrent Optimization for Selection and Control of AC Servomotors on the Powertrain of Industrial Robots

AC servomotors are widely used in industrial robot manipulators to drive high dynamic loads; thus, an appropriate selection and control of the motors contributes to a better performance at specific tasks. In this paper, a concurrent multi-objective dynamic optimization method is proposed for optimal selection and control of synchronous ac servomotors. Three objective functions, energy consumption, tracking error, and total weight of motors, are optimized. Regarding the importance of the reducers to drive the manipulator, our methodology considers as an equality constraint the closed-loop dynamic model of the whole system, where the powertrain (motor–reducer–load) at each actuated-joint is considered. The multi-objective optimization problem is solved by using a genetic algorithm with continuous and discrete variables. The efficiency of the proposed methodology is validated via simulations of an industrial robot.