Hybrid Control of a Mechatronic System: Fuzzy Logic and Grey System Modeling Approach

This paper presents a method of fuzzy control combined with grey system modeling approach of a mechatronic system to be used in driving a four-bar mechanism by a dc motor through a buck converter. The main contribution of this paper is that it predicts the future error values in terms of the previous error values using a grey estimator and determines the control action for the following step that depends on the predicted error value before it occurs. Despite the basic assumption that the angular velocity of the crank is constant in most of the mechanism analysis, this may not be valid when the mechanism is connected to an electric motor. In this paper, a complete state-space mathematical model of each part of the converter-motor-mechanism system is first developed and numerically simulated to demonstrate the crank angular speed fluctuations for the case of a constant voltage supply. Then, a fuzzy logic controller combined with grey system modeling approach is designed to regulate the crank angular speed of the mechanism and compared with a fuzzy controller used alone. Finally, results are obtained for each part of the system separately, then related parts are connected as cascade, and complete system control is tested for the proposed control approach. The connection between dc motor and four-bar mechanism has been considered as a dynamic load for the converter. Comparatively better results are obtained when the fuzzy controller is used together with grey system modeling approach. The results obtained from the proposed controller are not only superior in the rise time, speed fluctuations, and percent overshoot, but also much better in the controller output signal structure.