A Hybrid Adaptive Controller Applied for Oscillating System

Abstract

In this paper, a hybrid PI radial basis function neural network (RBFNN) controller is used for a plant with significant disturbances related to the mechanical part of the construction. It is represented through a two-mass system. State variables contain additional components—as a result, oscillations affect the precision of control. Classical solutions lead to movements of the poles of the whole control structure. However, proper tuning of the controller needs detailed identification of the object. In this work, the neural network is implemented to improve the classical PI controller’s performance and mitigate the errors generated by oscillations of the mechanical variables and parametric uncertainties. The proposed control strategy also guarantees the closed-loop stability of the system. The mathematical background is firstly presented. Afterward, the simulation results are shown. It can be stated that the results are very promising, and a significant improvement in oscillations damping is achieved. Finally, experimental tests are conducted to substantiate the obtained simulation results. For this purpose, the algorithm was implemented in the dSPACE card. Achieved transients confirm the numerical tests.