Rotation Angle Control Strategy for Telescopic Flexible Manipulator Based on a Combination of Fuzzy Adjustment and RBF Neural Network

Abstract The length of flexible manipulators with a telescopic arm alters during movement. The dynamic parameters of telescopic flexible manipulators exhibit significant time-varying characteristics owing to variations in length. With an increase in the manipulators’ length, the nonlinear terms caused by flexibility in the manipulators’ dynamic equations cannot be ignored. The time-varying characteristics and nonlinear terms of telescopic flexible manipulators cause fluctuations in rotation angles, which affect the operation accuracy of end-effectors. In this study, a control strategy based on a combination of fuzzy adjustment and an RBF neural network is utilized to improve the control accuracy of flexible telescopic manipulators. First, the dynamic equation of the manipulators is established using the assumed mode method and Lagrange’s principle, and the influence of nonlinear terms is analyzed. Subsequently, a combined control strategy is proposed to suppress the fluctuation of the rotation angle in telescopic flexible manipulators. The variation ranges of the feedforward PD controller parameters are determined by the pole placement strategy and length of the manipulators. Fuzzy rules are utilized to adjust the controller parameters in real-time. The RBF neural network is utilized to identify and compensate the uncertain part of the dynamic model of the flexible manipulators. The uncertain part comprises time-varying parameters and nonlinear terms. Finally, numerical simulations and prototype experiments prove the effectiveness of the combined control strategy. The results prove that the proposed control strategy has a smaller standard deviation of errors. Therefore, the combined control strategy is more suitable for telescopic flexible manipulators, which can effectively improve the control accuracy of rotation angles.