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