Methods of Trajectory Tracking for Flexible Link Manipulators

Abstract

reviewed to compare their structure and performance and to identify the most effective in resolving the difficulties. 6, 7, 8, 9, 10 The operational problems with robots in space relate to several factors. One of the most important is the structural flexibility problem of the manipulator and subsequently significant difficulties with its control systems, especially, position control. This paper presents a review of previous work on advanced control schemes for manipulator endpoint positioning while tracking a square trajectory 12.6 m x 12.6 m. by a two-link flexible robot. Schemes include; command shaping, inverse dynamics, linear quadratic regulator, linear quadratic Gaussian with linearized Kalman filter, extended Kalman filter and fuzzy logic adaptive extended Kalman filter, fuzzy logic and repetitive learning. While each scheme tracks with differing degrees of precision, fuzzy logic, in some form within a control scheme, tracks consistently with a high degree of precision, flexibility and robustness. They include; inverse dynamics (IDC), linear quadratic regulator (LQR), linear quadratic Gaussian (LQG) with linearized Kalman filter, extended Kalman filter (EKF) and fuzzy logic adaptive extended Kalman filter (FLAEKF), fuzzy logic (FLC) and, repeated learning (RL). A description of a command shaping, inverse kinematics technique for tracking a square trajectory studied by Banerjee and Singhose is included for comparison. 3, 4 Their work stimulated the studies reviewed in this paper and is alluded to in previous papers. 6, 7, 8, 9, 10