Application of principal base parameter analysis to design of adaptive robot controllers

The feasibility of using principal base parameter analysis (PBPA) as an aid in the design and tuning of adaptive model-based controllers for industrial manipulators is investigated. Results from PBPA are utilized to select the minimal size of the adaptive parameter vector and to develop a less heuristic procedure for controller tuning. The design procedure is illustrated by a simple two link example and then extended to the first three links of a PUMA-560. Experimental analysis contrasted with an adaptive model-based control (AMBC) design augmented with PBPA to a completely heuristic procedure used in previous research. The incorporation of PBPA into the AMBC design minimized the computational complexity while reducing the time and expertise necessary to tune the controller for satisfactory tracking efficacy