“…Due to their highly nonlinear nature and time-varying parameters, PAM robot arms present a challenging nonlinear model problem. Approaches to PAM control have included PID control, adaptive control (Lilly, 2003), nonlinear optimal predictive control (Reynolds et al, 2003), variable structure control (Repperger et al, 1998;Medrano-Cerda et al,1995), gain scheduling (Repperger et al,1999), and various soft computing approaches including neural network Kohonen training algorithm control (Hesselroth et al,1994), neural network + nonlinear PID controller (Ahn and Thanh, 2005), and neuro-fuzzy/genetic control (Chan et al, 2003;Lilly et al, 2003). Balasubramanian et al, (2003a) applied the fuzzy model to identify the dynamic characteristics of PAM and later applied the nonlinear fuzzy model to model and to control of the PAM system.…”