This article addresses the problem of designing intelligent robust tracking controls of robotsystems actuated by brushed direct current motors. The structures of both mechanical andelectrical dynamics are allowed to be completely unknown and adaptive fuzzy (or neuralnetwork) systems are employed to approximate these two uncertainties. Consequently, anadaptive fuzzy-based (or neural network-based) state feedback tracking controller isdeveloped such that the resulting closed-loop system guarantees that all the states and signalsare bounded and the tracking error can be made as small as possible. Finally, simulationexamples are made to demonstrate the effectiveness and tracking performance.
International Journal of Systems Science Vol. 39, No. 5, May 2008, 497–511