Abstract
This article addresses the problem of designing intelligent robust tracking controls of robot systems actuated by brushed direct current motors. The structures of both mechanical and electrical dynamics are allowed to be completely unknown and adaptive fuzzy (or neural network) systems are employed to approximate these two uncertainties. Consequently, an adaptive fuzzy-based (or neural network-based) state feedback tracking controller is developed such that the resulting closed-loop system guarantees that all the states and signals are bounded and the tracking error can be made as small as possible. Finally, simulation examples are made to demonstrate the effectiveness and tracking performance.
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