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Intelligent robust tracking control designs are proposed in this paper for both uncertain holonomic and nonholonomic mechanical systems. A unified and systematic procedure, that is based on an adaptive fuzzy (or neural network) system and a linear observer, is employed to derive the controllers for these two constrained mechanical systems. Adaptive fuzzy-based (or neural network-based) position feedback tracking controllers can be constructed such that all the states and signals of the closed-loop systems are bounded and the tracking error locally converges to a small region around zero. Only position measurements are required for feedback. The implementation of the fuzzy (or neural network) basis functions depends only on the desired reference information and so once a set of desired trajectories is given, the required basis functions can be explicitly preassigned. Consequently, the intelligent robust position feedback tracking controllers developed here possess the properties of computational simplicity and easy implementation. Finally, simulation examples are presented to demonstrate the effectiveness of the proposed control algorithms.