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The paper presents the design procedure that was followed to develop and implement a stable fuzzy model reference learning controller (FMRLC) for a rigid-link manipulator. A simulation-based phase plane approach is used to evaluate the stability of the controller. Simulation and experimental results are presented to demonstrate that the FMRLC exhibits learning abilities as well as the ability to adapt to varying system parameters. The results show that a well-designed FMRLC for a rigid-link manipulator can offer better performance than a classical direct fuzzy controller in applications where accurate reference model trajectory tracking and robustness to changes in system parameters are required.