Each individual performs different daily activities such as reaching and lifting with his hand that shows the mportant role of robots designed to estimate the position of the objects or the muscle forces. Understanding he body’s musculoskeletal system’s learning control mechanism can lead us to develop a robust control echnique that can be applied to rehabilitation robotics. The musculoskeletal model of the human arm used in his study is a 3-link robot coupled with 6 muscles which a neurofuzzy controller of TSK type along multicritic gents is used for training and learning fuzzy rules. The adaptive critic agents based on reinforcement learning versees the controller’s parameters and avoids overtraining. The simulation results show that in both states of ith/without optimization, the controller can well track the desired trajectory smoothly and with acceptable ccuracy. The magnitude of forces in the optimized model is significantly lower, implying the controller’s correct operation. Also, links take the same trajectory with a lower overall displacement than that of the nonoptimized mode, which is consistent with the hand’s natural motion, seeking the most optimum trajectory.

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