Each individual performs different daily activities such as reaching and lifting with his hand that shows the important role of robots designed to estimate the position of the objects or the muscle forces. Understanding the body’s musculoskeletal system’s learning control mechanism can lead us to develop a robust control technique that can be applied to rehabilitation robotics. The musculoskeletal model of the human arm used in this study is a 3-link robot coupled with 6 muscles which a neurofuzzy controller of TSK type along multicritic agents is used for training and learning fuzzy rules. The adaptive critic agents based on reinforcement learning oversees the controller’s parameters and avoids overtraining. The simulation results show that in both states of with/without optimization, the controller can well track the desired trajectory smoothly and with acceptable accuracy. 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.

中文翻译：

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二维平面上人体手臂三自由度肌肉骨骼系统的最优自适应神经模糊控制

每个人都会进行不同的日常活动，例如用手伸手和举起，这显示了旨在估计物体位置或肌肉力量的机器人的重要作用。了解身体肌肉骨骼系统的学习控制机制可以引导我们开发出一种可应用于康复机器人的稳健控制技术。本研究中使用的人类手臂的肌肉骨骼模型是一个与 6 个肌肉耦合的 3 连杆机器人，它使用 TSK 类型的神经模糊控制器和多批评代理来训练和学习模糊规则。基于强化学习的自适应批评代理监督控制器的参数并避免过度训练。仿真结果表明，在有优化和无优化两种状态下，控制器都能很好地平滑地跟踪期望的轨迹，并且精度可以接受。优化模型中的力的大小显着降低，这意味着控制器的操作正确。此外，连杆采用相同的轨迹，总体位移比非优化模式更低，这与手的自然运动一致，寻求最佳轨迹。