Abstract
The investigation and study of the limbs, especially the human arm, have inspired a wide range of humanoid robots, such as movement and muscle redundancy, as a human motor system. One of the main issues related to musculoskeletal systems is the joint redundancy that causes no unique answer for each angle in return for an arm's end effector's arbitrary trajectory. As a result, there are many architectures like the torques applied to the joints. In this study, an iterative learning controller was applied to control the 3-link musculoskeletal system's motion with 6 muscles. In this controller, the robot's task space was assumed as the feedforward of the controller and muscle space as the controller feedback. In both task and muscle spaces, some noises cause the system to be unstable, so a forgetting factor was used to a convergence task space output in the neighborhood of the desired trajectories. The results show that the controller performance has improved gradually by iterating the learning steps, and the error rate has decreased so that the trajectory passed by the end effector has practically matched the desired trajectory after 1000 iterations.
Highlights
The reaching movement is accounted for a huge part of hand movements
A swift and complex process occurs in the brain, and after processing, the generated control signals are transmitted to body motors, namely, muscles
Investigating the body’s musculoskeletal system’s control mechanism can lead us to develop a robust control technique that can be applied to rehabilitation robotics
Summary
The reaching movement is accounted for a huge part of hand movements In all these activities, a swift and complex process occurs in the brain, and after processing, the generated control signals are transmitted to body motors, namely, muscles. Many controllers have been introduced and employed to control such systems and produce motions similar to the human movement, which are of different design methods and performances based on their design space (robot task space, joint space, and muscle space) [1, 2]. Each of these spaces has its features and complexity, and as we move from the task space toward the muscle space, it will be difficult to design the controller because of the increasing space order. Many optimization techniques have been proposed to overcome this problem in classification [5], biology and robotics [6,7,8,9]
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