An Optimal Control Platform for Simulation of Gait Patterns and Assistive Strategies, Based on “MATLAB-Moco” Co-Simulation

This paper addresses the field of hip assist technology by focusing on a novel closed-loop control system that uses OpenSim Moco capabilities to optimize the motion and control of musculoskeletal models. The user is equipped with an exoskeleton specifically designed for the hip joint. In the strategy for assisting the user’s hip joint, the use of a closed-loop assist control algorithm for the exoskeleton robot is crucial for walking assistance. The user reacts based on the feedback, and the amount of assist and assist performance can be observed and checked at any step, which can improve factors such as accuracy, stability, and adaptability, which are critical for human-robot exoskeleton interaction. In this paper, the OpenSim Moco toolbox and direct collocation methods are used for the simulation of a walking gait. The OpenSim Moco toolbox supports musculoskeletal models and allows the problem to be solved as an optimal control problem using the direct collocation method. However, currently this toolbox does not support implementation of closed-loop control algorithms, as it is designed to solve the entire motion at once. Given these points, this paper enhances the advantages of the OpenSim Moco toolbox by providing the possibility of step-by-step “MATLABMoco” co-simulation. The proposed structure enables the “MATLAB-Moco” package to be used for closed-loop applications, like the simulation of assistive algorithms. In this structure, at every step of the motion, the controller can receive any desired feedback from the human model and can apply the defined actions to the body. This creates an integrated and responsive interface for any closed-loop simulations that involve the human body and its reactions. In this paper, the proposed method is used for the simulation of a simple assistive strategy in which the control algorithm receives the torque produced by the user and adjusts the assistive torque accordingly. As expected, with increased levels of assistance, the user exerted less effort while FUM-HEXA provided more torque assistance. The results validate the proposed platform and demonstrate its practicality.