Abstract
The purpose of this study is to implement a human-like balance recovery controller and analyze its robustness and energy consumption. Three main techniques to maintain balance can be distinguished in humans, namely (i) the ankle strategy, (ii) the hip-ankle strategy, (iii) the stepping strategy. Because we only consider quiet standing balance, then stepping is not included in our balance recovery study. Numerical model predictive control (N-MPC) is proposed to predict the best way to maintain balance against various disturbance forces. To simulate balance recovery, we build a three-link model including a foot with unilateral constraints, the lower body, and the upper body. Subsequently, we derive the dynamical equations of the model and linearize them. Based on human balance capabilities, we set bound constraints on our model, including angles and balance torques of the ankle and hip. Unilateral constraints are set on the foot, which makes our model more similar to the human quiet standing case. Finally, we implemented a simulation of the proposed ankle and hip-ankle strategy in simulation and analyzed the obtained results from kinematic and dynamic indices as well as from an energy consumption perspective. The robustness of the proposed controller was verified through the obtained simulation results. Thus, this study provides a better understanding of human quiet standing balance that could be useful for rehabilitation.
Highlights
Human balance recovery is an important topic in the human rehabilitation field
After obtaining the state-space representation of our model, we introduce the concept of model predictive control (MPC) and propose our Numerical model predictive control (N-MPC) approach with boundary conditions
NUMERICAL SIMULATION OF THE PROPOSED RECOVERY STRATEGY we describe our implementation of the proposed human-like hip-ankle strategy for balance recovery and the control scheme based on N-MPC in the Mujoco simulation environment [48]
Summary
Human balance recovery is an important topic in the human rehabilitation field. Human balance has been extensively studied for many years. 2) Numerical MPC with system states and control constraints is proposed to implement a human-like balance strategy and autonomous switching between ankle strategy and hip-ankle strategies during quiet standing balance for the different disturbing forces. This model illustrates that N-MPC is similar to the behavior elicited by the human brain and nervous system from a neuroscience viewpoint. In this study, we need to simultaneously consider the state and input constraints, to meet the requirements of a human-like balance behavior This is one of the advantages of the proposed controller. To the best of our knowledge, the method proposed in the aforementioned study is not straightforwardly applicable to solve our problem
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