Human-Like Balance Recovery Based on Numerical Model Predictive Control Strategy

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.