Bio-Inspired Synergistic Model Predictive Control for Control Reallocation and Reduced Computational Cost in a Hybrid Exoskeleton

Dynamic optimization is a versatile control tool to determine optimal control inputs in a redundantly actuated wearable robot. However, dynamic optimization requires high computational resources for real-time implementation. In this paper, we present a bio-inspired control approach, based on the principle of muscle synergies, to reduce the computational cost of optimization. The most important linear combinations of actuators, dubbed “artificial synergies,” were identified for the double support phase (DSP) and single support phase (SSP) of walking, allowing for hip, knee, and ankle actuation. In simulations, we compared the bio-inspired (input dimensionality reduced) model predictive control (MPC) with a conventional MPC using the full-dimensional actuation model. For both the DSP and SSP, incorporating synergies reduces the mean number of iterations per optimization step. A minimum number of synergies are indeed necessary to truly achieve redistribution of control effort across the other actuators when a primary muscle is fatigued. Additionally, we provide a practical approach to conduct real-time experiments with the bio-inspired MPC. A data-driven modeling approach is used to identify the nonlinear musculoskeletal dynamics and extract personalized artificial synergies from the experimental hybrid exoskeleton walking data. Synergistic MPC reduces computation time by an average of 28.16% ( ${p}\lt {0}.{03}$ ) compared to full-dimensional MPC. Furthermore, we demonstrate control redistribution in response to varying cost function penalties on individual synergy activations. It is, to the authors’ knowledge, the first instance of artificial synergy-based MPC in real-time for a hybrid gait exoskeleton. This study provides insight into the use of bio-inspiration for hybrid exoskeleton control and other rehabilitation systems with redundant actuators.