Model Predictive Control for Human-Centred Lower Limb Robotic Assistance

Abstract

Loss of mobility or balance resulting from neural trauma is a critical consideration in public health. Robotic exoskeletons hold great potential for rehabilitation and assisted movement, yet optimal assist-as-needed (AAN) control remains unresolved given pathological variance among patients. We introduce a model predictive control (MPC) architecture for lower limb exoskeletons centred around a fuzzy logic algorithm (FLA) identifying modes of assistance based on human involvement. Assistance modes are: 1) passive for human relaxed and robot dominant, 2) active-assist for human cooperation with the task, and 3) safety in the case of human resistance to the robot. Human torque is estimated from electromyography (EMG) signals prior to joint motions, enabling advanced prediction of torque by the MPC and selection of assistance mode by the FLA. The controller is demonstrated in hardware with three subjects on a 1-DOF knee exoskeleton tracking a sinusoidal trajectory with human relaxed, assistive, and resistive. Experimental results show quick and appropriate transfers among the assistance modes and satisfied assistive performance in each mode. Results illustrate an objective approach to lower limb robotic assistance through on-the-fly transition between modes of movement, providing a new level of human-robot synergy for mobility assist and rehabilitation.

Optimal Adaptive Control of a Knee Joint Exoskeleton for Lower Limb Functional Rehabilitation

“…The proposed optimization procedure is used to tune controller parameters (10) such as to minimize the square error sum cost function (11) respecting nonlinear constraints (12) and boundaries limit − π 2 ≤ θ ≤ 0rad, −3.1rad/s ≤ θ ≤ Figure 4 and figure 5 represent respectively step responses of the angular position of knee joint θ and angular velocity of knee joint θ after many executions using PSO algorithm. Figure 5 represent the delivered torque applied to.…”

“…Many control approaches have been developed to drive the lower limb exoskeleton for a different mode of assistance. One can cite, data-driven control [6] [10] [13], adaptive control [14] [15], model-free based control [16] [17], bounded control [18] [19], model predictive control [11] and sliding mode control technique [20] [21]. A comparative study between different control techniques for lower limb orthosis in different operating conditions was provided in [22].…”

“…A comparative study between different control techniques for lower limb orthosis in different operating conditions was provided in [22]. The authors in [11] proposed a control architecture for lower limb assistance robots based on a fuzzy logic system (FLs) for modes of assistance identification and a model predictive controller (MPC). Muscle activity is sensed by electromyography (EMG), enabling MPC to predict human torque and FLS to select assistance mode.…”