Concurrent Adaptive Control for a Robotic Leg Prosthesis via a Neuromuscular-Force-Based Impedance Method and Human-in-the-Loop Optimization - Ming Pi (Applied Sciences)
This paper proposes an adaptive human–robot concurrent control scheme that achieves the appropriate gait trajectory for a robotic leg prosthesis to improve the wearer’s comfort in various tasks. To accommodate different wearers, a neuromuscular-force-based impedance method was developed using muscle activation to reshape gait trajectory. To eliminate the use of sensors for torque measurement, a disturbance observer was established to estimate the interaction force between the human residual limb and the prosthetic receptacle. The cost function was combined with the interaction force and tracking errors of the joints. We aim to reduce the cost function by minimally changing the control weight of the gait trajectory generated by the Central Pattern Generator (CPG). The control scheme was primarily based on human-in-the-loop optimization to search for a suitable control weight to regenerate the appropriate gait trajectory. To handle the uncertainties and unknown coupling of the motors, an adaptive law was designed to estimate the unknown parameters of the system. Through a stability analysis, the control framework was verified by semi-globally uniformly ultimately bounded stability. Experimental results are discussed, and the effectiveness of the adaptive control framework is demonstrated. In Case 1, the mean error (MEAN) of the tracking performance was <semantics>3.6°</semantics> and <semantics>3.3°</semantics>, respectively. And the minimized mean square errors (MSEs) of the tracking performance were <semantics>2.3°</semantics> and <semantics>2.8°</semantics>, respectively. In Case 2, the mean error (MEAN) of the tracking performance is <semantics>2.7°</semantics> and <semantics>3.1°</semantics>, respectively. And the minimized mean square errors (MSEs) of the tracking performance are <semantics>1.8°</semantics> and <semantics>2.4°</semantics>, respectively. In Case 3, the mean errors (MEANs) of the tracking performance for subject1 and 2 are <semantics>2.4°</semantics>, <semantics>2.9°</semantics>, <semantics>3.4°</semantics>, and <semantics>2.2°</semantics>, <semantics>2.8°</semantics>, <semantics>3.1°</semantics>, respectively. The minimized mean square errors (MSEs) of the tracking performance for subject1 and 2 were <semantics>1.6°</semantics>, <semantics>2.3°</semantics>, <semantics>2.6°</semantics>, and <semantics>1.3°</semantics>, <semantics>1.7°</semantics>, <semantics>2.2°</semantics>, respectively.