Model Predictive Control of a Feedback-Linearized Hybrid Neuroprosthetic System With a Barrier Penalty

Abstract

Functional electrical stimulation (FES) is prescribed as a treatment to restore motor function in individuals with neurological impairments. However, the rapid onset of FES-induced muscle fatigue significantly limits its duration of use and limb movement quality. In this paper, an electric motor-assist is proposed to alleviate the fatigue effects by sharing work load with FES. A model predictive control (MPC) method is used to allocate control inputs to FES and the electric motor. To reduce the computational load, the dynamics is feedback linearized so that the nominal model inside the MPC method becomes linear. The state variables: the angular position and the muscle fatigue are still preserved in the transformed state space to keep the optimization meaningful. Because after feedback linearization the original linear input constraints may become nonlinear and state-dependent, a barrier cost function is used to overcome this issue. The simulation results show a satisfactory control performance and a reduction in the computation due to the linearization.

Copyright © 2019 by ASME.

Figures

Fig. 1

A hybrid musculoskeletal system driven by FES and an electric motor assist

Fig. 2

This block diagram illustrates how MPC is used to control the feedback linearized system

Fig. 3

Visualization of the optimum search process

Fig. 4

This figure shows the performance of the MPC control method

Fig. 5

This figure shows the muscle fatigue variable with time

Fig. 6

This figure shows the FES input

Fig. 7

This figure shows the motor torque

Fig. 8

This figure shows the computation time of the feedback linearized system and the original nonlinear system at each time instance

Fig. 9

This figure shows the performance of the feedback linearized system and the original nonlinear system