Shared Control of a Powered Exoskeleton and Functional Electrical Stimulation Using Iterative Learning

Abstract

A hybrid exoskeleton comprising a powered exoskeleton and functional electrical stimulation (FES) is a promising technology for restoration of standing and walking functions after a neurological injury. Its shared control remains challenging due to the need to optimally distribute joint torques among FES and the powered exoskeleton while compensating for the FES-induced muscle fatigue and ensuring performance despite highly nonlinear and uncertain skeletal muscle behavior. This study develops a bi-level hierarchical control design for shared control of a powered exoskeleton and FES to overcome these challenges. A higher-level neural network–based iterative learning controller (NNILC) is derived to generate torques needed to drive the hybrid system. Then, a low-level model predictive control (MPC)-based allocation strategy optimally distributes the torque contributions between FES and the exoskeleton’s knee motors based on the muscle fatigue and recovery characteristics of a participant’s quadriceps muscles. A Lyapunov-like stability analysis proves global asymptotic tracking of state-dependent desired joint trajectories. The experimental results on four non-disabled participants validate the effectiveness of the proposed NNILC-MPC framework. The root mean square error (RMSE) of the knee joint and the hip joint was reduced by 71.96 and 74.57%, respectively, in the fourth iteration compared to the RMSE in the 1st sit-to-stand iteration.

Exoskeletons: a review of recent progress

“…Figure 2 shows details of the exoskeleton and the PU-based AI technique, and Figure 3 shows a test subject using the system to stand. Research by workers from the University of Pittsburgh, NC State University and the University of Wisconsin-Milwaukee combines a powered exoskeleton with the functional electrical stimulation (FES) technique (Molazadeh et al, 2021). FES is often prescribed to reanimate standing and walking functions in people with spinal cord injury and other gait disorders but causes a rapid onset of muscle fatigue.…”

“…Structure of the controller and the test bed used during experiments to validate the controller (Credit: Molazadeh et al , 2021, Frontiers in Robotics and AI . doi: 10.3389/frobt.2021.711388)…”

Muscle Fatigue Regulation through Muscle Activation Control in a Knee Hybrid Exoskeleton: Simulation Study

“…Hierarchical control is another method used to overcome the fatigue challenge [15][16][17]. In this method, a closed-loop control signal is calculated.…”

“…The studies used before to deal with muscle fatigue have some drawbacks: (1) In MPC-based methods, to face the muscle fatigue challenge, the control distribution between the motor and FES is achieved by solving the optimization problem, but the muscle fatigue increase in the obtained results is significant [18][19][20][21]. (2) In hierarchical control and MPC-based methods [15][16][17][18][19][20][21], which use optimization to prevent muscle fatigue increase, sub-optimal control values may be calculated at the boundary points instead of optimal values. In this way, reducing muscle fatigue is not optimal, and preventing the increase of muscle fatigue is associated with errors.…”

“…In this way, reducing muscle fatigue is not optimal, and preventing the increase of muscle fatigue is associated with errors. (3) In the reported results of hierarchical control methods, for people with spinal cord injury, the muscle does not respond to FES, the muscle fatigue is not calculated, and it is impossible to determine the FES control signal weight to decrease fatigue [15][16][17]. (4) In the methods based on switching, to compensate for muscle fatigue, FES is deactivated in specific time intervals; as a result, it becomes impossible to use the therapeutic benefits of electrical stimulation in those intervals [8][9][10][11][12][13][14].…”