Nicholas Kirsch

Further Results on Predictor-Based Control of Neuromuscular Electrical Stimulation

Electromechanical delay (EMD) and uncertain nonlinear muscle dynamics can cause destabilizing effects and performance loss during closed-loop control of neuromuscular electrical stimulation (NMES). Linear control methods for NMES often perform poorly due to these technical challenges. A new predictor-based closed-loop controller called proportional integral derivative controller with delay compensation (PID-DC) is presented in this paper. The PID-DC controller was designed to compensate for EMDs during NMES. Further, the robust controller can be implemented despite uncertainties or in the absence of model knowledge of the nonlinear musculoskeletal dynamics. Lyapunov stability analysis was used to synthesize the new controller. The effectiveness of the new controller was validated and compared with two recently developed nonlinear NMES controllers, through a series of closed-loop control experiments on four able-bodied human subjects. Experimental results depict statistically significant improved performance with PID-DC. The new controller is shown to be robust to variations in an estimated EMD value.

Model Predictive Control-Based Dynamic Control Allocation in a Hybrid Neuroprosthesis

To date, a functional electrical stimulation (FES)-based walking technology is incapable of enabling a paraplegic user to walk more than a few hundred meters. This is primarily due to the rapid onset of muscle fatigue, which causes limited torque generation capability of the lower-limb muscles. A hybrid walking neuroprosthesis that combines FES with an electric motor can overcome this challenge, since an electric motor can be used to compensate for any reduction in force generation due to the muscle fatigue. However, the hybrid actuation structure creates an actuator redundancy control problem; i.e., a closed-loop controller must optimally distribute torque between FES and an electric motor. Further, the control inputs to FES and an electric motor must adapt as a skeletal muscle fatigues. We consider these issues as open research control problems. In this paper, we propose that a model predictive control (MPC)-based control design can be used to optimally distribute joint torque, and can adapt as the muscle fatigue sets in. Particularly, a customized quadratic programming solver (generated using CVXGEN) was used to simulate MPC-based control of the hybrid neuroprosthesis that elicits knee extension via FES and an electric actuator.Copyright

Optimized Control of Different Actuation Strategies for FES and Orthosis Aided Gait

A combination of functional electrical stimulation (FES) and an orthosis can be used to restore lower limb function in persons with paraplegia. This artificial intervention may allow them to regain the ability to walk again, however, only for short time durations. To improve the time duration of hybrid (FES and orthosis) gait, the muscle fatigue due to FES and the fatigue in arms, caused by a user’s supported weight on a walker, needs to be minimized. In this paper, we show that dynamic optimization can be used to compute stimulation/torque profiles and their corresponding joint angle trajectories which minimize electrical stimulation and walker push or pull forces. Importantly, the computation of these optimal stimulation or torque profiles did not require a predefined or a nominal gait trajectory (i.e., a tracking control problem was not solved). Rather the trajectories were computed based only on pre-defined end-points. For optimization we utilized the recently developed three-link dynamic walking model, which includes both single and double support phases and muscle dynamics. Moreover, different optimal actuation strategies for FES and orthosis aided gait under various scenarios (e.g., use of a powered or an unpowered orthosis combined with stimulation of all or few selected lower-limb muscles) were calculated. The qualitative comparison of these results depict the advantages and disadvantages of each actuation strategy. The computed optimal FES/orthosis aided gait were also compared with able-bodied trajectories to illustrate how they differed from able-bodied walking.Copyright

A Muscle Synergy-Inspired Adaptive Control Scheme for a Hybrid Walking Neuroprosthesis.

A hybrid neuroprosthesis that uses an electric motor-based wearable exoskeleton and functional electrical stimulation (FES) has a promising potential to restore walking in persons with paraplegia. A hybrid actuation structure introduces effector redundancy, making its automatic control a challenging task because multiple muscles and additional electric motor need to be coordinated. Inspired by the muscle synergy principle, we designed a low dimensional controller to control multiple effectors: FES of multiple muscles and electric motors. The resulting control system may be less complex and easier to control. To obtain the muscle synergy-inspired low dimensional control, a subject-specific gait model was optimized to compute optimal control signals for the multiple effectors. The optimal control signals were then dimensionally reduced by using principal component analysis to extract synergies. Then, an adaptive feedforward controller with an update law for the synergy activation was designed. In addition, feedback control was used to provide stability and robustness to the control design. The adaptive-feedforward and feedback control structure makes the low dimensional controller more robust to disturbances and variations in the model parameters and may help to compensate for other time-varying phenomena (e.g., muscle fatigue). This is proven by using a Lyapunov stability analysis, which yielded semi-global uniformly ultimately bounded tracking. Computer simulations were performed to test the new controller on a 4-degree of freedom gait model.

A semi-active hybrid neuroprosthesis for restoring lower limb function in paraplegics.

Through the application of functional electrical stimulation (FES) individuals with paraplegia can regain lost walking function. However, due to the rapid onset of muscle fatigue, the walking duration obtained with an FES-based neuroprosthesis is often relatively short. The rapid muscle fatigue can be compensated for by using a hybrid system that uses both FES and an active orthosis. In this paper, we demonstrate the initial testing of a semi-active hybrid walking neuroprosthesis. The semi-active hybrid orthosis (SEAHO) supports a user during the stance phase and standing while the electric motors attached to the hip section of the orthosis are used to generate hip flexion/extension. FES in SEAHO is mainly used to actuate knee flexion/extension and plantar flexion of the foot. SEAHO is controlled by a finite state machine that uses a recently developed nonlinear controller for position tracking control of the hip motors and cues from the hip angle to actuate FES and other components.

Dynamic surface control of neuromuscular electrical stimulation of a musculoskeletal system with activation dynamics and an input delay

Neuromuscular electrical stimulation (NMES) is the application of an external electrical potential across a neuromuscular effector to generate desired limb movements. Some of the challenges faced during closed-loop control of NMES include: an electromechanical delay (EMD) in the neuromuscular activation dynamics and uncertain nonlinear musculoskeletal dynamics. In this paper, a dynamic surface control (DSC) approach was used to design an NMES controller that compensates for EMD in the activation dynamics. EMD was modeled as a known constant delay embedded in the control input to the first-order muscle activation dynamics that is cascaded to the second-order uncertain musculoskeletal system. The DSC approach was employed to avoid the “explosion of terms” associated with an integrator backstepping approach. The Lyapunov stability analysis confirmed that the DSC approach achieves semi-global uniformly ultimately bounded (SGUUB) tracking for the delayed musculoskeletal system. Simulations were performed on a 1-degree of freedom knee extension dynamics to illustrate the performance of the developed controller during a trajectory tracking task.

Control of functional electrical stimulation in the presence of electromechanical and communication delays

In this paper, we show the feasibility of remotely controlling the elbow extension through functional electrical stimulation (FES) of the triceps muscle. Particularly, we present the experimental results obtained with the new automatic control method, designed to achieve position tracking between a user and the remote manipulator device. The major advantage of the controller is its ability to compensate for the electromechanical delay (EMD) during an FES and the communication delay (CD) due to a remote actuation. Another advantage of the developed FES controller is that only the error state and delay knowledge are required to elicit desired muscle contractions, i.e., the control implementation does not depend on model knowledge of highly nonlinear and time-varying muscle dynamics. The experimental results show its superior performance in comparison to the proportional integral derivative (PID) controller. The control performance of the PID controller and the new controller were tested for different values of a composite delay (EMD + CD).

Model-based Dynamic Control Allocation in a Hybrid Neuroprosthesis

A hybrid neuroprosthesis that combines human muscle power, elicited through functional electrical stimulation (FES), with a powered orthosis may be advantageous over a sole FES or a powered exoskeleton-based rehabilitation system. The hybrid system can conceivably overcome torque reduction due to FES-induced muscle fatigue by complementarily using torque from the powered exoskeleton. The second advantage of the hybrid system is that the use of human muscle power can supplement the powered exoskeleton’s power (motor torque) requirements; thus, potentially reducing the size and weight of a walking restoration system. To realize these advantages, however, it is unknown how to concurrently optimize desired control performance and allocation of control inputs between FES and electric motor. In this paper, a model predictive control-based dynamic control allocation (DCA) is used to allocate control between FES and the electric motor that simultaneously maintain a desired knee angle. The experimental results, depicting the performance of the DCA method while the muscle fatigues, are presented for an able-bodied participant and a participant with spinal cord injury. The experimental results showed that the motor torque recruited by the hybrid system was less than that recruited by the motor-only system, the algorithm can be easily used to allocate more control input to the electric motor as the muscle fatigues, and the muscle fatigue induced by the hybrid system was found to be less than the fatigue induced by sole FES. These results validate the aforementioned advantages of the hybrid system; thus implying the hybrid technology’s potential use in walking rehabilitation.

Nonlinear Model Predictive Control of Functional Electrical Stimulation

One of the major limitations of functional electrical stimulation (FES) is the rapid onset of muscle fatigue. Minimizing stimulation is the key to decreasing the adverse effects of muscle fatigue caused by FES. Optimal control can be used to compute the minimum amount of stimulation necessary to produce a desired motion. In this paper, a gradient projection-based model predictive controller is used for an approximate optimal control of a knee extension neuroprosthesis. A control Lyapunov function is used as a terminal cost to ensure stability of the model predictive control.Copyright

Dynamic Optimization of a Hybrid Gait Neuroprosthesis to Improve Efficiency and Walking Duration: A Simulation Study

The walking duration of gait restoration systems that use functional electrical stimulation (FES) is severely limited by the rapid onset of muscle fatigue. Alternatively, fully actuated orthoses can also be employed to restore walking in paraplegia. However, due to the high power consumption of electric motors the walking duration of such devices are limited by the charge of the batteries. This paper proposes that a hybrid system, which uses FES and an actuated orthosis, is capable of achieving greater walking durations than an FES only system and more energetically efficient than a lower-limb exoskeleton. This is illustrated through results of optimizations of a musculoskeletal gait model for three actuation cases: FES only, electric motors only, and a hybrid system. The presented results illustrate that a hybrid system may be capable of greater walking durations than FES-based systems while using half the energy of a lower-limb exoskeleton.