For an individual suffering from a neurological disorder such as spinal cord injury, traumatic brain injury, or stroke, motorized functional electrical stimulation (FES) cycling is a rehabilitation strategy which offers numerous health benefits. Motorized FES cycling is an example of physical humanrobot interaction in which both systems must be controlled; the human is actuated by applying neuromuscular electrical stimulation to the large leg muscle groups and the cycle is actuated through its onboard electric motor. While the rider is stimulated using a robust sliding-mode controller, the cycle utilizes an admittance controller to preserve rider safety. The admittance controller is shown to be passive with respect to the rider, and the cadence controller is shown to be globally exponentially stable through a Lyapunov-like switched systems stability analysis. Experiments are conducted on three ablebodied participants and four participants with neurological conditions (NCs) to demonstrate the efficacy of the developed controller and investigate the effect of manipulating individual admittance parameters. Results demonstrate an average admittance cadence error of -0.06±1.47 RPM for able bodied participants and -0.02±0.93 RPM for participants with NCs.

Cycling induced by functional electrical stimulation (FES) coupled with motorized assistance is a promising rehabilitative strategy. A switching controller that activates lower limb muscles alongside an electric motor based on the crank angle is developed to facilitate cycling. Due to the periodic nature of cadence tracking in cycling, a repetitive learning controller (RLC) is developed to track a desired cadence trajectory with a known period. The RLC is developed for an uncertain, nonlinear cycle-rider system with autonomous state-dependent switching. Electrical stimulation switches across multiple lower limb muscle groups based on the torque effectiveness throughout the crank cycle. The electric motor provides assistance when the muscle groups yield low torque production. A Lyapunov-based stability analysis that invokes a recently developed LaSalle-Yoshizawa corollary for nonsmooth systems is used to guarantee asymptotic tracking. The developed controller was tested during FES-cycling experiments in five able-bodied individuals and three participants with neurological conditions. The added value of the RLC in cadence tracking is illustrated by comparing the results of two trials with and without the learning feedforward term. The results indicate that the RLC yields a lower mean root-mean-squared cadence tracking error.

Additive Friction Stir Deposition (AFSD) is a recent innovation in non-beam-based metal additive manufacturing that achieves layer-by-layer deposition while avoiding the solid-to-liquid phase transformation. AFSD presents numerous benefits over other forms of fusion-based additive manufacturing, such as high-strength mechanical bonding, joining of dissimilar alloys, and high deposition rates. To improve, automate, and ensure the quality, uniformity, and consistency of the AFSD process, it is necessary to control the temperature at the interaction zone and the force applied to the consumable feedstock during deposition. In this paper, real-time temperature and force feedback are achieved by embedding thermocouples into the nonconsumable machine tool-shoulder and estimating the applied force from the motor current of the linear actuator driving the feedstock. Subsequently, temperature and force controllers are developed for the AFSD process, ensuring that the temperature at the interaction zone and the force applied to the feedstock track desired command values. The temperature and force controllers were evaluated separately and together on setpoints and time-varying trajectories. For combined temperature and force control with setpoints selected at a temperature of 420 °C and a force of 2669 N, the average temperature and force tracking errors are 5.4 ± 6.5 °C (1.4 ± 1.6%) and 140.1 ± 213.5 N (5.2 ± 8.0%), respectively.

Motorized functional electrical stimulation (FES) cycling is a rehabilitation therapy in which electrical stimulation is used to activate lower body muscles to pedal a cycle in conjunction with motorized assistance. FES cycling has been demonstrated to improve cardiovascular parameters, muscle mass, and motor control of people with neurological conditions (NCs). A common FES cycling objective is simultaneous cadence and power (torque) tracking; however, it is unclear how to best coordinate the FES and motor contributions and if power should be tracked instantaneously or averaged over a period of time. This article develops a new FES cycling controller using a switched Lyapunov-like dwell-time analysis for the nonlinear, uncertain cycling system to conclude global exponential cadence tracking and uniformly ultimately bounded power tracking. To evaluate the performance of the developed controller, comparisons are made with two previously developed FES cycling controllers through experiments on seven able-bodied participants and six participants with NCs. For a desired cadence of 50 RPM and a desired power of 10 W, the developed controller demonstrated the smallest tracking errors with an average cadence and power error of 0.01±1.03 RPM and 0.00±0.94 W, respectively, in the able-bodied population and an average cadence and power error of 0.02±1.87 RPM and 0.00±2.46 W, respectively, in the population of people with NCs. Results suggest that the electric motor should be used to track cadence and the FES induced muscle torques should be used to track instantaneous power.

For an individual affected by a neuromuscular condition (NC), functional electrical stimulation (FES)-induced cycling provides a means of functional restoration and therapeutic exercise. Although FES-cycling has been shown to have numerous benefits, there are challenges to implementing closed-loop FES control for coordinated motion. For example, there exists a potentially destabilizing input delay between the application (or removal) of stimulation and the resulting muscle force. Moreover, switching between multiple actuators (such as FES or motor control) can also be destabilizing. This brief develops delay-dependent switching conditions and a robust control method to account for an unknown time-varying input delay of a switched system. A Lyapunov-like analysis is performed to yield semiglobal exponential cadence tracking to an ultimate bound. Experiments were performed on six able-bodied participants and four participants with NCs to validate the developed controller. The proposed controller resulted in an average cadence error of 0.01 ± 2.00 revolutions per minute (RPM) for the able-bodied participants and 0.01 ± 2.72 RPM for participants with NCs.

For individuals with movement impairments due to neurological injuries, rehabilitative therapies such as functional electrical stimulation (FES) and rehabilitation robots hold vast potential to improve their mobility and activities of daily living. Combining FES with rehabilitation robots results in intimately coordinated human–robot interaction. An example of such interaction is FES cycling, where motorized assistance can provide high-intensity and repetitive practice of coordinated limb motion, resulting in physiological and functional benefits. In this paper, the development of multiple FES cycling testbeds and safeguards is described, along with the switched nonlinear dynamics of the cycle–rider system. Closed-loop FES cycling control designs are described for cadence and torque tracking. For each tracking objective, the authors’ past work on robust and adaptive controllers used to compute muscle stimulation and motor current inputs is presented and discussed. Experimental results involving both able-bodied individuals and participants with neurological injuries are provided for each combination of controller and tracking objective. Trade-offs for the control algorithms are discussed based on the requirements for implementation, desired rehabilitation outcomes and resulting rider performance. Lastly, future works and the applicability of the developed methods to additional technologies including teleoperated robotics are outlined.