Nonlinear tracking control of a human limb via neuromuscular electrical stimulation (original) (raw)
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Nonlinear neuromuscular electrical stimulation tracking control of a human limb
2009
Abstract A high-level objective of neuromuscular electrical stimulation (NMES) is to enable a person to achieve some functional task. Towards this goal, the objective of the current effort is to develop a NMES controller to produce a knee position trajectory that will enable a human shank to track any continuous desired trajectory (or constant setpoint). A nonlinear control method is developed to control the human quadriceps femoris muscle undergoing nonisometric contractions.
Online identification and nonlinear control of the electrically stimulated quadriceps muscle
Control Engineering Practice, 2005
A new approach for estimating nonlinear models of the electrically stimulated quadriceps muscle group under non-isometric conditions is investigated. The model can be used for designing controlled neuro-prostheses. In order to identify the muscle dynamics (stimulation pulsewidth -active knee moment relation) from discrete-time angle measurements only, a hybrid model structure is postulated for the shankquadriceps dynamics. The model consists of a relatively well known time-invariant passive component and an uncertain time-variant active component. Rigid body dynamics, described by the Equation of Motion (EoM), and passive joint properties form the time-invariant part. The actuator, i.e. the electrically stimulated muscle group, represents the uncertain time-varying section. A recursive algorithm is outlined for identifying online the stimulated quadriceps muscle group. The algorithm requires EoM and passive joint characteristics to be known a priori. The muscle dynamics represent the product of a continuous-time nonlinear activation dynamics and a nonlinear static contraction function described by a Normalised Radial Basis Function (NRBF) network which has knee-joint angle and angular velocity as input arguments. An Extended Kalman Filter (EKF) approach is chosen to estimate muscle dynamics parameters and to obtain full state estimates of the shank-quadriceps dynamics simultaneously. The latter is important for implementing state feedback controllers. A nonlinear state feedback controller using the backstepping method is explicitly designed whereas the model was identified a priori using the developed identification procedure.
Frontiers in Robotics and AI
Neuromuscular electrical stimulation (NMES) is a promising technique to artificially activate muscles as a means to potentially restore the capability to perform functional tasks in persons with neurological disorders. A pervasive problem with NMES is that overstimulation of the muscle (among other factors) leads to rapid muscle fatigue, which limits the use of clinical and commercial NMES systems. The objective of this article is to develop an NMES controller that incorporates the effects of muscle fatigue during NMES-induced non-isometric contraction of the human quadriceps femoris muscle. Our previous work that used the RISE class of non-linear controllers cannot accommodate fatigue and muscle activation dynamics. A totally new control design approach and associated stability proof is required to derive a new class of NMES control design that accounts for muscle fatigue dynamics and a first-order activation dynamics, in addition to the second-order musculoskeletal dynamics. Motivated from a control method for robotic systems in a strict-feedback form, a backstepping based-non-linear NMES controller was designed to accommodate for the additional muscle activation dynamics. Further, experimentally identified estimates of the fatigue and activation dynamics were incorporated in the control design. The developed controller uses a neural networkbased estimate of the musculoskeletal dynamics and error due to fatigue estimation. A globally uniformly ultimately bounded stability is proven the new controller that accounts for an uncertain non-linear muscle model and bounded non-linear disturbances (e.g., spasticity and changing load dynamics). The developed controller was validated through experiments on the left and right legs of 3 able-bodied subjects and was compared with a proportional-derivative (PD) controller and a PD augmented with a neural network. The statistical analysis showed improved control performance compared with the PD controller.
Control of leg movements driven by electrically stimulated muscles
Journal of Automatic Control, 2003
A planar biomechanical model of a human leg has been developed to investigate automatic control strategies for artificially stimulated muscle using electrical stimulation (ES). The model comprises the nonlinear multiplicative model of the muscles, tendons and two body segments (thigh and shank-foot complex), that is, the hip and the knee. Flexor and extensor muscles are included for each joint. The parameters determining the model used for the analysis can all be assessed; hence, the model can be customized to a particular subject. Simulation software is organized within the SIMULINK environment, and the user can easily change parameters of the model by using Dialogue Boxes. The simulation allows determining constraints imposed by parameters, which are essential for designing control system for rehabilitation of humans with disabilities.
Feedback control methods for task regulation by electrical stimulation of muscles
IEEE Transactions on Biomedical Engineering, 1991
Three feedback control algorithms of varying complexity were compared for controlling three different tasks during electrical stimulation of muscles. Two controllers use stimulus pulse width (or recruitment) modulation to grade muscle force (the fixed parameter, first-order PW controller and the adaptive controller). The third controller varies both stimulus pulse width and period simultaneously for muscle force modulation (the PWlSP controller described in the companion paper). The three tasks tested were isometric torque control, unloaded position tracking, and control of transitions between isometric and unloaded conditions. The first task involved the muscle recruitment nonlinearity. The second task added the effects of muscle length-tension and force-velocity nonlinearities. The third task included a sudden change in external loading conditions. The comparative evaluation was carried out in an intact cat ankle joint with stimulation of tibialis anterior and medial gastrocnemius muscles. The simplest PW controller demonstrated robust control for all tasks. The PW/SP controller improved the performance of the PW controller significantly for control of isometric torque and load transition, but only slightly for control of unloaded joint position. However, the adaptive controller did not consistently achieve a significant improvement in performance compared with the PW controller for any task. Results suggest that muscle length-tension and force-velocity nonlinearities affect the performance of these controllers similarly within the tested ranges of movement amplitudes and speeds. Abrupt changes in the system, such as those due to recruitment nonlinearity and external loading transitions, tend to limit the performance of the adaptive controller. The study provides guidelines for choosing control algorithms for neural prostheses.
Further Results on Predictor-Based Control of Neuromuscular Electrical Stimulation
Electromechanical delay (EMD) and uncertain non-linear 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. Index Terms-Electromechanical delay, functional electrical stimulation, input delay, Lyapunov-Krasovskii functionals, Lya-punov Methods, neuromuscular electrical stimulation (NMES), nonlinear control.
Closed-loop neural network-based NMES control for human limb tracking
2012
Abstract Closed-loop control of skeletal muscle is complicated by the nonlinear muscle force to length and velocity relationships and the inherent unstructured and time-varying uncertainties in available models. Some pure feedback methods have been developed with some success, but the most promising and popular control methods for neuromuscular electrical stimulation (NMES) are neural network (NN)-based methods.
ArXiv, 2020
Neuromuscular electrical stimulation (NMES) has been effectively applied in many rehabilitation treatments of spinal cord injured (SCI) individuals. In this context, we introduce a novel robust and intelligent control-based methodology to closed-loop NMES systems. Our approach uses a control law to guarantee the system's stability. And, machine learning tools for both optimizing the controller parameters and system identification, with the novelty of using past rehabilitation data. In this paper, we apply the proposed methodology to the rehabilitation of lower limbs using a control technique namely robust integral of the sign of the error (RISE), an off-line improved genetic algorithm optimizer, and neural network models. Although in the literature the RISE controller presented good results on healthy subjects without any fine-tuning method, a trial and error approach would quickly lead to muscle fatigue for SCI individuals. Therefore, in this paper, for the first time, the RISE...