3,156 research outputs found

    Temperature sensitivity of the pyloric neuromuscular system and its modulation by dopamine

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    We report here the effects of temperature on the p1 neuromuscular system of the stomatogastric system of the lobster (Panulirus interruptus). Muscle force generation, in response to both the spontaneously rhythmic in vitro pyloric network neural activity and direct, controlled motor nerve stimulation, dramatically decreased as temperature increased, sufficiently that stomach movements would very unlikely be maintained at warm temperatures. However, animals fed in warm tanks showed statistically identical food digestion to those in cold tanks. Applying dopamine, a circulating hormone in crustacea, increased muscle force production at all temperatures and abolished neuromuscular system temperature dependence. Modulation may thus exist not only to increase the diversity of produced behaviors, but also to maintain individual behaviors when environmental conditions (such as temperature) vary

    New control strategies for neuroprosthetic systems

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    The availability of techniques to artificially excite paralyzed muscles opens enormous potential for restoring both upper and lower extremity movements with\ud neuroprostheses. Neuroprostheses must stimulate muscle, and control and regulate the artificial movements produced. Control methods to accomplish these tasks include feedforward (open-loop), feedback, and adaptive control. Feedforward control requires a great deal of information about the biomechanical behavior of the limb. For the upper extremity, an artificial motor program was developed to provide such movement program input to a neuroprosthesis. In lower extremity control, one group achieved their best results by attempting to meet naturally perceived gait objectives rather than to follow an exact joint angle trajectory. Adaptive feedforward control, as implemented in the cycleto-cycle controller, gave good compensation for the gradual decrease in performance observed with open-loop control. A neural network controller was able to control its system to customize stimulation parameters in order to generate a desired output trajectory in a given individual and to maintain tracking performance in the presence of muscle fatigue. The authors believe that practical FNS control systems must\ud exhibit many of these features of neurophysiological systems

    Robust compensation of electromechanical delay during neuromuscular electrical stimulation of antagonistic muscles

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    Nonlinear Model-Based Control for Neuromuscular Electrical Stimulation

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    Neuromuscular electrical stimulation (NMES) is a technology where skeletal muscles are externally stimulated by electrodes to help restore functionality to human limbs with motor neuron disorder. This dissertation is concerned with the model-based feedback control of the NMES quadriceps muscle group-knee joint dynamics. A class of nonlinear controllers is presented based on various levels of model structures and uncertainties. The two main control techniques used throughout this work are backstepping control and Lyapunov stability theory. In the first control strategy, we design a model-based nonlinear control law for the system with the exactly known passive mechanical that ensures asymptotical tracking. This first design is used as a stepping stone for the other control strategies in which we consider that uncertainties exist. In the next four control strategies, techniques for adaptive control of nonlinearly parameterized systems are applied to handle the unknown physical constant parameters that appear nonlinearly in the model. By exploiting the Lipschitzian nature or the concavity/convexity of the nonlinearly parameterized functions in the model, we design two adaptive controllers and two robust adaptive controllers that ensure practical tracking. The next set of controllers are based on a NMES model that includes the uncertain muscle contractile mechanics. In this case, neural network-based controllers are designed to deal with this uncertainty. We consider here voltage inputs without and with saturation. For the latter, the Nussbaum gain is applied to handle the input saturation. The last two control strategies are based on a more refined NMES model that accounts for the muscle activation dynamics. The main challenge here is that the activation state is unmeasurable. In the first design, we design a model-based observer that directly estimates the unmeasured state for a certain activation model. The second design introduces a nonlinear filter with an adaptive control law to handle parametric uncertainty in the activation dynamics. Both the observer- and filter-based, partial-state feedback controllers ensure asymptotical tracking. Throughout this dissertation, the performance of the proposed control schemes are illustrated via computer simulations

    Robust Compensation of Electromechanical Delay during Neuromuscular Electrical Stimulation of Antagonistic Muscles

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    Neuromuscular electrical stimulation (NMES) can potentially be used to restore the limb function in persons with neurological disorders, such as spinal cord injury (SCI), stroke, etc. Researches on control system design has so far focused on relatively simple unidirectional NMES applications requiring stimulation of single muscle group. However, for some advanced tasks such as pedaling or walking, stimulation of multiple muscles is required. For example, to extend as well as flex a limb joint requires electrical stimulation of an antagonistic muscle pair. This is due to the fact that muscles are unidirectional actuators. The control challenge is to allocate control inputs to antagonist muscles based on the system output, usually a limb angle error to achieve a smooth and precise transition between antagonistic muscles without causing discontinuities. Furthermore, NMES input to each muscle is delayed by an electromechanical delay (EMD), which arises due to the time lag between the electrical excitation and the force development in muscle. And EMD is known to cause instability or performance loss during closed-loop control of NMES. In this thesis, a robust delay compensation controller for EMDs in antagonistic muscles is presented. A Lyapunov stability analysis yields uniformly ultimately bounded tracking for a human limb joint actuated by antagonistic muscles. The simulation results indicate that the controller is robust and effective in switching between antagonistic muscles and compensating EMDs during a simulated NMES task. Further experiments on a dual motor testbed shows its feasibility as an NMES controller for human antagonistic muscles

    Predictor-Based Compensation for Electromechanical Delay During Neuromuscular Electrical Stimulation

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    Master of Science

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    thesisFunctional neuromuscular stimulation (FNS) is electrical stimulation for muscle control. This ability has brought about a new advent in the field of prosthetics called neuroprosthetics. Neuroprosthetics consists of a wide field of devices that stimulate muscles or nerve tissue to either control part of the human body or to give it feedback. Strokes and spinal cord injuries cause a neural disconnect between the brain and the body. Recent research with FNS is exploring methods of bypassing this disconnect and allowing the affected person to control their body with just a thought. This same technology is also being used in robotic limbs that are controlled by thought and are capable of giving the wearer feedback about their environment. Researchers use control algorithms to convert brain signals into motion. With the development and testing of these control algorithms the question has arisen of how to determine when a controller is good enough. How should the neuroprosthetic perform? A standard is needed with which neuroprosthetic control can be measured. The purpose of this research is to measure standard engineering control metrics from functional human wrists in order to develop a standard for future neuroprosthetic design. Three different types of functions were presented to healthy human subjects for trajectory tracking exercises. These functions included step functions to measure transient responses, ramp functions to measure steady state responses, and periodic functions, which are most typical of normal activities of daily living. Varying loads were applied to the participants' wrists, and wrist position was measured. The data from these three experiments were used to measure standard engineering control metrics. From these control metrics statistical regression models were developed to provide a quantitative view of healthy human wrist control with a load applied to it

    A Human Motor Control-Inspired Control System for a Walking Hybrid Neuroprosthesis

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    The purpose of this research is to develop a human motor control-inspired control system for a hybrid neuroprosthesis that combines functional electrical stimulation (FES) with electric motors. This device is intended to reproduce gait for persons with spinal cord injuries (SCI). Each year approximately 17,000 people suffer from an SCI in the U.S. alone, of which about 20% of them are diagnosed with complete paraplegia. Currently, there is a lot of interest in gait restoration for subjects with paraplegia but the existing technologies use either solely FES or electric motors. These two sources of actuation both have their own limitation when used alone. Recently, there have been efforts to provide a combination of the two means of actuation, FES and motors, into gait restoration devices called hybrid neuroprostheses. In this dissertation the derivation and experimental demonstration of control systems for the hybrid neuroprosthesis are presented. Particularly, the dissertation addresses technical challenges associated with the real-time control of a FES such as nonlinear muscle dynamics, actuator dynamics, muscle fatigue, and electromechanical delays (EMD). In addition, when FES is combined with electric motors in hybrid neuroprostheses, an actuator redundancy problem is introduced. To address the actuator redundancy issue, a synergy-based control framework is derived. This synergy-based framework is inspired from the concept of muscle synergies in human motor control theory. Dynamic postural synergies are developed and used in the feedforward path of the control system for the walking hybrid neuroprosthesis. To address muscle fatigue, the stimulation levels are gradually increased based on a model-based fatigue estimate. A dynamic surface control technique, modified with a delay compensation term, is used to address the actuator dynamics and EMD in the control derivation. A Lyapunov-based stability approach is used to derive the controllers and guarantee their stability. The outcome of this research is the development of a human motor control-inspired control framework for the hybrid neuroprosthesis where both FES and electric motors can be simultaneously coordinated to reproduce gait. Multiple experiments were conducted on both able-bodied subjects and persons with SCI to validate the derived controllers

    Estimates of Persistent Inward Current Decline in Human Soleus Motor Units during Fatigue

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    Fatigue is defined as any exercise induced reduction in strength or power, and can be attributed to central and peripheral components. Many central and peripheral mechanisms have been extensively studied, but few studies have looked at the changes in the intrinsic properties of motor neurons and their contribution to fatigue. Persistent inward current (PIC) is an important intrinsic property of motor neurons responsible for setting a large increase in the gain of motor output and may contribute to fatigue. Inhibitory inputs such as reciprocal inhibition (RI) have been shown to turn off PICs and reducing the gain of output. PIC measurements are typically done in animals but have recently been estimated in humans using the paired motor unit technique. Estimates of PIC were taken from paired motor unit recordings in the soleus. Estimates of PIC are calculated by using the difference (∆F) between the instantaneous firing frequency of a control unit at the recruitment and derecruitment of a test unit during an isometric triangular ramp contraction. Inhibitory input via electrical stimulation of the common peroneal nerve was used to reduce PIC in the soleus. These isometric triangular ramp contractions used to calculated ∆F were performed with and without electrical stimulation after sets of 20 fatiguing contractions in order to assess ∆F estimates of PIC before and after fatigue. Maximum voluntary contractions (MVC) were performed after each set of fatiguing contractions to quantify the amount of fatigue. The experiment was terminated after a 30% reduction in MVC. It was hypothesized that there would be a decline in ∆F estimates of PIC during a fatiguing protocol and no change in PIC during a control day in ramps without electrical stimulation. In ramps with inhibitory input via electrical stimulation (RI), ∆F estimates of PIC would not decline as significantly as ramps without electrical stimulation over the course of a fatiguing protocol. On a control day, the ramps with electrical stimulation would have a lower ∆F than ramps without electrical stimulation, and also would not change over time. On the fatigue day, MVC dropped from 347.18N ± 96.54N to 220.57N ± 65.53N, t(9) = 4.23 (

    Reduction Model Approach for Systems with a Time-Varying Delay

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    International audienceWe provide a reduction model approach for achieving global exponential stabilization of linear systems with a time-varying pointwise delay in the input. We allow the delay to be discontinuous and uncertain. We also provide a stability result based on a different dynamic extension that ensures input-to-state stability with respect to additive uncertainties on the dynamics. Instead of the usual Lyapunov-Krasovskii or Razumikhin methods, we use a trajectory based approach
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