257 research outputs found

    Online identification and nonlinear control of the electrically stimulated quadriceps muscle

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    A new approach for estimating nonlinear models of the electrically stimulated quadriceps muscle group under nonisometric 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 shank-quadriceps 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

    Multifingered robot hand robot operates using teleoperation

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    The purpose of research on anthropomorphic dextrous manipulation is to develop anthropomorphic dextrous robot hand which approximates the versatility and sensitivity of the human hand by teleoperation methods that will communicate in masterā€“ slave manners. Glove operates as master part and multi-fingered hand as slave. The communication medium between operator and multi-fingered hand is via KC-21 Bluetooth wireless modules. Multi-fingered hand developed using 5 volt, 298:1 gear ratio micro metal dc motors which controlled using L293D motor drivers and actuator controlled the movement of robot hand combined with dextrous human ability by PIC18F4520 microcontroller. The slave components of 5 fingers designed with 15 Degree of Freedom (DOF) by 3 DOF for each finger. Fingers design, by modified IGUS 07-16-038-0 enclosed zipper lead E-ChainĀ® Cable Carrier System, used in order to shape mimic as human size. FLEX sensor, bend sensing resistance used for both master and slave part and attached as feedback to the system, in order to control position configuration. Finally, the intelligence, learning and experience aspects of the human can be combined with the strength, endurance and speed of the robot in order to generate proper output of this project

    A robust vector autoregressive model for forecasting economic growth in Malaysia

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    Economic indicator measures how solid or strong an economy of a country is. Basically, economic growth can be measured by using the economic indicators as they give an account of the quality or shortcoming of an economy. Vector Auto-regressive (VAR) method is commonly useful in forecasting the economic growth involving a bounteous of economic indicators. However, problems arise when its parameters are estimated using least square method which is very sensitive to the outliers existence. Thus, the aim of this study is to propose the best method in dealing with the outliers data so that the forecasting result is not biased. Data used in this study are the economic indicators monthly basis starting from January 1998 to January 2016. Two methods are considered, which are filtering technique via least median square (LMS), least trimmed square (LTS), least quartile difference (LQD) and imputation technique via mean and median. Using the mean absolute percentage error (MAPE) as the forecasting performance measure, this study concludes that Robust VAR with LQD filtering is a more appropriate model compare to others model

    Optimized Fuzzy Control For Natural Trajectory Based Fes- Swinging Motion

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    The use of electrical signals to restore the function of paralyzed muscles is called functional electrical stimulation (FES). FES is a promising method to restore mobility to individuals paralyzed due to spinal cord injury (SCI). A crucial issue of FES is the control of motor function by the artificial activation of paralyzed muscles due to the various characteristics of the underlying physiological/biomechanical system. Muscle response characteristics are nonlinear and time-varying. After developing a nonlinear model describing the dynamic behavior of the knee joint and muscles, a closed-loop approach of control strategy to track the reference trajectory is assessed in computer simulations. Then, the controller was validated through experimental work. In this approach only the quadriceps muscle is stimulated to perform the swinging motion by controlling the amount of stimulation pulsewidth. An approach of fuzzy trajectory tracking control of swinging motion optimized with genetic algorithm is presented. The results show the effectiveness of the approach in controlling FES-induced swinging motion in the simulation as well as in the practical environment

    Influence of material composition on flame spread behavior over combustible solid of paper/bagasse

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    Fire Safety Engineering is an application of science to improve the safety from the destructive effect of the fire. Paper is one of sources that easily catch fire, however, research on flame spread towards paper is still not enough to describe about the phenomenon. Inspiration from this, the behavior of downward flame spread over paper/bagasse is experimentally investigated. Composition of 0%, 30%, 50%, 70% and 100% bagasse is chosen for this research. Flame spread behavior for each composition is analyzed from the observation. Results show for each composition, the flame spreads with ā€œUā€ shape at the beginning of combustion until the whole specimen. The result shows that the flame spread rate decreases as bagasse composition increases. The highest flame spread is 0.813 mm/s for pure paper and the lowest one is 0.481 mm/s for pure bagasse. Not only that, it also shows the flame spread rate decreases as the density increases. It is clarified that density for bagasse is higher than paper. Result infers that the flame spread shape and rate are not only influenced by the bagasse composition but also by the densit

    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
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