1,721,016 research outputs found
Electrode Array-based Electrical Stimulation using ILC with Restricted Input Subspace
No full textElectrode arrays are gaining increasing popularity within the rehabilitation and assistive technology communities, due to their potential to deliver selective electrical stimulation to underlying muscles. This paper develops the first model-based control strategy in this area, unlocking the potential for faster, more accurate postural control. Due to time-varying nonlinear musculoskeletal dynamics, the approach fuses model identification with iterative learning control (ILC), and employs a restricted input subspace comprising only those inputs deemed critical to task completion. The subspace selection embeds past experience and/or structural knowledge, with a dimension chosen to affect a trade-off between the test time and overall accuracy. Experimental results using a 40 element surface electrode array confirm accurate tracking of three reference hand postures
Upper limb electrical stimulation using input-output linearization and iterative learning control
No full textA control scheme is developed for multi-joint upper limb reference tracking using functional electrical stimulation (FES). In accordance with the needs of stroke rehabilitation, FES is applied to a reduced set of muscles in the arm and shoulder, with support against gravity provided by a passive exoskeletal mechanism. The approach fuses input-output linearization with iterative learning control (ILC), one of the few techniques to have been applied in clinical treatment trials with patients. This powerful hybrid control structure hence extends performance and scope of clinically proven technology for widespread application in rehabilitation robotic and FES domains. In addition to simplifying tracking and convergence properties of the stimulated joints, the framework enables conditions for the stability of unstimulated joints to be derived for the first time. Experimental results confirm tracking performance of the stimulated joints, together with unstimulated joint stability
Control system design for electrical stimulation in upper limb rehabilitation: modelling, identification and robust performance
No full textThis book presents a comprehensive framework for model-based electrical stimulation (ES) controller design, covering the whole process needed to develop a system for helping people with physical impairments perform functional upper limb tasks such as eating, grasping and manipulating objects.The book first demonstrates procedures for modelling and identifying biomechanical models of the response of ES, covering a wide variety of aspects including mechanical support structures, kinematics, electrode placement, tasks, and sensor locations. It then goes on to demonstrate how complex functional activities of daily living can be captured in the form of optimisation problems, and extends ES control design to address this case. It then lays out a design methodology, stability conditions, and robust performance criteria that enable control schemes to be developed systematically and transparently, ensuring that they can operate effectively in the presence of realistic modelling uncertainty, physiological variation and measurement noise
Point-to-point repetitive control of functional electrical stimulation for drop-foot
No full textDrop-Foot is a common problem resulting from a range of neurological conditions, and prevents normal leg swing during gait, leading to abnormal, inefficient motion with an increased risk of falling. It damages the quality of life of over 122,000 people in the US and 11,400 people in the UK every year. Functional electrical stimulation (FES) addresses drop-foot by artificially contracting the tibialis anterior, and has had considerable success both clinically and commercially. However current commercial controllers are open loop and have long set-up times. The few controllers in the research domain are predominantly open-loop, lack accuracy, and struggle with muscle delays, non-linearities and the onset of fatigue. More advanced controllers require extensive sensor data and/or are highly dependent on an identified model. Recent developments have shown model based controllers combined with learning can facilitate higher accuracy, however previous attempts employed batch-wise learning, and led to disjointed control signals. This paper applies repetitive control (RC) to drop-foot for the first time, facilitating a continuous, smooth process of learning with no resetting. To maximise performance, a comprehensive extension to the traditional RC framework is undertaken to enable only isolated time points to be tracked, improving robustness and reducing memory and communication requirements. Experimental data confirms that RC can achieve normal gait when applied to FES-assisted gait with no voluntary effort. The new ‘point-to-point’ RC framework outperformed traditional RC, while using only 5 data points per gait cycle and minimal control effort.</p
Robust ILC design with application to stroke rehabilitation
No full textIterative learning control (ILC) is a design technique which can achieve accurate tracking by learning over repeated task attempts. However, long-term stability remains a critical limitation to widespread application, and to-date robustness analysis has overwhelmingly considered structured uncertainties. This paper substantially expands the scope of existing ILC robustness analysis by addressing unstructured uncertainties, a widely used ILC update class, the presence of a feedback controller, and a general task description that incorporates the most recent expansions in the ILC tracking objective. Gap metric based analysis is applied to ILC by reformulating the finite horizon trial-to-trial feedforward dynamics into an equivalent along-the-trial feedback system, as well as deriving relationships to link their respective gap metric values. The results are used to generate a comprehensive design framework for robust control design of the interacting feedback and ILC loops. This is illustrated via application to rehabilitation engineering, an area where they meet an urgent need for high performance in the presence of significant modeling uncertainty
Experimental evaluation of iterative learning control algorithms for non-minimum phase plants
No full textThe purpose of this paper is two-fold, firstly it describes the development and modelling of an experimental test facility as a platform on which to assess the performance of Iterative Learning Control (ILC) schemes. This facility includes a non-minimum phase component. Secondly, P-Type, D-Type and phase-lead types of the algorithm have been implemented on the test-bed, results are presented for each method and their performance is compared. Although all the ILC strategies tested experience eventual divergence when applied to a non-minimum phase system, it is found that there is an optimum phase-lead ILC design that maximizes convergence and minimizes error. A general method of arriving at this phase-lead from knowledge of the plant model is described. A variety of filters have been applied and assessed in order to improve the overall performance of the algorithm
Multiple model adaptive control of functional electrical stimulation
Full text linkThis paper establishes the feasibility of multiple-model switched adaptive control to regulate functional electrical stimulation for upper limb stroke rehabilitation. An estimation-based multiple-model switched adaptive control (EMMSAC) framework for nonlinear time-invariant systems is described, and extensions are presented to enable application to time-varying Hammerstein structures that can accurately represent the stimulated arm. A principled design procedure is then developed to construct both a suitable set of candidate models from experimental data and a corresponding set of tracking controllers. The procedure is applied to a sample of able-bodied young participants to produce a general EMMSAC controller. This is then applied to a different sample of the population during an isometric nonvoluntary trajectory tracking task. The results show that it is possible to eliminate model identification while employing closed-loop controllers that maintain high performance in the presence of rapidly changing system dynamics. This paper hence addresses critical limitations to effective stroke rehabilitation in a clinical setting
Norm optimal iterative learning control with intermediate point weighting: theory, algorithms and experimental evaluation
No full textThis brief considers the iterative learning control(ILC) problem when tracking is only required at a subset of isolated time points along the trial duration. It presents a norm optimal ILC solution to the problem with well-defined convergence properties, design guidelines, and supporting experimental results using an electromechanical test facility
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