1,720,974 research outputs found
Multiple model switched repetitive control with application to tremor suppression
No full textTremor is a debilitating oscillation of the limbs that affects millions of people worldwide. Functional electrical stimulation (FES) can reduce tremor by artificially activating opposing muscles, and when mediated by repetitive control (RC), has potential to provide complete suppression. However, all previous RC applications have limited performance due to fatigue, spasticity and modelling error. This paper first applies gap metric analysis to derive robust stability margins for RC subject to model uncertainty. It then formulates a multiple model switched repetitive control (MMSRC) scheme with guaranteed robust performance bounds. Simulation results demonstrate that MMSRC effectively suppresses tremor with realistic levels of identification error, fatigue and spasticity, whereas conventional RC FES schemes are unstable.</p
Multiple model switched repetitive control for tremor suppression
No full textTremor is a condition that impacts millions of people globally, and is characterised by a periodic limb movement that impedes voluntary motion. Recent studies have shown that functional electrical stimulation (FES) can help reduce tremor by artificially stimulating opposing muscles, thereby decreasing the oscillation’s amplitude. Various control methods have been proposed for this purpose, but repetitive control (RC) has shown the most promise with potential to completely suppress the tremor. While several RC approaches have demonstrated suppression rates of up to 90%, they heavily rely on an accurate model of the underlying dynamics, and their effectiveness declines steeply due to factors like muscle fatigue, spasticity, and modelling inaccuracies.This paper introduces a multiple model switched repetitive control (MMSRC) framework that addresses the limitations of existing RC approaches. It guarantees high performance tremor suppression provided the true dynamics belong to an uncertainty set specified by the designer. This enables it to adapt to time-varying physiological changes, as well as changes in the placement of the FES electrodes. Moreover, once an uncertainty set has been established, it removes the need for subsequent model identification. This is an important step towards home-based tremor suppression where model identification and expert tuning are not possible. Experimental validation is performed with four participants, showing that MMSRC effectively suppresses tremor even in the presence of severe modelling uncertainty and fatigue, unlike conventional RC methods which often become unstable under these conditions
Iterative learning control of minimum energy path following tasks for second-order MIMO systems: an indirect reference update framework
No full textIn a large range of manufacturing tasks, the design objective is characterised as following a given path defined in space. In these applications, the tracking time of any particular position along the path is not specified, so an appropriate motion profile can be chosen among its admissible solutions to improve its tracking performance. This paper develops an indirect reference update framework that maximizes accuracy while embedding practical constraints. An optimal path planning problem, incorporating system constraints, is formulated and can be solved using a discretized approach to derive a motion profile that minimizes control energy for a broad spectrum of industrial tasks. To satisfy robustness concerns, an iterative learning control (ILC) algorithm with an indirect reference update framework is designed to improve the accuracy and robustness of path following. It is evaluated on a gantry robot test platform, and the results illustrate superior levels of practical performance in terms of energy reduction and path following accuracy compared with existing approaches
Predictive iterative learning control with experimental validation
Full text linkThis paper develops an iterative learning control law that exploits recent results in the area of predictive repetitive control where a priori information about the characteristics of the reference signal is embedded in the control law using the internal model principle. The control law is based on receding horizon control and Laguerre functions can be used to parameterize the future control trajectory if required. Error convergence of the resulting controlled system is analyzed. To evaluate the performance of the design, including comparative aspects, simulation results from a chemical process control problem and supporting experimental results from application to a robot with two inputs and two outputs are given
Multiple-model iterative learning control with application to stroke rehabilitation
No full textModel-based iterative learning control (ILC) algorithms achieve high accuracy but often exhibit poor robustness to model uncertainty, causing divergence and long-term instability as the number of trials increases. To address this, an estimation-based multiple-model switched ILC (EMMILC) approach is developed based on novel theorem results which guarantee stability if the true plant lies within a uncertainty space defined by the designer. Using gap metric analysis, EMMILC eliminates restrictive assumptions on the uncertainty structure assumed in existing multiple-model ILC methods. Our design framework minimises computational load while maximising tracking accuracy. Applied to a common rehabilitation scenario, EMMILC outperforms the standard ILC approaches that have been previously employed in this setting. This is confirmed by experimental tests with four participants where performance increased by 28%. EMMILC is the first model-based ILC framework that can guarantee high performance while not requiring any model identification or tuning, and paves the way for effective, home-based rehabilitation systems
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Multiple model iterative learning control with application to upper limb stroke rehabilitation
No full textFunctional electrical stimulation (FES) is an upper limb stroke rehabilitation technology that can enable patients to recover their lost movement by assisting functional task training. Unfortunately, current FES controllers cannot simultaneously satisfy the competing demands of high accuracy, robustness to modelling error and minimal set-up/identification time that are needed for clinical or home deployment. To address this, an estimation-based multiple model switched iterative learning control framework is proposed, combining the most successful adaptive and learning properties of existing FES controllers. A practical design procedure guaranteeing robust performance is developed, and initial experimental results are then presented to confirm efficacy of the approach
Iterative learning control of functional electrical stimulation in the presence of voluntary user effort
No full textWorldwide 17 million people are left with impairment to their upper or lower limb following stroke. Functional electrical stimulation (FES) is a method of artificially activating muscles using electrical pulses and is the most common rehabilitation technology. A significant body of clinical research confirms that successful rehabilitation requires FES to be applied in a way that supports voluntary intention during repeated attempts at functional tasks. Electromyography (EMG) measures the voluntary contraction of muscles and has been used to directly control FES in openloop, however it is limited by poor accuracy. On the other hand, model-based feedback control can provide high accuracy, but does not explicitly promote voluntary intention.A new dynamic model of the muscle activation, generated by combined voluntary nerve signals and FES, is developed in this paper. It includes both nonlinear recruitment and linear activation dynamics. An efficient identification procedure is then formulated which can be applied to people with stroke. A model-based hybrid EMG/FES control scheme is then derived based on the model structure, allowing tracking and volitional intention support to be simultaneously optimised for the first time. Exploiting the repeated nature of rehabilitation, the control framework is then extended to further improve tracking accuracy. That is achieved by learning from experience through iterative learning control. The framework is experimentally tested with results confirming it can deliver greater performance compared to existing FES approaches, which do not consider voluntary action in the model or controller
Identification of electrically stimulated muscle models of stroke patients
No full textDespite significant recent interest in the identification of electrically stimulated muscle models, current methods are based on underlying models and identification techniques that make them unsuitable for use with subjects who have incomplete paralysis. One consequence of this is that very few model-based controllers have been used in clinical trials. Motivated by one case where a model-based controller has been applied to the upper limb of stroke patients, and the modeling limitations that were encountered, this paper first undertakes a review of existing modeling techniques with particular emphasis on their limitations. A Hammerstein structure, already known in this area, is then selected, and a suitable identification procedure and set of excitation inputs are developed to address these short-comings. The technique that is proposed to obtain the model parameters from measured data is a combination of two iterative schemes: the first of these has rapid convergence and is based on alternating least squares, and the second is a more complex method to further improve accuracy. Finally, experimental results are used to assess the efficacy of the overall approach
Generalized norm optimal iterative learning control: constraint handling
No full textThis paper proposes a novel control methodology to incorporate constraint handling within generalized iterative learning control (ILC), an overarching methodology which includes intermediate point and sub-interval tracking as special cases. The constrained generalized ILC design objective is first described, and then the design problem is formulated into a successive projection framework. This framework yields a constrained generalized ILC algorithm which embeds system input and output constraints. Convergence analysis of the algorithm is performed and supported by rigorous proofs. The algorithm is verified using a gantry robot experimental platform, whose results reveal its practical efficacy and robustness against plant uncertainty
Spatial path tracking using iterative learning control
No full textThis paper proposes a novel control methodology to enable accurate tracking of a path profile defined in output space. No temporal requirement is specified on this movement a priori, and the proposed framework enforces path tracking while minimizing an additional objective function. The problem is solved by formulating the problem as a constrained optimization involving simultaneous spatial tracking constraints and temporal via-point constraints. Practical implementation is via a two stage iterative learning control algorithm based on norm optimal and gradient updates which embeds robustness to plant uncertainty. The algorithm is verified using a gantry robot experimental platform, whose results reveal practical efficacy
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