1,721,373 research outputs found
Constrained Point-to-Point Iterative Learning Control with Experimental Verification
No full textIterative learning control is a methodology applicable to systems which perform the same operation repeatedly, where the task is the tracking of a specified reference trajectory defined over a finite time duration. Here the methodology is instead applied to the point-to-point motion control problem in which the output is only specified at a subset of time instants. The iterative learning framework is expanded to address this case, and it is shown how the extra design freedom this set-up brings allows additional input, output and state constraints to be addressed. Experimental results illustrate the practical benefits of the proposed approach, and confirm the performance and accuracy that can be achieved
Dataset for Robust ILC design for electrical stimulation in upper limb rehabilitation
No full textPercentage error data supporting:
Freeman, C. T. (2017). Robust ILC design for electrical stimulation in upper limb rehabilitation. Automatica.</span
Freeman Christopher, Jahoda Marie — Models of doom. A critique of the limits to growth
No full textPaillat Paul. Freeman Christopher, Jahoda Marie — Models of doom. A critique of the limits to growth. In: Population, 31ᵉ année, n°6, 1976. p. 1327
Multiple Model Switched Repetitive Control
No full textA multiple model switched repetitive control (RC) framework is developed for a general class of system and widely used RC update structure. This guarantees stability and robust performance under the assumption that the true plant model belongs to a plant uncertainty set specified by the designer. A comprehensive design procedure for the candidate model set and RC update is presented based on novel application of gap metric analysis to RC, and switching of the corresponding RC schemes is achieved efficiently using a bank of Kalman filters
Parametrised Function ILC with application to FES Electrode Arrays
No full textFunctional electrical stimulation (FES) is an effective approach to regain lost movement in paralysed or impaired subjects. FES arrays can achieve functional multi-joint angular motion by activating a large number of FES elements. However, their control is challenging due to the need for high precision but the lack of a model or available identification time in a clinical or home setting. This paper develops an approach to deliver high accuracy with minimal identification overhead. It is based on iterative learning control (ILC), a technique that exploits the repeated nature of rehabilitation training. It uses a parameterised model form that adapts to new data and replaces identification tests while maintaining accuracy. Results show that 4 references can be tracked using only 10.8% of the experimental tests required by conventional ILC approaches
A decentralised iterative learning control framework for collaborative tracking
No full textCollaborative tracking control involves two or more subsystems working together to perform a global objective, and is increasingly used within a diverse range of applications. Decentralised iterative learning control schemes have demonstrated highly accurate collaborative tracking by using past experience gained over repeated attempts at the task. However they impose highly restrictive constraints on the system dynamics, and their reliance on inverse dynamics has degraded their robustness to model uncertainty.This paper proposes the first general decentralised iterative learning framework to address this problem, thereby enabling a wide range of existing iterative learning control methodologies to be applied in a decentralised manner to collaborative subsystems. This framework is illustrated through the derivation of a variety of new decentralised iterative learning control algorithms which balancecollaborative tracking performance with optimisation of a general objective function. The framework is illustrated by application to wearable stroke rehabilitation technology in which each subsystem is a muscle artificially activated by electrical stimulation. These verify the framework’s simplified design and reduced hardware and communication overheads
Iterative Learning Control with Mixed Constraints for Point-to-Point Tracking
No full textIterative learning control is concerned with tracking a reference trajectory defined over a finite time duration, and is applied to systems which perform this action repeatedly. However, in many application domains the output is not critical at all points over the task duration. In this paper the facility to track an arbitrary subset of points is therefore introduced, and the additional flexibility this brings is used to address other control objectives in the framework of iterative learning. These comprise hard and soft constraints involving the system input, output and states. Experimental results using a robotic arm confirm that embedding constraints in the ILC framework leads to superior performance than can be obtained using standard ILC and an a priori specified reference
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