The development of a training system in the field of rehabilitation has always been a challenge for scientists. Surface electromyographical signals are widely used as input signals for upper limb prosthetic devices. The great mental effort of patients fitted with myoelectric prostheses during the training stage, can be reduced by using a simulator of such device. This paper presents an architecture of a system able to assist the patient and a classification technique of surface electromyographical signals, based on neural networks. Four movements of the upper limb have been classified and a rate of recognition of 96.67% was obtained when a reduced number of features were used as inputs for a feed-forward neural network with two hidden layers.
Cranes are used to move heavy cargo. While they are in general controlled by a human operator, automated systems are able to obtain more precise control. In this paper, we design a Takagi-Sugeno (TS) fuzzy controller for the crane. For this, first a TS fuzzy model of the crane is developed, and a TS observer is used to estimate the unmeasurable states. The observer is tested in simulation and on a laboratory-scale 3D crane, while the controller is tested in simulation.
In this paper we consider observer design for discrete-time switching systems represented by Takagi-Sugeno fuzzy models. For the design we use a switching Lyapunov function defined on the switches. In order to develop stability conditions for the estimation error dynamics, we consider the variation of this function along possible switches. The conservativeness of the approach is reduced by considering the α-sample variation of the Lyapunov function. This approach can bring solutions to observer design for some switching systems with unobservable and unstable local models. The developed conditions are illustrated on a numerical example.
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