Paula Raica

Classification of surface electromyographic signals for control of upper limb virtual prosthesis using time-domain features

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.

Hierarchical myoelectric control of a human upper limb prosthesis

Myolectric control is nowadays the most used approach for electrically-powered upper limb prostheses. The myoelectric controllers use electromyographic (EMG) signals as inputs. These signals can be collected from the skin surface using surface EMG sensors, or intramuscular, using needle sensors. No matter which method is used, they have to be processed before being used as controller inputs. In this paper, we present an algorithm based on an autoregressive (AR) model representation and a neural network, for EMG signal classification. The results have shown that combining a low-order AR model with a feed-forward neural network, a rate of classification of 98% can be achieved, while keeping the computational cost low. We also present a hierarchical control architecture and the implementation of the high-level controller using Finite State Machine. The solution proposed is capable of controlling three joints (i.e. six movements) of the upper limb prosthesis. The inputs of the high-level controller are obtained from the classifier, while its outputs are applied as input signals for the low-level controller. The main advantage of the proposed strategy is the reduced effort required to the patient for controlling the prosthetic device, since he only has to initiate the movement that is finalized by the low-level part of the controller.

Fuzzy Modeling and Design for a 3D Crane.

Abstract 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.

Observer design for switching nonlinear systems

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 conser-vativeness 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.

On stabilization of discrete-time periodic TS systems

In this paper we consider controller design for periodic Takagi-Sugeno fuzzy models. For this, we use a periodic nonquadratic Lyapunov function defined at the time instants when the subsystems switch. Using the proposed conditions we are able to handle periodic Takagi-Sugeno systems where the local models or even the subsystems are unstable or cannot be stabilized. The application of the conditions is illustrated on numerical examples.

Finding a stabilising switching law for switching nonlinear models

This paper considers the stabilisation of switching nonlinear models by switching between the subsystems. We assume that arbitrary switching between two subsystems is possible once a subsystem has been active for a predefined number of samples. We use a Takagi–Sugeno representation of the models and a switching Lyapunov function is employed to develop sufficient stability conditions. If the conditions are satisfied, we construct a switching law that stabilises the system. The application of the conditions is illustrated in several examples.

Observer Design for Discrete-Time Switching Nonlinear Models

Switched systems are often described by continuous and discrete dynamics as well as their interactions. Although results are available for linear switching systems, for nonlinear switching models few results exist. In this chapter, we consider observer design for discrete-time switching nonlinear systems with a Takagi–Sugeno representation. For designing the observers, a switching nonquadratic Lyapunov function is used. Such Lyapunov functions have shown a real improvement of the design conditions for discrete-time Takagi–Sugeno models. The Lypunov function can be defined for each subsystem or just for the moments when switching takes place. In the first case the results are more general, but also more conservative. The second case represents a significant improvement for periodic models. Thanks to the Lyapunov function used, it is possible to design observers for some switching systems with unobservable subsystems. The developed conditions are formulated as linear or bilinear matrix inequalities. Their advantages and shortcomings are illustrated on numerical examples.

Analysis and design for continuous-time string-connected Takagi-Sugeno systems

Abstract Distributed systems consist of interconnected, lower-dimensional subsystems. For such systems, distributed analysis and design present several advantages, such as modularity, easier analysis and design, and reduced computational complexity. A special case of distributed systems is when the subsystems are connected in a string. Applications include distributed process control, traffic and communication networks, irrigation systems, hydropower valleys, etc. By exploiting such a structure, in this paper, we propose conditions for the distributed stability analysis of Takagi–Sugeno fuzzy systems connected in a string. These conditions are also extended to observer and controller design and illustrated on numerical examples.

Nonquadratic stabilization of switching TS systems

Abstract In this paper we consider stabilization of switching nonlinear systems represented by TS models. To develop the conditions we use two different switching Lyapunov functions. For each Lyapunov function a set of conditions is developed. The conditions are formulated as LMIs and relaxed using delays in the controller and the Lyapunov function. The application of the conditions is illustrated on numerical examples.

Stability analysis of switching TS models using α-samples approach

In this paper we consider stability analysis of discrete-time switching systems represented by Takagi-Sugeno fuzzy models. For the analysis we use a switching Lyapunov function defined on the switches. In order to develop stability conditions, we consider the variation of this function along switching paths of length α, i.e., an α sample variation of the Lyapunov function. This approach is able to determine stability of a switching system containing unstable local models. The developed conditions can be formulated as LMIs and they are illustrated on a numerical example.