Addressed here is the problem of designing a dynamic controller capable of performing rest-to-rest maneuvers for flexible spacecraft, by using attitude measures. This controller does not need the knowledge of modal variables and spacecraft angular velocity. The absence of measurements of these variables is compensated by appropriate dynamics of the controller, which supplies their estimates. The Lyapunov technique is applied in the design of this dynamic controller. Possible source of instability of the controlled system in real cases are the influence of the flexibility on the rigid motion, the presence of disturbances acting on the structure, and parameter variations. In order to attenuate their effects and to damp out undesirable vibrations affecting the spacecraft attitude, distributed piezoelectric actuators are used. In fact, in presence of disturbances and/or parameter variation the proposed controller ensures an approximate solution of the control problem.

In this paper, a sensorless control scheme is presented for induction motors with core loss. First, a controller is designed using a high order sliding mode twisting algorithm, to track a desired rotor velocity signal and an optimal rotor flux modulus, minimizing the power loss in copper and core. Then, a super-twisting (ST) sliding mode observer for stator current is designed and the rotor flux is calculated, by means of the equivalent control method. Two methods for the rotor velocity estimation are then proposed. The first consists of a further super-twisting sliding mode observer for rotor fluxes, with the purpose of retrieving the back-electromotive force components by means of the equivalent control method. These components are functions of the rotor velocity which, hence, can be easily determined. The second method is based on a generalization of the phase-locked loop methodology. Finally, a simple Luenberger observer is designed, filtering the rotor velocity estimate and giving also an estimate of the load torque. The performance of the motor is verified by means of numeric simulations and experimental tests, where good tracking results are obtained.

We present a new technique for detection of epileptiform activity in EEG signals. After preprocessing of EEG signals we extract representative features in time, frequency and time-frequency domain as well as using nonlinear analysis. The features are extracted in a few frequency sub-bands of clinical interest since these sub-bands showed much better discriminatory characteristics compared with the whole frequency band. Then we optimally reduce the dimension of feature space to two using scatter matrices. A decision about the presence of epileptiform activity in EEG signals is made by quadratic classifiers designed in the reduced two-dimensional feature space. The accuracy of the technique was tested on three sets of electroencephalographic (EEG) signals recorded at the University Hospital Bonn: surface EEG signals from healthy volunteers, intracranial EEG signals from the epilepsy patients during the seizure free interval from within the seizure focus and intracranial EEG signals of epileptic seizures also from within the seizure focus. An overall detection accuracy of 98.7% was achieved.

The electroencephalogram (EEG) signal is very important in the diagnosis of epilepsy. Long-term EEG recordings of an epileptic patient contain a huge amount of EEG data. The detection of epileptic activity is, therefore, a very demanding process that requires a detailed analysis of the entire length of the EEG data, usually performed by an expert. This paper describes an automated classification of EEG signals for the detection of epileptic seizures using wavelet transform and statistical pattern recognition. The decision making process is comprised of three main stages: (a) feature extraction based on wavelet transform, (b) feature space dimension reduction using scatter matrices and (c) classification by quadratic classifiers. The proposed methodology was applied on EEG data sets that belong to three subject groups: (a) healthy subjects, (b) epileptic subjects during a seizure-free interval and (c) epileptic subjects during a seizure. An overall classification accuracy of 99% was achieved. The results confirmed that the proposed algorithm has a potential in the classification of EEG signals and detection of epileptic seizures, and could thus further improve the diagnosis of epilepsy.