Gerasimos Rigatos

Spin-Orbit Interaction in particles' motion as a model of quantum computation

02/2011;

The paper studies spin-orbit interaction (i.e. the effect the spin has on the particle's trajectory in a magnetic field) as a model of quantum computation. The two-level spin quantum system is examined using the stochastic mechanics formulation. The control of the entangled spin state is conside... [more] The paper studies spin-orbit interaction (i.e. the effect the spin has on the particle's trajectory in a magnetic field) as a model of quantum computation. The two-level spin quantum system is examined using the stochastic mechanics formulation. The control of the entangled spin state is considered as a problem of control of the mean moment of a particles ensemble along a reference trajectory. It is shown that such a control can be succeeded by applying an open-loop control scheme.

1.00

Impact points

Particle and Kalman filtering for state estimation and control of DC motors.

Gerasimos G Rigatos

ISA transactions. 12/2008;

State estimation is a major problem in industrial systems. To this end, Gaussian and nonparametric filters have been developed. In this paper the Kalman Filter, which assumes Gaussian measurement noise, is compared to the Particle Filter, which does not make any assumption on the measurement noise d... [more] State estimation is a major problem in industrial systems. To this end, Gaussian and nonparametric filters have been developed. In this paper the Kalman Filter, which assumes Gaussian measurement noise, is compared to the Particle Filter, which does not make any assumption on the measurement noise distribution. As a case study the estimation of the state vector of a DC motor is used. The reconstructed state vector is used in a feedback control loop to generate the control input of the DC motor. In simulation tests it was observed that for a large number of particles the Particle Filter could succeed in accurately estimating the motor's state vector, but at the same time it required higher computational effort.

2.99

Impact points

ADAPTIVE FUZZY CONTROL WITH OUTPUT FEEDBACK FOR H(infinity) TRACKING OF SISO NONLINEAR SYSTEMS.

Gerasimos G Rigatos

International journal of neural systems. 09/2008; 18(4):305-20.

Observer-based adaptive fuzzy H(infinity) control is proposed to achieve H(infinity) tracking performance for a class of nonlinear systems, which are subject to model uncertainty and external disturbances and in which only a measurement of the output is available. The key ideas in the design of the ... [more] Observer-based adaptive fuzzy H(infinity) control is proposed to achieve H(infinity) tracking performance for a class of nonlinear systems, which are subject to model uncertainty and external disturbances and in which only a measurement of the output is available. The key ideas in the design of the proposed controller are (i) to transform the nonlinear control problem into a regulation problem through suitable output feedback, (ii) to design a state observer for the estimation of the non-measurable elements of the system's state vector, (iii) to design neuro-fuzzy approximators that receive as inputs the parameters of the reconstructed state vector and give as output an estimation of the system's unknown dynamics, (iv) to use an H(infinity) control term for the compensation of external disturbances and modelling errors, (v) to use Lyapunov stability analysis in order to find the learning law for the neuro-fuzzy approximators, and a supervisory control term for disturbance and modelling error rejection. The control scheme is tested in the cart-pole balancing problem and in a DC-motor model.

Stochastic Processes in Machine Intelligence: Neural Structures Based on the Model of the Quantum Harmonic Oscillator

Gerasimos G. Rigatos

Quantum, Nano and Micro Technologies, 2008 Second International Conference on; 03/2008

This paper studies neural structures with weights that follow the model of the quantum harmonic oscillator. The proposed neural networks have stochastic weights which are calculated from the solution of Schrodinger's equation under the assumption of a parabolic (harmonic) potential. These weight... [more] This paper studies neural structures with weights that follow the model of the quantum harmonic oscillator. The proposed neural networks have stochastic weights which are calculated from the solution of Schrodinger's equation under the assumption of a parabolic (harmonic) potential. These weights correspond to diffusing particles, which interact to each other as the theory of Brownian motion (Wiener process) predicts. It is shown that conventional neural networks and learning algorithms based on error gradient can be conceived as a subset of the proposed quantum neural structures. The learning of the stochastic weights (convergence of the diffusing particles to an equilibrium) is analyzed. In the case of associative memories the proposed neural model results in an exponential increase of patterns storage capacity (number of attractors).

Fuzzy model validation using the statistical local approach

G. Rigatos, Q. Zhang

Systems, Man and Cybernetics, 2002 IEEE International Conference on; 11/2002

Not Available

Fuzzy model validation using the local statistical approach

G. Rigatos, Q. Zhang

Fuzzy Sets and Systems.

The local statistical approach for fault detection and isolation is applied to fuzzy models validation. The method detects the inconsistencies between a fuzzy rule base and the modelled system. It can also identify which are the faulty parameters of the fuzzy model. The Fisher information matrix exp... [more] The local statistical approach for fault detection and isolation is applied to fuzzy models validation. The method detects the inconsistencies between a fuzzy rule base and the modelled system. It can also identify which are the faulty parameters of the fuzzy model. The Fisher information matrix explains the detectability of changes in the parameters of the fuzzy model. Simulation tests illustrate the method's credibility.

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