Alexander G. Loukianov

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This paper deals with adaptive tracking for discrete-time multiple-input-multiple-output (MIMO) nonlinear systems in presence of bounded disturbances. In this paper, a high-order neural network (HONN) structure is used to approximate a control law designed by the backstepping technique, applied to a block strict feedback form (BSFF). This paper also(More)
—In this note, we propose a solution to the well-know problem of ensuring a simultaneous globally convergent online estimation of the state and the frequencies of a sinusoid signal composed of sinusoidal terms. We present an estimator which guarantees global boundedness and convergence of the state and frequencies estimation for all initial conditions and(More)
In this paper, the authors propose a particle swarm optimization (PSO) for a discrete-time inverse optimal control scheme of a doubly fed induction generator (DFIG). For the inverse optimal scheme, a control Lyapunov function (CLF) is proposed to obtain an inverse optimal control law in order to achieve trajectory tracking. A posteriori, it is established(More)
—In this paper, a controller for induction motors is proposed. A continuous feedback is first applied to obtain a discrete-time model in closed form. Then, on the basis of these exact sampled dynamics, a discrete-time controller ensuring speed and flux modulus reference tracking is determined, making use of the sliding mode technique. The resulting(More)
—Based on the complete model of the plant, a sliding-mode stabilizing controller for synchronous generators is designed. The block control approach is used in order to derive a nonlinear sliding surface, on which the mechanical dynamics are linearized. This combined approach enables us to compensate the inherent nonlinearities of the generator and to reject(More)
A nonlinear discrete-time neural observer for discrete-time unknown nonlinear systems in presence of external disturbances and parameter uncertainties is presented. It is based on a discrete-time recurrent high-order neural network trained with an extended Kalman-filter based algorithm. This brief includes the stability proof based on the Lyapunov approach.(More)