the aim of this paper is to investigate a novel approach for output feedback damping controller design ofstatcom in order to enhance the damping of power system low frequency oscillations (lfo). the design ofoutput feedback controller is considered as an optimization problem according with the time domain-basedobjective function which is solved by a honey bee mating optimization algorithm (hbmo...

In this paper, we present a neural-network (NN) based approach to represent a nonlinear Tagagi-Sugeno (T-S) system. A linear differential inclusion (LDI) state-space representation is utilized to deal with the NN models. Taking advantage of the LDI representation, the stability conditions and controller design are derived for a class of nonlinear structural systems. Moreover, the concept of uti...

In this paper, an almost new control approach called terminal synergetic control which works based on user defined manifold is applied to a nonlinear helicopter model. Stability analysis is convestigated using Lyapunov stability theory. Synergetic controller is applied to this nonlinear fifth-order helicopter model to control height and angle. Simulation results showed that it has faster and sm...

BP artificial neural network is a non-feedback network. This paper utilizes the initial weights of neural network to choose controller performance. Simultaneously according to the characteristics that process of central air-conditioning energy saving control is the system of multi-parameter and nonlinear time-varying complexity, we analysis and study its algorithm and system architecture. The e...

The nonlinear load such as UPS, fan controller, mobile charger is distorting the supply current waveform. This current harmonics derates the power system equipment. To avoid this current harmonics, filters namely passive filter / active filter /hybrid filters are introduced in the supply line. In this paper the overall view of the performance of the various filters are studied and the performan...

This paper deals with the constrained predictive control of nonlinear systems. Artificial Neural Networks (ANN) are used as a process model. The control law is derived by minimizing a non convex criterion. The optimization problem is solved using Ellipsoid and genetic algorithms. The structure and operators of the combining two algorithms have been specifically developed for control design prob...

In this paper, a novel nonlinear model predictive controller (MPC) is proposed based on an identified nonlinear parameter varying (NPV) model. First, an NPV model scheme is present for process identification, which is featured by its nonlinear hybrid Hammerstein model structure and varying model parameters. The hybrid Hammerstein model combines a normalized static artificial neural network with...