Hussain N. Al-Duwaish

A new method for the identification of Hammerstein model

Abstract A new method for the identification of the nonlinear Hammerstein model, consisting of a static nonlinear part in cascade with a linear dynamic part, is introduced. The static nonlinear part is modeled by a multilayer feedforward neural network (MFNN), and the linear part is modeled by an autoregressive moving average (ARMA) model. A recursive algorithm is developed for estimating the weights of the MFNN and the parameters of ARMA model. Simulation examples are included to illustrate the performance of the proposed method.

A new load frequency variable structure controller using genetic algorithms

Abstract In this paper, selection of the variable structure controller (VSC) feedback gains by genetic algorithms (GA) is presented contrary to the trial and error selection of the variable structure feedback gains reported in the literature. This is considered as one of the main underlying problems associated with VSC. The proposed method provides an optimal and systematic way of feedback gains selection in the VSC. To test the effectiveness of the new selection method, the proposed design has been applied to the load frequency problem of a single area power system. The system performance against step load variations has been simulated and compared to some previous methods, Simulation results show that not only the dynamic system performance has been improved, but also the control effort is dramatically reduced.

Nonlinear model predictive control of Hammerstein and Wiener models using genetic algorithms

Model predictive control or MPC can provide robust control for processes with variable gain and dynamics, multivariable interaction, measured loads and unmeasured disturbances. In this paper a novel approach for the implementation of nonlinear MPC is proposed using genetic algorithms (GAs). The proposed method formulates the MPC as an optimization problem and genetic algorithms are used in the optimization process. Application to two types of nonlinear models namely Hammerstein and Wiener Models is studied and the simulation results are shown for the case of two chemical processes to demonstrate the performance of the proposed scheme.

A genetic approach to the identification of linear dynamical systems with static nonlinearities

This paper investigates the use of genetic algorithms in the identification of linear systems with static nonlinearitites. Linear systems with static nonlinearities at the input known as the Hammerstein model, and linear systems with static nonlinearities at the output known as the Wiener model are considered in this paper. The parameters of the Hammerstein and the Wiener models are estimated using genetic algorithms from the input-output data by minimizing the error between the true model output and the identified model output. Using genetic algorithms, the Hammerstein and the Wiener models with known nonlinearity structure and unknown parameters can be identified. Moreover, systems with non-minimum phase characteristics can be identified. Extensive simulations have been used to study the convergence properties of the proposed scheme. Simulation examples are included to demonstrate the effectiveness and robustness of the proposed identification scheme.

Hammerstein model identification by multilayer feedforward neural networks

A new method for the identification of the nonlinear Hammerstein model, consisting of a static linearity in cascade with a linear dynamic part, is introduced. The static nonlinearity is modelled by a multilayer feedforward neural network (MFNN) and the linear part is modelled by an autoregressive moving average (ARMA) model. A recursive algorithm is developed to update the weights of the MFNN and the parameters of the ARMA. The new method makes use of the well-known nonlinear mapping ability of MFNN and avoids the restrictive assumptions of the previous identification methods. Two numerical examples are presented to illustrate the performance of the developed model and recursive algorithm.

Variable structure load frequency controller using particle swarm optimization technique

In this paper, selection of the variable structure controller feedback gains by Particle Swarm Optimization (PSO) technique is presented contrary to the trial and error selection of the variable structure feedback gains reported in literature. The proposed design has been applied to the load frequency problem of a single area power system. The system performance against a step load variations has been simulated and compared to some previous methods. Simulation results show that not only dynamic system performance has been improved, but also the control effort is reduced. The results show the reliability of the proposed technique.

A neural network-based approach for on-line dynamic stability assessment using synchronizing and damping torque coefficients

Abstract This paper presents an artificial neural network (ANN)-based on-line approach to evaluate the dynamic stability of a single machine infinite bus system. The proposed on-line assessment scheme is based on estimating the synchronizing and damping torque coefficients as dynamic performance indices. The two performance indices are estimated from on-line measurements of the changes in the rotor angle, speed and electromagnetic torque using a three-layer feedforward neural network with back propagation. The results show that the proposed method is very promising and encouraging for fast real-time evaluation of the dynamic performance of power systems.

On the Design of Variable Structure Load Frequency Controllers by Tabu Search Algorithm: Application to Nonlinear Interconnected Models

A variable structure controller (VSC) with chattering reduction feature applied to interconnected load frequency control (LFC) problem is presented. Formulating the design of VSC as an optimization problem and utilizing tabu search (TS) algorithm provides a simple and systematic way of arriving at the optimal feedback gains and switching vector values of the controller. In addition, this will cut down the need for nonlinear or coordinate transformation as reported before. The tested interconnected LFC model incorporates nonlinearities in terms of generation rate constraint (GRC) and a limiter on the integral control value. In order to guarantee the enhancement of the system, dynamical performance and chattering reduction of the VSC, different objective functions were investigated in the optimization process. In addition, the complexity of the controller is reduced by using only the accessible states in designing the VSC. Comparison with previous LFC methods reported in literature validates the significance of the proposed VSC design.

Adaptive Output Feedback Controller for Wind Turbine Generators Using Neural Networks

An adaptive output feedback controller using neural networks for improving the dynamic stability of a wind turbine generator supplying an infinite bus through a transmission line under widely varying conditions is presented. The need for adaptive output feedback comes from the fact that the wind turbine operates over a range of operating points, some of which are unstable; hence, no single output feedback controller gains are sufficient for the entire operating range. Neural networks are used for on-line prediction of the suitable gains for the output controller when the operating point changes. Simulation results are included to demonstrate the performance of the proposed control scheme.

A new variable structure DC motor controller using genetic algorithms

This paper presents a new application of the genetic algorithm for the selection of the variable structure controller (VSC) feedback gains and switching vector for a separately excited DC motor. Contrary to the VSC design methods reported in the literature, the method provides an optimal and systematic design procedure for the selection of the feedback gains and switching vector. By the proposed VSC controller, the speed of a tested DC motor follows a pre-determined speed track to a high degree of accuracy. The proposed controller has been found to be robust against high variations in the motor parameters.