The Modares Journal of Electrical Engineering
Volume:11 Issue: 4, 2012

In this paper a novel process monitoring scheme for reducing the type І and type ІІ error rates in the monitoring phase is proposed. First، the proposed approach uses an augmented data matrix to implement the process dynamic. Then، we apply independent component analysis (ICA) transformation to the augmented data matrix، and eliminate the outliers using the local outlier factor (LOF) algorithm. Finally، the control limit based on the LOF value of the cleaned data are obtained. In the monitoring phase، if the LOF value of each sample exceeds the control limit، fault has occurred; otherwise، data is normal. The proposed method is applied to fault detection in both a simple multivariate dynamic process and the Tennessee Eastman process. In both processes، type І and type ІІ error rates are witnessed to reduce by considering the process dynamic and performing the LOF algorithm. Results clearly indicate better performance of the proposed scheme compared to the alternative methods.

The present article investigates the application of high order TSK (Takagi Sugeno Kang) fuzzy systems in modeling photo voltaic (PV) cell characteristics. A method has been introduced for training second order TSK fuzzy systems using ANFIS (Artificial Neural Fuzzy Inference System) training method. It is clear that higher order TSK fuzzy systems are more precise approximators while they cover nonlinearities better than zero and first order systems with the same number of rules and input membership functions (MF). However existence of nonlinear terms of the rules’ consequent prohibits use of current available ANFIS algorithm codes as is. This article aims to give a simple method for employing ANFIS over a class of simplified second order TSK systems and applies the proposed method on the nonlinear problem of modeling PV cells. Error comparison shows that the proposed method trains the second order TSK system more effectively.

This paper introduces a technique for controlling a class of uncertain chaotic systems using an adaptive fuzzy Proportional-Integrator-Derivative (PID) controller with H∞ tracking performance. The purpose of this work is to achieve optimal tracking performance of the controller using Backtracking Search Algorithm (BSA). BSA، which is a novel heuristic algorithm، has an easy structure with single control parameter. In BSA، three basic genetic operators (selection، mutation and crossover) are utilized to generate trial individuals. To this reason، the control problem in hand is considered as an optimization problem by defining an appropriate objective function. Stability analysis of the control scheme is provided based on Lyapunov theory and modified Riccati-like equation، where the robustness of the closed-loop system is guaranteed by H∞ tracking performance for any predefined level. To evaluate the performance of the proposed control method، it is employed for tracking control of Duffing uncertain chaotic system. Simulation results show the capability of the proposed controller.

This paper introduces an indirect adaptive fuzzy sliding mode controller as a power system stabilizer for damping local and inter-area modes of oscillations of multi-machine power systems. This controller is designed based on the combination of sliding mode controller and the fuzzy logic systems. The fuzzy systems are used to approximate the unknown functions of power system model. Generator speed deviation and accelerator power are selected as fuzzy logic system inputs. A new sliding mode control law achieved by changing the sliding condition and the undesirable chattering has been removed by using of a continuous function. Based on the Lyapunov synthesis، adaptation laws are developed. Performance of the proposed stabilizer is studied for a two-area four-machine power system. Simulation results show the effectiveness of the proposed controller in comparison with multi-band power system stabilizer (MB-PSS)، classical adaptive fuzzy sliding mode stabilizer and adaptive fuzzy sliding mode stabilizer with a proportional integral function (PI).

This paper addresses adaptive observer design problem for joint estimation of the states and unknown parameters for a class of nonlinear systems which satisfying one-sided Lipschitz and quadratic inner bounded conditions. It’s shown that the stability of the proposed observer is related to finding solutions to a quadratic inequality consists of state and parameter errors. A coordinate transformation is used to reformulate this inequality as a linear matrix inequality (LMI). Sufficient conditions that ensure the existence of adaptive observer are expressed in forms of LMIs، which are easily tractable via standard software algorithms. If the proposed conditions are satisfied، then the state estimation errors are guaranteed to converge to zero asymptotically while، the convergence of the parameters is guaranteed when a persistence of excitation condition is held. The effectiveness of the proposed method is shown by simulation for the joint estimation of states and parameters of a numerical system.

This paper proposes a new method of gain scheduling control design for a nonlinear system which is described as linear parameter varying form. A performance measure based on Linear Matrix Inequality is introduced. To consider stability and performance measures in design process، the H∞ loop-shaping method is used to design the local controllers، which can be described as state feedback observer based structure. By introducing the stability and performance covering condition for the linear parameter varying system، a new interpolation law is proposed، and it is proofed that the resultant controller can preserve the performance measure for the observer based structure for all values of the scheduling parameter. Also the closed loop stability is guaranteed. The method is successfully applied on the control of a well-known benchmark system، namely، the autopilot for a pitch-axis model of an air vehicle. The performance and effectiveness is evaluated against disturbances and parameter uncertainties using computer simulation.