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It is well known that sliding-mode control can give good transient performance and system robustness. However, the presence of chattering may introduce problems to the actuators. Many chattering elimination methods use a finite dc gain controller which leads to a finite steady-state error. One method to ensure zero steady-state error is using a proportional plus integral (PI) controller. This paper proposes a fuzzy logic controller which combines a sliding-mode controller (SMC) and a PI controller. The advantages of the SMC and the PI controller can be combined and their disadvantages can be removed. The system stability is proved, although there is one more state variable to be considered in the PI subsystem. An illustrative example shows that good transient and steady-state responses can be obtained by applying the proposed controller. Index Terms--Combining controllers, fuzzy sliding, Lyapunov, stability.

This paper addresses the optimization and stabilization problems of nonlinear systems subject to parameter uncertainties. The methodology is based on a fuzzy logic approach and an improved genetic algorithm (GA). The TSK fuzzy plant. model is employed to describe the dynamics of the uncertain nonlinear plant. A. fuzzy controller is then obtained to close the feedback Loop. The stability conditions are derived. The feedback gains of the fuzzy controller and the solution for meeting the stability conditions are determined using the improved GA. In order to obtain the optimal fuzzy controller, the membership functions are further tuned by minimizing a defined fitness function using the improved GA. An application example oil stabilizing a two-link robot arm will be given. [ABSTRACT FROM AUTHOR]

Electric load forecasting is essential to improve the reliability of the ac power line data network and provide optimal load scheduling in an intelligent home system. In this paper, a short-term load forecasting realized by a neural fuzzy network (NFN) and a modified genetic algorithm (GA) is proposed. It can forecast the hourly load accurately with respect to different day types and weather information. By introducing new genetic operators, the modified GA performs better than the traditional GA under some benchmark test functions. The optimal network structure can be found by the modified GA when switches in the links of the network are introduced. The membership functions and the number of rules of the NFN can be obtained automatically. Results for a short-term load forecasting will be given. [ABSTRACT FROM AUTHOR]

Highlights the design of fuzzy logic controllers for use in nonlinear systems. Details on the stability of the design; Information on the combination of conventional controllers; Detailed information on the robustness of fuzzy logic controllers.