Jang-Hyun Park

Robust adaptive fuzzy controller for nonlinear system using estimation of bounds for approximation errors

This paper describes the design of the robust adaptive fuzzy controller for uncertain single-input single-output nonlinear dynamical systems with unknown nonlinearities. These unknown nonlinearities are approximated by the fuzzy system with a set of fuzzy IF-THEN rules whose parameters are adjusted on-line according to some adaptive laws for the purpose of controlling the output of the nonlinear system to track a given trajectory. The Lyapunov synthesis approach is used to develop a state-feedback adaptive control algorithm based on the adaptive fuzzy model. The key assumption is that the reconstruction errors satisfy certain bounding conditions. The control law consists of two terms. One is the certainty equivalent control and the other is the bounding control. A bounding parameter adaptive law is used to obtain this bounding control. The overall control system guarantees that the tracking error converges in the small neighborhood of zero and that all signals involved are uniformly bounded. It is also shown that, in the special case, the tracking error exponentially converges to zero even though the approximation errors exist.

Adaptive fuzzy control of nonaffine nonlinear systems using Takagi-Sugeno fuzzy models

We present a control method for a general nonlinear systems using Takagi-Sugeno fuzzy models and develop an adaptation law to adjust the parameters of the fuzzy system. It is proved that the closed-loop system is stable in the sense of Lyapunov and all signals including tracking error, fuzzy parameters and estimate of upper bound for approximation error are bounded. Finally, simulation results verify the effectiveness of the proposed control method.

Robustly stable fuzzy controller for uncertain nonlinear systems with unknown input gain sign

We propose and analyze a robust adaptive fuzzy controller for uncertain nonlinear systems without information on the input gain sign. The proposed scheme completely overcomes the singularity problem which occurs in the indirect adaptive feedback linearizing control. No projection in the estimated parameters and no switching in the control input are needed. The stability of the closed-loop system is guaranteed in the Lyapunov viewpoint.

Robust adaptive observer using fuzzy systems for uncertain nonlinear systems

This paper describes the design of a robust adaptive fuzzy observer for uncertain nonlinear dynamical system. The Lyapunov synthesis approach is used to guarantee a uniform ultimate boundedness property of the state estimation error, as well as of all other signals in the closed-loop system. Especially, we have focused on the realization of minimal dynamic order of the observer. For the purpose, we propose a new method in which no strictly proper (SPR) condition is needed and combine dynamic rule insertion scheme with on-line estimation of fuzzy parameters. No a priori knowledge of upper bounds on the optimal parameters and modeling errors is required. The theoretical results are illustrated through a simulation example.

Design of a robust stable speed-sensorless induction motor direct torque control system using the RBFN

The objective of this paper is design of a robust stable speed controller for speed-sensorless induction motor direct torque control (DTC) systems. Overall control systems are composed of a speed estimator and the proposed uncertainty observer using the radial basis function networks (RBFN). The induction motor systems in the real industrial fields, the obtaining of an exact mathematical model is hardly difficult due to the unmodeled uncertainties such as parametric uncertainties and external load disturbances. In this paper, the uncertainties are approximated by the RBFN, and the control algorithm is applied to the DTC system. Control laws and adaptive laws for the bounding constant and weights in the output layer of the RBFN are established so that the whole closed loop system is stable in the sense of Lyapunov. The proposed control algorithm is relatively simple and requires no restrictive conditions on the design constants for the stability. Simulation results show the effectiveness and validity of the proposed control algorithm.

Robust adaptive controller using universal approximators for nonlinear systems under input constraint

This paper describes a design of the robust adaptive controller for a nonlinear dynamical system with unknown nonlinearities. These unknown nonlinearities are approximated by a linearly-parameterized universal approximators (LPUA) whose parameters are adjusted on-line according to some adaptive laws for the purpose of controlling the output of the nonlinear system to track a given trajectory. The Lyapunov synthesis approach is used to develop a state-feedback adaptive control algorithm based on the adaptive LPUA model. The main contribution of this paper is to present a method to consider input constraint with rigorous stability proof. The overall control system guarantees that the tracking error converges in the small neighborhood of zero, and that all signals involved are uniformly bounded. Theoretical results are illustrated through a simulation example.

Modeling of Nonlinear SBR Process for Nitrogen Removal Using Fuzzy Systems

This paper shows the application of fuzzy system for a modeling of nonlinear biochemical process. A wastewater treatment process for nitrogen removal in a sequencing batch reactor (SBR) is presented and fuzzy systems with different consequent polynomials in the fuzzy rules to model and identify the oxidation reduction potential (ORP) of the process are introduced. The paper compares, analyzes the results of fuzzy modeling, and shows the nonlinear process can be modeled reasonably well by the present scheme.

Combination of fuzzy rule based model and self-organizing approximator technique: a new approach to nonlinear system modeling

We introduce a hybrid architecture that dwells on the ideas of fuzzy rule-based computing and an approximation scheme (SOPNN). The hybrid system is combined to get a novel heuristic approximation method. This composite structure overcomes the shortcomings of the individual methods especially it solves drawbacks of SOPNN while maintaining their desirable features. The combined method is efficient and much more accurate than either of the two individual schemes as well as other modeling methods. A three-input nonlinear static function is demonstrated for the utility of the proposed approach.

Decentralized neuro-fuzzy controller based on input-output linearization for multimachine power systems

Power systems have uncertain dynamics due to various effects such as lightning, severe storms and equipment failure in addition to interconnections between generators. The variation of the effective reactance of a transmission line due to a fault is an example of uncertainty in the system dynamics. Hence a robust controller to deal with these uncertainties is needed. Neuro-fuzzy controllers have been previously successfully applied in many cases where conventional control algorithms are difficult to apply due to lack of adaptivity and robustness. In this paper, we present a decentralized neuro-fuzzy controller for the transient stability and voltage regulation of a multimachine power system under a sudden fault. Simulation results show that satisfactory performance is achieved by the proposed controller.