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Ching-Hung Lee

Publications and source records attributed to Ching-Hung Lee.

3 recordsLinked to original sources

Low order robust controller design for preserving Hinfinity performance: genetic algorithm approach.

This paper investigates the design of low order robust controllers based on an Hinfinity performance index using a real-code genetic algorithm. In Hinfinity controller design, the major disadvantage of the existing methods is that they lead to high-order controllers. This is the gap between theory and practice. Therefore the purpose of this paper is to design a low order controller with similar performance to the Hinfinity optimal controllers, which can find sufficiently wide use in engineering practice. We first design the Hinfinity optimal controller using Glover and Doyle's results, and obtain the corresponding performance index gamma. Second, the desired low order controller with several parameters is chosen, e.g., a first-order controller, or a PID controller. Finally, we use the real-code genetic algorithm to find the optimal controller parameters that preserve the performance index y. Computational simulations illustrate the effectiveness of the proposed approach.

Journal Article↗

Stabilization of nonlinear nonminimum phase systems: adaptive parallel approach using recurrent fuzzy neural network.

In this paper, an adaptive parallel control architecture to stabilize a class of nonlinear systems which are nonminimum phase is proposed. For obtaining an on-line performance and self-tuning controller, the proposed control scheme contains recurrent fuzzy neural network (RFNN) identifier, nonfuzzy controller, and RFNN compensator. The nonfuzzy controller is designed for nominal system using the techniques of backstepping and feedback linearization, is the main part for stabilization. The RFNN compensator is used to compensate adaptively for the nonfuzzy controller, i.e., it acts like a fine tuner; and the RFNN identifier provides the system's sensitivity for tuning the controller parameters. Based on the Lyapunov approach, rigorous proofs are also presented to show the closed-loop stability of the proposed control architecture. With the aid of the RFNN compensators, the parallel controller can indeed improve system performance, reject disturbance, and enlarge the domain of attraction. Furthermore, computer simulations of several examples are given to illustrate the applicability and effectiveness of this proposed controller.

Journal Article↗

Calculation of PID controller parameters by using a fuzzy neural network.

In this paper, we use the fuzzy neural network (FNN) to develop a formula for designing the proportional-integral-derivative (PID) controller. This PID controller satisfies the criteria of minimum integrated absolute error (IAE) and maximum of sensitivity (Ms). The FNN system is used to identify the relationship between plant model and controller parameters based on IAE and Ms. To derive the tuning rule, the dominant pole assignment method is applied to simplify our optimization processes. Therefore, the FNN system is used to automatically tune the PID controller for different system parameters so that neither theoretical methods nor numerical methods need be used. Moreover, the FNN-based formula can modify the controller to meet our specification when the system model changes. A simulation result for applying to the motor position control problem is given to demonstrate the effectiveness of our approach.

Algorithms↗