• Journal of the Institute of Electronics Engineers of Korea SC
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• v.38 no.3
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• pp.1-12
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• 2001

We consider the robust control problem for non-minimum phase(NMP) systems with parametric uncertainty. First, a new method that translates such an uncertain NMP system into a interval family of minimum phase(MP) transfer functions followed a time delay term in the form of Pade' approximation is presented. The controller to be proposed consists of a compensator with Smith predictor structure, so that it can compensate the time delay behaviour due to NMP plant. Therein, the main feedback controller for a family of MP plants has been designed by using quantitative feedback theory(QFT) such that satisfies the robust stability against the structured uncertainty. The stability and performance of overall system are examined through an illustrative example.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.993-996
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• 1993

This paper addresses the structure and its associated learning algorithms of a feedforward multi-layered connectionist network, which has distributed learning abilities, for realizing the basic elements and functions of a traditional fuzzy logic controller. The proposed neural-network-based fuzzy logic control system (NN-FLCS) can be contrasted with the traditional fuzzy logic control system in their network structure and learning ability. An on-line supervised structure/parameter learning algorithm dynamic learning algorithm can find proper fuzzy logic rules, membership functions, and the size of output fuzzy partitions simultaneously. Next, a Reinforcement Neural-Network-Based Fuzzy Logic Control System (RNN-FLCS) is proposed which consists of two closely integrated Neural-Network-Based Fuzzy Logic Controllers (NN-FLCS) for solving various reinforcement learning problems in fuzzy logic systems. One NN-FLC functions as a fuzzy predictor and the other as a fuzzy controller. As ociated with the proposed RNN-FLCS is the reinforcement structure/parameter learning algorithm which dynamically determines the proper network size, connections, and parameters of the RNN-FLCS through an external reinforcement signal. Furthermore, learning can proceed even in the period without any external reinforcement feedback.