초록

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Abstract ▼ AI-Helper

This paper investigates a neuro-controller combined in parallel with a conventional linear controller of PID type in order to control nonminimum phase systems more efficiently. The objective is to minimize overall position errors as well as to maintain small undershooting. A costfunction is proposed...

This paper investigates a neuro-controller combined in parallel with a conventional linear controller of PID type in order to control nonminimum phase systems more efficiently. The objective is to minimize overall position errors as well as to maintain small undershooting. A costfunction is proposed with two conflict objectives. The neuro-controller is trained off-line with evolutionary programming(EP) in such a way that it becomes optimal by minimizing the given cost function through global evaluation based on desired control performance during the whole training time interval. However, it is not easy to find an optimal solution which satisfies individual objective simultaneously. With the concept of Pareto optimality and EP, we train the proposed controller more effectively and obtain a valuable set of optimal solutions. Simulation results show the efficacy of the proposed controller in a viewpoint of improvement of performance of a step response like fast settling time and small undershoot or overshoot compared with that of a conventional linear controller.