Decentralized control of interconnected systems using a neuro-coordinator and an application to a planar robot manipulator

신경회로망을 이용한 상호 연결된 시스템의 비집중 제어와 평면 로봇 매니퓰레이터에의 응용

  • 정희태 (경북대학교 전자.전기공학부) ;
  • 전기준 (경북대학교 전자.전기공학부, 제어계측신기술연구센터)
  • Published : 1996.06.01

Abstract

It is inevitable for local systems to have deviations which represent interactions and modeling errors originated from the decomposition process of a large scale system. This paper presents a decentralized control scheme for interconnected systems using local linear models and a neuro-coordinator. In the proposed method, the local system is composed of a linear model and unknown deviations caused by linearizing the subsystems around operating points or by estimating parameters of the subsystems. Because the local system has unmeasurable deviations we define a local reference model which consists of a local linear model and a neural network to estimate the deviations indirectly. The reference model is reformed into a linear model which has no deviations through a transformation of input variables and we obtain an optimum feedback control law which minimizes a local performance index. Finally, we derive a decentralized feedback control law which consists of local linear states and neural network outputs. In the decentralized control, the neuro-coordinator generates a corrective control signal to cancel the effect of deviations through backpropagation learning with the errors obtained from the differences of the local system outputs and reference model outputs. Also, the stability of local system is proved by the degree of learning of the neural network under an assumption on a neural network learning index. It is shown by computer simulations that the proposed control scheme can be applied successfully to the control of a biased two-link planar robot manipulator.

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