Earticle

초록

영어

Neural networks are known as kinds of intelligent strategies since they have learning capability. There are various their applications from intelligent control fields, however, their applications have limits from the point that the stability of the intelligent control systems is not usually guaranteed. In this paper we propose an Adaptive Tracking Control of Nonlinear System the radial basis function network (RBFN) that is a kind of neural networks. The learning method involves structural adaptation and parameter adaptation. No prior knowledge of the plant is assumed, and the controller has to begin with exploration of the state space. The exploration versus exploitation dilemma of reinforcement learning is solved through smooth transitions between the two modes. The controller is capable of asymptotically approaching the desired reference trajectory, which is showed in simulation result.

목차

Abstract
 1. Introduction
 2. Dynamics and Structural Properties of Neuro Adaptive Control System
 3. Formulation of nonlinear control problem
 4. Radial Basis Function Neural Network (RBFN)
 5. Design of an Adaptive Tracking Controller
 6. Simulation Examples
 7. Conclusion
 References

키워드

저자

• Hyun-Seob Cho [ Dept. of Electronic Engineering, Chungwoon University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605