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Applied Mathematics and Mechanics (English Edition) ›› 2000, Vol. 21 ›› Issue (12): 1417-1422.

• 论文 • 上一篇    下一篇

PRECISE INTEGRATION METHOD FOR LQG OPTIMAL MEASUREMENT FEEDBACK CONTROL PROBLEM

钟万勰, 蔡志勤   

  1. State Key Laboratory of Structure Analysis, Dalian University of Technology, Dalian 116024, P. R. China
  • 收稿日期:2000-02-22 出版日期:2000-12-18 发布日期:2000-12-18
  • 基金资助:

    the National Natural Science Foundation of China(19732020)

PRECISE INTEGRATION METHOD FOR LQG OPTIMAL MEASUREMENT FEEDBACK CONTROL PROBLEM

ZHONG Wan-xie, CAI Zhi-qin   

  1. State Key Laboratory of Structure Analysis, Dalian University of Technology, Dalian 116024, P. R. China
  • Received:2000-02-22 Online:2000-12-18 Published:2000-12-18
  • Supported by:

    the National Natural Science Foundation of China(19732020)

PDF

500

被引次数

6

摘要： By using the precise integration method,the numerical solution of linear quadratic Gaussian (LQG) optimal control problem was discussed. Based on the separation principle, the LQG control problem decomposes, or separates, into an optimal state-feedback control problem and an optimal state estimation problem. That is the off-line solution of two sets of Riccati differential equations and the on-line integration solution of the state vector from a set of time-variant differential equations. The present algorithms are not only appropriate to solve the two-point boundary-value problem and the corresponding Riccati differential equation, but also can be used to solve the estimated state from the time-variant differential equations. The high precision of precise integration is of advantage for the control and estimation. Numerical examples demonstrate the high precision and effectiveness of the algorithm.

Abstract: By using the precise integration method,the numerical solution of linear quadratic Gaussian (LQG) optimal control problem was discussed. Based on the separation principle, the LQG control problem decomposes, or separates, into an optimal state-feedback control problem and an optimal state estimation problem. That is the off-line solution of two sets of Riccati differential equations and the on-line integration solution of the state vector from a set of time-variant differential equations. The present algorithms are not only appropriate to solve the two-point boundary-value problem and the corresponding Riccati differential equation, but also can be used to solve the estimated state from the time-variant differential equations. The high precision of precise integration is of advantage for the control and estimation. Numerical examples demonstrate the high precision and effectiveness of the algorithm.

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