The Multiobjective Covariance Control for Stochastic Systems

• Main Authors: Wen-Jer Chang, 張文哲
• Other Authors: Hung-Yuan Chung
• Format: Others
• Language: en_US
• Published: 1995
• Online Access: http://ndltd.ncl.edu.tw/handle/84438254600222109327

博士 === 國立中央大學 === 電機工程研究所 === 83 === Virtually all dynamical systems are in some way disturbed by uncontrolled external forces. This disturbances may assume a myriad of forms such as wind gusts, gravity gradients, the- rmal gradients, structural vibrations, or sensor and actuator noise, and may enter the system in many different ways. For the theory of control systems, the disturbances are often mo- deled as a Gaussian noise process, and the pointing specific- ations are expressed in terms of allowable Root-Mean-Squared (RMS) pointing error. As a general rule, the control engineer first designs a controller and then computes the covariance matrix of the closed-loop system to assess the state or output RMS response. The major difficulty encountered here is that one is attempting to indirectly satisfy very specific control objectives. Moreover, the requirements are multiobjective si- nce RMS constraints are imposed on many outputs or states. Certainly a direct design method for complete variance (RMS squared) assignment would be useful. A complete approach, which is called covariance control theory, for assigning the entire state covariance matrix to the closed-loop systems via state feedback or output dynamic controllers has been developed in past years. However, the systems considered in these literatures are often linear ones. Hence, in this dissertation we will first attempt to extend the covariance control theory to other different stochastic systems. Furthermore, the second purpose of this dissertation is to describe how the covariance controller design problem can be solved for simultaneously achieving multiobjective pe- rformance requirements for linear stochastic systems. These performance constraints considered in this dissertation incl- ude circular closed-loop system pole constraints, individual state variance constraints, H∞ norm and minimum auxiliary entropy constraints of closed-loop system transfer function.