DESIGN OF AN OBSERVER/IDENTIFIER

Abstract

Adaptive modern control theory primly deals with systems that are nonlinear and dynamic in nature. The challenge then becomes system modeling, identification, and observation. Feedback strategy for those systems will not be effective unless they are well identified and observed, and hence the need for observer/identifier. System identification is the process of parameters estimation when they are either completely or partially un- known. Likewise, system observation is the process of state estimation when they are inaccessible or physically impractical to measure. The design of observers/identifiers are usually based on an estimation algorithms that are well established such as Kalman filtering, Recursive Least-squares, Stochastic approximation [1], and stability criterion methods such as Lyapunov. We will introduce new state space equation model that can be used as an estimation algorithm and serve as the basis of yet another design for an observer/identifier