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State estimation problem together with systems classification is solved. The novel idea is to represent all conditional probability density functions (c.p.d.f.) as a set of random samples. Usual approach is to represent c.p.d.f. as a function over the state space. Using large number of samples an exact equivalent representation of c.p.d.f. is obtained. From this samples the estimates of moments like...
The optimal control strategy for discrete time multiple model is described. Simulation of control and on-line estimation of model probability is shown. Robustness of stability and comparison of the classical LQ control and LQ control based on multiple models is presented.
Simultaneous state estimation and parameter tracking of time varying input-output ARMAX model with known noise parameters was described recently. Optimal control strategies of such model are developed in this contribution. Certainty equivalent and cautious LQ strategies (Linear system and Quadratic criterion) are obtained. Certainty equivalent strategies operate with state and parameter means only...
Kalman filter is a frequently used tool for linear state estimation due to its simplicity and optimality. It can further be used for fusion of information obtained from multiple sensors. Kalman filtering is also often applied to nonlinear systems. As the direct application of bayesian functional recursion is computationally not feasible, approaches commonly taken use either a local approximation —...
Linearization is a standard part of modeling and control design theory for a class of nonlinear dynamical systems taught in basic undergraduate courses. Although linearization is a straight-line methodology, it is not applied correctly by many students since they often forget to keep the operating point in mind. This paper explains the topic and suggests a way to improve the teaching of the methodology...
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