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Parameter and state estimation is considered for uncertain linear time-invariant systems. Estimates of the state and parameters are computed by differential equations with estimation error between the plant output and its estimate by the observer. Design method for the estimator is proposed via conditions on matrix gains multiplied by the output estimation error. Multiple observers are utilized to...
This paper considers prediction problems for discrete-time signals. We introduce a virtual system that generates the signals to be predicted. The prediction problem is reduced to an estimation problem for the dynamics of the virtual system. In order to estimate the dynamics, an adaptive scheme is introduced. The scheme estimates the state and some parameters of the virtual system from the discrete-time...
Stability condition is considered for a class of second-order piecewise linear systems. The state space of the system is divided by partitions into some subsystems. The state trajectory evolves along with the dynamics of the subsystem in which the state exists. A necessary and sufficient condition for the system to be stable is derived. The condition is expressed via the eigenvalues of the matrices...
Stabilization of a multi-input multi-output plant with variable operating conditions is considered. The plant is described as a linear interpolation of proper stable factorizations of transfer functions defined at representative operating points. In this paper, more than two representative models are considered, in contrast with the case of two representative models, which has been extensively dealt...
A parameter and state estimation problem is considered for uncertain linear time-invariant systems. Under a certain condition, it is shown that the state of the system can be asymptotically described by a linear combination of state estimates generated by suitable multiple observers, where the weights of the linear combination are the parameters to be estimated. Using this property, a computation...
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