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We address the problem of finite-horizon optimal control of uncertain discrete-time SISO linear systems with input constraints. The uncertainty for the problem analyzed is related to incomplete state information (output feedback) and stochastic disturbances. We analyze the optimal solutions for short optimization horizons when the disturbances and the initial state are Gaussian random vectors. We...
We present results linking model predictive control (MPC) and minimax optimal control theory. A distinction from previous work is that we show that typical MPC policies, which use the current system state, are minimax optimal closed-loop policies with respect to a certain class of cost functions. The control algorithms under study are similar, and sometimes identical, to conventional MPC. They require...
This paper presents novel results on the joint problem of sampling, reconstruction and quantization of analog signals. Existing literature on this topic deals exclusively with band-limited signals in sampled form. Our key departure from earlier results is that we deal with continuous time reconstruction of not necessarily band-limited signals. Our approach utilizes concepts and tools from optimal...
This paper further develops a new approach to optimal experiment design for dynamical systems by introducing and comparing different robust optimal experiment design criteria. Until recently, solutions to the optimal experiment design problem were paradoxical in so far that the optimal experiment depended on the system parameters, i.e. the optimal experiment depended on the very thing that the experiment...
This paper studies the asymptotic properties of the Hessian in discrete-time linear quadratic optimal control. We show that the singular values of the Hessian converge, in a well defined sense, to the principal gains in the frequency domain of an associated normalized system transfer function. We treat the stable and unstable case for multi-input multi-output linear systems. Potential applications...
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