The proposed output-feedback MPC algorithms guarantee practically stochastic input-to-state stability for linear systems with multiplicative and additive random uncertainty.
Provides a theoretical framework for robust algorithmic control under random uncertainty; leaves open its clinical utility for physiological modeling.
Summary This paper studies the output‐feedback model predictive control (MPC) design problem for linear systems with multiplicative and additive random uncertainty. We first present an off‐line optimization algorithm to optimize feedback gains of the observer and the dual‐mode control policy. After that, by defining a cuboid tube whose center and boundary are both time‐varying variables, we develop a set sequence with increased freedom to contain stochastic system trajectories. A quadratic performance function with analytic upper and lower bounds is minimized such that it decreases exponentially to a finite range under the expectation. The resulting MPC algorithms are proved to guarantee practically stochastic input‐to‐state stability. A numerical example of the wind turbine model illustrates the properties of the MPC algorithms.
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Li et al. (2017) studied this question.
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