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In this paper, we consider finite-horizon predictive control of linear stochastic systems with chance constraints where the admissible region is a convex polytope. For this problem, we present a novel solution approach based on box approximations. The key notion of our approach consists of two steps. First, we apply a linear operation to the joint state probability density function such that its covariance...
In this paper, we present an open-loop Stochastic Model Predictive Control (SMPC) method for discrete-time nonlinear systems whose state is defined on the unit circle. This modeling approach allows considering systems that include periodicity in a more natural way than standard approaches based on linear spaces. The main idea of this work is twofold: (i) we model the quantities of the system, i.e...
Closed-loop model predictive control of nonlinear systems, whose internal states are not completely accessible, incorporates the impact of possible future measurements into the planning process. When planning ahead in time, those measurements are not known, so the closed-loop controller accounts for the expected impact of all potential measurements. We propose a novel conservative closed-loop control...
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