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Control of Uncertain Systems

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Springer Handbook of Automation

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Abstract

Dynamical systems typically have uncertainties due to modeling errors, measurement inaccuracy, mutations in the evolutionary processes, etc. The stochastic deviations can compound overtime and can lead to catastrophic results. In this chapter, we will present different approaches for decision-making in systems with uncertainties. We first consider model predictive control, which models and learns the system and uses the learned model to optimize the system. In many cases, a prior model of the system is not known. For such cases, we will also explain Gaussian process regression, which is one of the efficient modeling techniques. Further, we present constrained Markov decision process (CMDP) as an approach for decision-making with uncertainties, where the system is modeled as a MDP with constraints. The formalism of CMDP is extended to a model-free approach based on reinforcement learning to make decisions in the presence of constraints.

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    Vaneet Aggarwal & Mridul Agarwal

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  1. Vaneet Aggarwal
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    Aggarwal, V., Agarwal, M. (2023). Control of Uncertain Systems. In: Nof, S.Y. (eds) Springer Handbook of Automation. Springer Handbooks. Springer, Cham. https://doi.org/10.1007/978-3-030-96729-1_8

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