Design and Development of Cascade Heating Control for a «Smart» Residential Housing

Akimov, Valery I.; Polyakov, Sergey; Polukazakov, Aleksey V.

doi:10.1109/rusautocon49822.2020.9208225

Cited by 11 publications

(1 citation statement)

References 3 publications

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“…Although it is a partial representation of the system, this model allows the expensive design and implementation of a digital twin to be avoided; • Using two separate GPs to obtain the probabilistic models of performance metric and safety, respectively. This allows different uncertainty components to be dealt with that could affect, separately as well as jointly, performances and safety; • Generating new safe-and more effective-policies by solving a constrained optimization problem involving the two GP models mentioned before; • Validating the proposed approach on two case studies inspired by real-life systems and quite commonly considered in the optimal control literature: the control of a house heating system [18,19] and the control of a water tank [20][21][22]. Safe-by-design and new safely explored policies are compared both in terms of performances and incurred risk (i.e., safety violations).…”

Section: Introductionmentioning

confidence: 99%

Safe Optimal Control of Dynamic Systems: Learning from Experts and Safely Exploring New Policies

Candelieri,

Ponti,

Fersini

et al. 2023

Mathematics

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Many real-life systems are usually controlled through policies replicating experts’ knowledge, typically favouring “safety” at the expense of optimality. Indeed, these control policies are usually aimed at avoiding a system’s disruptions or deviations from a target behaviour, leading to suboptimal performances. This paper proposes a statistical learning approach to exploit the historical safe experience—collected through the application of a safe control policy based on experts’ knowledge— to “safely explore” new and more efficient policies. The basic idea is that performances can be improved by facing a reasonable and quantifiable risk in terms of safety. The proposed approach relies on Gaussian Process regression to obtain a probabilistic model of both a system’s dynamics and performances, depending on the historical safe experience. The new policy consists of solving a constrained optimization problem, with two Gaussian Processes modelling, respectively, the safety constraints and the performance metric (i.e., objective function). As a probabilistic model, Gaussian Process regression provides an estimate of the target variable and the associated uncertainty; this property is crucial for dealing with uncertainty while new policies are safely explored. Another important benefit is that the proposed approach does not require any implementation of an expensive digital twin of the original system. Results on two real-life systems are presented, empirically proving the ability of the approach to improve performances with respect to the initial safe policy without significantly affecting safety.

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