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1. Distributed Control of Thermostatically Controlled Loads: Kullback-Leibler Optimal Control in Continuous Time

   https://doi.org/10.1109/CDC40024.2019.9029603  

   Busic, Ana; Meyn, Sean (December 2019, Conference on Decision and Control)

   null (Ed.)

   The paper develops distributed control techniques to obtain grid services from flexible loads. The Individual Perspective Design (IPD) for local (load level) control is extended to piecewise deterministic and diffusion models for thermostatically controlled load models.The IPD design is formulated as an infinite horizon average reward optimal control problem, in which the reward function contains a term that uses relative entropy rate to model deviation from nominal dynamics. In the piecewise deterministic model, the optimal solution is obtained via the solution to an eigenfunction problem, similar to what is obtained in prior work. For a jump diffusion model this simple structure is absent. The structure for the optimal solution is obtained, which suggests an ODE technique for computation that is likely far more efficient than policy- or value-iteration. 

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2. Decoupled Data-Based Approach for Learning to Control Nonlinear Dynamical Systems

   https://doi.org/10.1109/TAC.2021.3108552  

   Wang, Ran; Parunandi, Karthikeya Sharma; Yu, Dan; Kalathil, Dileep; Chakravorty, Suman (August 2021, IEEE Transactions on Automatic Control)

   This paper addresses the problem of learning the optimal control policy for a nonlinear stochastic dynam- ical. This problem is subject to the ‘curse of dimension- ality’ associated with the dynamic programming method. This paper proposes a novel decoupled data-based con- trol (D2C) algorithm that addresses this problem using a decoupled, ‘open-loop - closed-loop’, approach. First, an open-loop deterministic trajectory optimization problem is solved using a black-box simulation model of the dynamical system. Then, closed-loop control is developed around this open-loop trajectory by linearization of the dynamics about this nominal trajectory. By virtue of linearization, a linear quadratic regulator based algorithm can be used for this closed-loop control. We show that the performance of D2C algorithm is approximately optimal. Moreover, simulation performance suggests a significant reduction in training time compared to other state of the art algorithms. 

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3. Learning‐based adaptive‐scenario‐tree model predictive control with improved probabilistic safety using robust Bayesian neural networks

   https://doi.org/10.1002/rnc.6560  

   Bao, Yajie; Chan, Kimberly J.; Mesbah, Ali; Velni, Javad Mohammadpour (December 2022, International Journal of Robust and Nonlinear Control)

   Summary Scenario‐based model predictive control (MPC) methods can mitigate the conservativeness inherent to open‐loop robust MPC. Yet, the scenarios are often generated offline based on worst‐case uncertainty descriptions obtaineda priori, which can in turn limit the improvements in the robust control performance. To this end, this paper presents a learning‐based, adaptive‐scenario‐tree model predictive control approach for uncertain nonlinear systems with time‐varying and/or hard‐to‐model dynamics. Bayesian neural networks (BNNs) are used to learn a state‐ and input‐dependent description of model uncertainty, namely the mismatch between a nominal (physics‐based or data‐driven) model of a system and its actual dynamics. We first present a new approach for training robust BNNs (RBNNs) using probabilistic Lipschitz bounds to provide a less conservative uncertainty quantification. Then, we present an approach to evaluate the credible intervals of RBNN predictions and determine the number of samples required for estimating the credible intervals given a credible level. The performance of RBNNs is evaluated with respect to that of standard BNNs and Gaussian process (GP) as a basis of comparison. The RBNN description of plant‐model mismatch with verified accurate credible intervals is employed to generate adaptive scenarios online for scenario‐based MPC (sMPC). The proposed sMPC approach with adaptive scenario tree can improve the robust control performance with respect to sMPC with a fixed, worst‐case scenario tree and with respect to an adaptive‐scenario‐based MPC (asMPC) using GP regression on a cold atmospheric plasma system. Furthermore, closed‐loop simulation results illustrate that robust model uncertainty learning via RBNNs can enhance the probability of constraint satisfaction of asMPC. 

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4. Virtual energy storage through decentralized load control with quality of service bounds

   https://doi.org/10.23919/ACC.2017.7963040  

   Brooks, Jonathan; Barooah, Prabir (May 2017, American Control Conference)

   null (Ed.)

   We propose a decentralized algorithm to help reduce demand-supply imbalance in a power grid by varying the demand from loads, just like charging and discharging a battery. The algorithm ensures strict bounds on the consumers' quality of service (QoS) by constraining the bandwidth of demand variation. A model-predictive-control formulation is adopted to compute local decisions at the loads. The algorithm is decentralized in the sense that loads do not communicate with one another. Instead, loads coordinate using local measurements of the grid frequency, which provide information about global demand-supply imbalance. It is envisioned that consumers will be recruited through long-term contracts, aided by the QoS guarantees provided by the proposed scheme. Simulation results show that loads are able to reduce frequency deviations while maintaining QoS constraints and that the performance of the algorithm scales well with the number of loads. Closed-loop stability is established under some assumptions. 

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5. Controller Switching-Enabled Active Detection of Multiplicative Cyberattacks on Process Control Systems

   https://doi.org/10.23919/ACC53348.2022.9867804  

   Narasimhan, Shilpa; El-Farra, Nael H.; Ellis, Matthew J. (June 2022, American Control Conference)

   This work focuses on the problem of enhancing cyberattack detection capabilities in process control systems subject to multiplicative cyberattacks. First, the relationship between closed-loop stability and attack detectability with respect to a class of residual-based detection schemes is rigorously analyzed. The results are used to identify a set of controller parameters (called "attack-sensitive" controller parameters) under which an attack can destabilize the closed-loop system. The selection of attack-sensitive controller parameters can enhance the ability to detect attacks, but can also degrade the performance of the attack-free closed-loop system. To balance this trade-off, a novel active attack detection methodology employing controller parameter switching between the nominal controller parameters (chosen on the basis of standard control design criteria) and the attack-sensitive controller parameters, is developed. The proposed methodology is applied to a chemical process example to demonstrate its ability to detect multiplicative sensor-controller communication link attacks. 

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