Cyber-security in networked and distributed model predictive control

Arauz, T.; Chanfreut, Paula; Maestre, J. M.

doi:10.1016/j.arcontrol.2021.10.005

Cited by 18 publications

(7 citation statements)

References 119 publications

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“…This presumes the existence of a coordinator agent or software that is acting in good faith, which should be a reasonable assumption in EWO. We are also assuming that all agents are honest‐but‐curious, that is, that no agent is trying to trick the coordinator or launch any adversarial “attacks,” which is the scope of a whole subfield of literature 63 …”

Section: Methodsmentioning

confidence: 99%

Data‐driven coordination of subproblems in enterprise‐wide optimization under organizational considerations

Berg¹,

Petsagkourakis²,

Shah³

et al. 2023

AIChE Journal

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While decomposition techniques in mathematical programming are usually designed for numerical efficiency, coordination problems within enterprise-wide optimization are often limited by organizational rather than numerical considerations. We propose a "data-driven" coordination framework which manages to recover the same optimum as the equivalent centralized formulation while allowing coordinating agents to retain autonomy, privacy, and flexibility over their own objectives, constraints, and variables. This approach updates the coordinated, or shared, variables based on derivative-free optimization (DFO) using only coordinated variables to agent-level optimal subproblem evaluation "data." We compare the performance of our framework using different DFO solvers (CUATRO, Py-BOBYQA, DIRECT-L, GPyOpt) against conventional distributed optimization (ADMM) on three case studies: collaborative learning, facility location, and multiobjective blending. We show that in low-dimensional and nonconvex subproblems, the exploration-exploitation trade-offs of DFO solvers can be leveraged to converge faster and to a better solution than in distributed optimization.

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Section: Methodsmentioning

confidence: 99%

Data‐driven coordination of subproblems in enterprise‐wide optimization under organizational considerations

Berg¹,

Petsagkourakis²,

Shah³

et al. 2023

AIChE Journal

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show abstract

“…An optimal solution of problem (9) provides a set of state trajectories starting at x k I for all scenarios, behaving according to the local discrete-time dynamics as in (2), and taking only feasible states x l+1,s I ∈ X I . The optimal inputs are chosen to be feasible, to satisfy the constraints in (4) in all scenarios s ∈ Σ .…”

Section: Contract-based Robust Distributed Mpcmentioning

confidence: 99%

“…Based on updated measurements, it repeatedly computes optimal inputs to the system at each sampling time. Distributed MPC (DMPC) methods, see [1] for an overview and [2] for security-related DMPC, are designed for large systems of coupled subsystems and locally apply MPC in each subsystem. In contrast to fully decentralized approaches where the neighbors' dynamic evolution is unknown to every subsystem, DMPC schemes involve some exchange of information among neighbors.…”

Section: Introductionmentioning

confidence: 99%

Resilient Model Predictive Control of Distributed Systems Under Attack Using Local Attack Identification

Braun¹,

Albrecht²,

Lucia³

2023

Preprint

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With the growing share of renewable energy sources, the uncertainty in power supply is increasing. In addition to the inherent fluctuations in the renewables, this is due to the threat of deliberate malicious attacks, which may become more prevalent with a growing number of distributed generation units. Also in other safety-critical technology sectors, control systems are becoming more and more decentralized, causing the targets for attackers and thus the risk of attacks to increase. It is thus essential that distributed controllers are robust toward these uncertainties and able to react quickly to disturbances of any kind. To this end, we present novel methods for model-based identification of attacks and combine them with distributed model predictive control to obtain a resilient framework for adaptively robust control. The methodology is specially designed for distributed setups with limited local information due to privacy and security reasons. To demonstrate the efficiency of the method, we introduce a mathematical model for physically coupled microgrids under the uncertain influence of renewable generation and adversarial attacks, and perform numerical experiments, applying the proposed method for microgrid control.

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“…Therefore, various dual decomposition techniques, such as the alternating direction method of multipliers (ADMM), the dual fast gradient method, and others, can be used to achieve distributed solutions to the original DMPC problems. 7,8 However, ensuring computational efficiency in the sense of terminating internal iterations prematurely is an additional crucial consideration when employing such algorithms. In Reference 9, the whole MPC optimization problem subject to coupled constraints is concerted into a consensus problem with edge-wise constraints, which is then solved distributedly by using ADMM.…”

Section: Introductionmentioning

confidence: 99%

Iterative distributed model predictive control for nonlinear systems with coupled non‐convex constraints and costs

Wu,

Dai,

Xia

2024

Intl J Robust & Nonlinear

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This paper proposes a distributed model predictive control (DMPC) algorithm for dynamic decoupled discrete‐time nonlinear systems subject to nonlinear (maybe non‐convex) coupled constraints and costs. Solving the resulting nonlinear optimal control problem (OCP) using a DMPC algorithm that is fully distributed, termination‐flexible, and recursively feasible for nonlinear systems with coupled constraints and costs remains an open problem. To address this, we propose a fully distributed and globally convergence‐guaranteed framework called inexact distributed sequential quadratic programming (IDSQP) for solving the OCP at each time step. Specifically, the proposed IDSQP framework has the following advantages: (i) it uses a distributed dual fast gradient approach for solving inner quadratic programming problems, enabling fully distributed execution; (ii) it can handle the adverse effects of inexact (insufficient) calculation of each internal quadratic programming problem caused by early termination of iterations, thereby saving computational time; and (iii) it employs distributed globalization techniques to eliminate the need for an initial guess of the solution. Under reasonable assumptions, the proposed DMPC algorithm ensures the recursive feasibility and stability of the entire closed‐loop system. We conduct simulation experiments on multi‐agent formation control with non‐convex collision avoidance constraints and compare the results against several benchmarks to verify the performance of the proposed DMPC method.

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Cyber-security in networked and distributed model predictive control

Cited by 18 publications

References 119 publications

Data‐driven coordination of subproblems in enterprise‐wide optimization under organizational considerations

Data‐driven coordination of subproblems in enterprise‐wide optimization under organizational considerations

Resilient Model Predictive Control of Distributed Systems Under Attack Using Local Attack Identification

Iterative distributed model predictive control for nonlinear systems with coupled non‐convex constraints and costs

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