Maryam Kamgarpour

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Kamgarpour

First Name

Maryam

ORCID

0000-0003-0230-3518

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Publications 1 - 10 of 94

Payoff-based approach to learning Nash Equilibria in Convex Games
Tatarenko, Tatiana; Kamgarpour, Maryam (2017)
IFAC-PapersOnLine ~ 20th IFAC World Congress. Proceedings
We consider multi-agent decision making, where each agent optimizes its cost function subject to constraints. Agents’ actions belong to a compact convex Euclidean space and the agents’ cost functions are coupled. We propose a distributed payoff-based algorithm to learn Nash equilibria in the game between agents. Each agent uses only information about its current cost value to compute its next action. We prove convergence of the proposed algorithm to a Nash equilibrium in the game leveraging established results on stochastic processes. The performance of the algorithm is analyzed with a numerical case study.
Multi-robot task allocation for safe planning against stochastic hazard dynamics
Tihanyi, Daniel; Lu, Yimeng; Karaca, Orçun; et al. (2023)
2023 European Control Conference (ECC)
We address multi-robot safe mission planning in uncertain dynamic environments. This problem arises in safety-critical exploration, surveillance, and emergency rescue missions. The multi-robot optimal control problem is challenging because of the dynamic uncertainties and the exponentially increasing problem size with the number of robots. Leveraging recent works obtaining a tractable safety maximizing plan for a single robot, we propose a scalable two-stage framework. Specifically, the problem is split into a low-level single-agent problem and a high-level task allocation problem. The low-level problem uses an efficient approximation of stochastic reachability for a Markov decision process to derive the optimal control policy under dynamic uncertainty. The task allocation is solved using forward and reverse greedy heuristics and in a distributed auction-based manner. Properties of our safety objective enable provable performance bounds on the safety of the approximate solutions of the two heuristics.
A Robust Fault Detection and Isolation Filter for a Horizontal Axis Variable Speed Wind Turbine
Svetozarevic, Bratislav; Mohajerin Esfahani, Peyman; Kamgarpour, Maryam; et al. (2013)
2013 American Control Conference (ACC)
We develop a robust fault detection and isolation (FDI) technique in the presence of measurement noise and apply it to the horizontal axis variable speed wind turbine. In the first part, we provide a nonlinear model of the wind turbine in the form of differential-algebraic equations. We consider wind as a disturbance to the system, having two components: the wind speed and the wind direction. In the second part, treating the nonlinear term due to wind in the system dynamics as an unknown disturbance, we propose an optimization-based approach to robustify a linear residual generator with respect to measurement noise. The contribution of the noise into the residual is introduced in the framework of linear matrix inequalities, in which the requirements of the FDI filter are modeled as linear constraints. We illustrate the performance of our proposed method on the wind turbine benchmark model implemented in the FAST simulation code.
Distributed Design for Decentralized Control using Chordal Decomposition and ADMM
Zheng, Yang; Kamgarpour, Maryam; Sootla, Aivar; et al. (2020)
IEEE Transactions on Control of Network Systems
We propose a distributed design method for decentralized control by exploiting the underlying sparsity properties of the problem. Our method is based on the chordal decomposition of sparse block matrices and the alternating direction method of multipliers (ADMM). We first apply a classical parameterization technique to restrict the optimal decentralized control into a convex problem that inherits the sparsity pattern of the original problem. The parameterization relies on a notion of strongly decentralized stabilization, and sufficient conditions are discussed to guarantee this notion. Then, chordal decomposition allows us to decompose the convex restriction into a problem with partially coupled constraints, and the framework of ADMM enables us to solve the decomposed problem in a distributed fashion. Consequently, the subsystems only need to share their model data with their direct neighbors, without needing central computation. Numerical experiments demonstrate the effectiveness of the proposed method.
A market-based approach for enabling inter-area reserve exchange
Karaca, Orçun; Delikaraoglou, Stefanos; Kamgarpour, Maryam (2021)
Operations Research Letters
Considering the sequential clearing of energy and reserves in Europe, enabling inter-area reserve exchange requires optimally allocating inter-area transmission capacities between these two markets. To achieve this, we provide a market-based allocation framework and derive payments with desirable properties. The proposed min-max least core selecting payments achieve individual rationality, budget balance, and approximate incentive compatibility and coalitional stability. The results extend the works on private discrete items to a network of continuous public choices.
Exploiting Weak Supermodularity for Coalition-Proof Mechanisms
Karaca, Orçun; Kamgarpour, Maryam (2018)
2018 IEEE Conference on Decision and Control (CDC)
Robust control of constrained systems given an information structure
Furieri, Luca; Kamgarpour, Maryam (2017)
2017 IEEE 56th Annual Conference on Decision and Control (CDC)
We study finite horizon optimal control where the controller is subject to sensor-information constraints, that is, each input has access to a fixed subset of states at all times. In particular, we consider linear systems affected by exogenous disturbances with state and input constraints. We establish the class of sensor-information structures that allows for the formulation of this optimization problem as a convex program. In the literature, Quadratic Invariance (QI) is a well-established result that is applicable to the infinite horizon unconstrained case. We show that, despite state and inputs constraints being enforced, QI results can be naturally adapted to our problem. To this end, we highlight and exploit the connection between Youla parametrization and disturbance-feedback policies. Additionally, we provide graph-theoretic visual insight which is consistent with Partially Nested (PN) interpretations.
Enabling inter-area reserve exchange through stable benefit allocation mechanisms
Karaca, Orcun; Delikaraoglou, Stefanos; Hug, Gabriela; et al. (2022)
Omega (United Kingdom)
The establishment of a single European day-ahead market has accomplished the integration of the regional day-ahead markets. However, reserve provision and activation remain an exclusive responsibility of regional operators. This limited spatial coordination and the separated structure hinder the efficient utilization of flexible generation and transmission, since their capacities have to be ex-ante allocated between energy and reserves. To promote reserve exchange, recent work proposed a preemptive model that withdraws a portion of the inter-area transmission capacity available from day-ahead energy for reserves by minimizing the expected system cost. This decision-support tool, formulated as a stochastic bilevel program, respects the current architecture but does not suggest area-specific costs that guarantee sufficient benefits for areas to accept the solution. To this end, we formulate a preemptive model in a framework that allows application of game theory methods to obtain a stable benefit allocation, i.e., an outcome immune to coalitional deviations ensuring willingness of areas to coordinate. We show that benefit allocation mechanisms can be formulated either at the day-ahead or the real-time stages, in order to distribute the expected or the scenario-specific benefits, respectively. For both games, the proposed benefits achieve minimal stability violation, while allowing for a tractable computation with limited queries to the bilevel program. Our case studies, based on an illustrative and a more realistic test case, compare our method with well-studied benefit allocations, namely, the Shapley value and nucleolus, and analyze the factors that drive these allocations (e.g., flexibility, network structure, wind correlations). We show that our method performs better in stability and tractability.
No-Regret Learning in Unknown Games with Correlated Payoffs
Sessa, Pier Giuseppe; Bogunovic, Ilija; Kamgarpour, Maryam; et al. (2020)
Advances in Neural Information Processing Systems 32
We consider the problem of learning to play a repeated multi-agent game with an unknown reward function. Single player online learning algorithms attain strong regret bounds when provided with full information feedback, which unfortunately is unavailable in many real-world scenarios. Bandit feedback alone, i.e., observing outcomes only for the selected action, yields substantially worse performance. In this paper, we consider a natural model where, besides a noisy measurement of the obtained reward, the player can also observe the opponents' actions. This feedback model, together with a regularity assumption on the reward function, allows us to exploit the correlations among different game outcomes by means of Gaussian processes (GPs). We propose a novel confidence-bound based bandit algorithm GP-MW, which utilizes the GP model for the reward function and runs a multiplicative weight (MW) method. We obtain novel kernel-dependent regret bounds that are comparable to the known bounds in the full information setting, while substantially improving upon the existing bandit results. We experimentally demonstrate the effectiveness of GP-MW in random matrix games, as well as real- world problems of traffic routing and movie recommendation. In our experiments, GP-MW consistently outperforms several baselines, while its performance is often comparable to methods that have access to full information feedback.
Model Based Power Optimisation of Wind Farms
Heer, F.; Esfahani, P. Mohajerin; Kamgarpour, Maryam; et al. (2014)
Proceedings of the 2014 European Control Conference (ECC)

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Maryam Kamgarpour

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