Article
Automation & Control Systems
Mudassir Shabbir, Waseem Abbas, A. Yasin Yazcoglu, Xenofon Koutsoukos
Summary: In this article, the problem of finding a tight lower bound on the dimension of the strong structurally controllable subspace (SSCS) in networks with Laplacian dynamics is studied. The distance-to-leaders vectors, which are vectors containing the distances between leaders and followers in the network graph, are used to provide distance-based bounds on the dimension of SSCS. Exact and approximate algorithms for computing the longest sequences of distance-to-leaders vectors are presented. The distance-based bound outperforms other known bounds and has applications in leader selection and characterizing strong structural controllability in specific graph structures.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Automation & Control Systems
Jun Yang, Chunjie Zhou, Yu-Chu Tian, Chao An
Summary: The integration of cyber and physical domains can enhance the flexibility and efficiency of industrial systems, but also introduces security threats. A zoning-based approach is introduced to identify and mitigate actuator attacks in industrial cyber-physical systems, ensuring system controllability. Case studies demonstrate the effectiveness of the proposed approach.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2021)
Article
Computer Science, Interdisciplinary Applications
Om Prakash, Mani Bhushan
Summary: This study proposes an information theoretic based sensor placement design approach for reliable estimation of variables in a steady state linear flow process. The approach minimizes residual Kullback-Leibler divergence based objective function, utilizes time varying system reliability, and allows user customization.
COMPUTERS & CHEMICAL ENGINEERING
(2022)
Article
Automation & Control Systems
Lingying Huang, Junfeng Wu, Yilin Mo, Ling Shi
Summary: This article addresses the problem of sensor and actuator placement and proposes a branch-and-bound algorithm to search for solutions. By deriving lower and upper bounds in the search space, a suboptimal solution is obtained and the optimality gap is analyzed. Numerical examples demonstrate the effectiveness of the algorithm, showing significant reduction in iteration numbers and improvement in LQG cost compared to the canonical algorithm.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Computer Science, Information Systems
Yuji Saito, Taku Nonomura, Keigo Yamada, Kumi Nakai, Takayuki Nagata, Keisuke Asai, Yasuo Sasaki, Daisuke Tsubakino
Summary: This paper focuses on the sparse sensor placement problem for least-squares estimation and extends the previous novel approach of the sparse sensor selection algorithm. The study shows that the method used when the number of sensors is less than the number of state variables is mathematically the same as the previously proposed QR method, while a new algorithm is developed for cases where the number of sensors is greater than the number of state variables. Furthermore, the effectiveness of the proposed algorithm is demonstrated through comparisons with other algorithms using real datasets.
Article
Engineering, Electrical & Electronic
Bangjun Li, Haoran Liu, Ruzhu Wang
Summary: In this paper, a novel method using signal reconstruction error as the cost function for sensor placement is proposed, with a focus on greedy minimization to improve reconstruction accuracy. A recursive formula is used to enhance the evaluation efficiency of the criterion, and a fast reconstruction-oriented local optimization technique is developed. Experimental results demonstrate the superiority of the proposed algorithm over state-of-the-art methods.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2021)
Article
Engineering, Electrical & Electronic
Tomoya Nishida, Natsuki Ueno, Shoichi Koyama, Hiroshi Saruwatari
Summary: Sensor placement methods for field estimation based on Gaussian processes are proposed, which can be applied to cases in which the sensor placement region is arbitrarily restricted. The effectiveness of these methods in sound field estimation is confirmed through numerical experiments.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2022)
Article
Computer Science, Hardware & Architecture
Nguyen Thi Hanh, Huynh Thi Thanh Binh, Vu Quang Truong, Nguyen Phuc Tan, Huynh Cong Phap
Summary: This paper proposes a two-phase solution for the target coverage and connectivity problems in wireless sensor networks. The first phase utilizes a greedy algorithm combined with linear programming, while the second phase employs clustering combined with a graph max flow approach. The results of the study demonstrate significant improvements in various evaluation metrics compared to baseline methods. Additionally, the findings provide advantages for future research on WSNs and target coverage.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2023)
Article
Automation & Control Systems
Geethu Joseph
Summary: In this article, the conditions for a discrete-time linear system to achieve output controllability using sparse control inputs are studied. Necessary and sufficient conditions are obtained by extending the Kalman rank test, but their verification is computationally heavy due to combinatorial nature. Noncombinatorial conditions with polynomial time complexity are derived to verify output sparse controllability. The results also provide bounds on the minimum sparsity level required to ensure output controllability of the system.
IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS
(2023)
Article
Automation & Control Systems
Milad Siami, Alexander Olshevsky, Ali Jadbabaie
Summary: This article investigates actuator selection for linear dynamical systems, developing a framework for designing sparse actuator schedules with guaranteed performance bounds using deterministic and randomized algorithms. Introducing systemic controllability metrics, the study provides a polynomial-time actuator schedule selecting a constant number of actuators at each time step for approximating controllability metrics. The results also apply to sensor selection, demonstrating effectiveness through numerical simulations.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2021)
Review
Chemistry, Analytical
Abhijeet Redekar, Dipankar Deb, Stepan Ozana
Summary: This paper reviews the application of electric actuators in renewable energy systems, analyzing the functions and challenges of different types of actuators in various renewable applications, and proposing optimization opportunities and replacements.
Article
Automation & Control Systems
Pauline Bernard, Ricardo G. Sanfelice
Summary: This paper proposes a general framework for state estimation of hybrid dynamical systems with known jumps. The candidate observer is a hybrid dynamical system with jumps triggered when the system jumps. The paper provides sufficient conditions for the design of high-gain flow-based and jump-based observers, as well as a hybrid Kalman filter for systems with different characteristics.
Article
Polymer Science
Rudolf Kiefer, Ngoc Tuan Nguyen, Quoc Bao Le, Gholamreza Anbarjafari, Tarmo Tamm
Summary: Different adaptations for artificial muscles following the natural muscle antagonist actuation principle were introduced in this work, combining responses from Polypyrrole (PPy) films of different polymerization techniques. Through consecutive one-pot electrosynthesis of two layers with different deposition regimes, an all-PPy bending hybrid actuator was successfully produced. The nearly equal expansions upon oxidation and reduction under carefully selected conditions enabled the fabrication of a mirrored trilayer laminate that behaved as a linear actuator, revealing a new perspective on the mixed-ion activity of conductive polymers.
Review
Automation & Control Systems
Giacomo Baggio, Fabio Pasqualetti, Sandro Zampieri
Summary: This article introduces the fundamental principles and limitations of controlling complex networks, presents an energy-aware controllability metric, discusses its properties and bounds, and examines the problem of optimally selecting a set of control nodes to minimize control effort.
ANNUAL REVIEW OF CONTROL ROBOTICS AND AUTONOMOUS SYSTEMS
(2022)
Article
Automation & Control Systems
Geethu Joseph, Chandra R. Murthy
Summary: In this article, the controllability of a discrete-time linear dynamical system with sparse control inputs is considered. Algebraic necessary and sufficient conditions for ensuring controllability are derived and can be verified in polynomial time complexity. A generalized Kalman decomposition-like procedure is presented to separate the state-space into subspaces corresponding to sparse-controllable and sparse-uncontrollable parts, providing a theoretical basis for designing networked linear control systems with sparse inputs.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2021)
Editorial Material
Management
Orcun Karaca, Baiwei Guo, Maryam Kamgarpour
Summary: We provide a counterexample to the performance guarantee of a greedy algorithm for minimizing a supermodular set function as claimed in the paper by Il'ev and Linker in 2006, and identify the origin of this error in the proof of the main theorem.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Automation & Control Systems
Yang Zheng, Luca Furieri, Antonis Papachristodoulou, Na Li, Maryam Kamgarpour
Summary: The article presents explicit affine mappings among Youla, system-level, and input-output parameterizations, and two direct implications of these affine mappings.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2021)
Article
Operations Research & Management Science
Orcun Karaca, Stefanos Delikaraoglou, Maryam Kamgarpour
Summary: By optimizing the allocation of inter-area transmission capacities between European markets through a market-based framework and min-max least core selecting payments, individual rationality, budget balance, approximate incentive compatibility, and coalitional stability can be achieved. These results extend the works on private discrete items to a network of continuous public choices.
OPERATIONS RESEARCH LETTERS
(2021)
Article
Automation & Control Systems
Vasileios Lefkopoulos, Maryam Kamgarpour
Summary: This study focuses on trajectory planning in an environment with a set of obstacles whose locations vary with uncertain time. Uncertainties are modeled using Gaussian distributions, and estimates are made through finite samples to tighten the chance-constraint program. Provable guarantees are provided on satisfaction of the chance-constraints corresponding to nominal but unknown moments.
Article
Operations Research & Management Science
Orcun Karaca, Daniel Tihanyi, Maryam Kamgarpour
Summary: This study investigates the problem of minimizing increasing set functions over the base matroid, providing two novel performance guarantees for approximate solutions obtained by two greedy heuristics. These methods have significant implications for solving practical problems such as actuator and sensor placement in control, task allocation, and video summarization.
OPERATIONS RESEARCH LETTERS
(2021)
Article
Automation & Control Systems
Yang Zheng, Luca Furieri, Maryam Kamgarpour, Na Li
Summary: This paper discusses the convex parameterization of internally stabilizing controller, reveals the equivalence between four groups of stable closed-loop transfer matrices and internal stability, and investigates the properties of these parameterizations in terms of FIR approximation and numerical robustness.
Article
Management
Orcun Karaca, Stefanos Delikaraoglou, Gabriela Hug, Maryam Kamgarpour
Summary: This study proposes a preemptive model to promote reserve exchange in the European day-ahead market and achieves stable benefit allocation through the application of game theory methods.
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE
(2022)
Article
Automation & Control Systems
Kai Ren, Heejin Ahn, Maryam Kamgarpour
Summary: In this paper, we address the problem of safe trajectory planning under Gaussian mixture model (GMM) uncertainty. We propose a mixed-integer conic approximation approach to solve the chance-constrained trajectory planning problem with deterministic linear systems and polyhedral obstacles. We also develop a Conditional Value-at-Risk (CVaR) method for tackling constraint violation. The proposed methods are validated using state-of-the-art trajectory prediction algorithms and autonomous driving datasets.
IEEE CONTROL SYSTEMS LETTERS
(2022)
Article
Robotics
Tony A. Wood, Maryam Kamgarpour
Summary: This letter investigates the minimization of the longest travel distance for a group of mobile robots with interchangeable goals through polynomial-time approximations of the shortest path search. The proposed algorithm iteratively increases the accuracy of the path planning and provides a certificate for the optimality of the goal assignment. Feasible paths and expanded sample sets are used to determine upper and lower bounds on the shortest paths, respectively, using sampling-based path planning. A case study on multi-robot path planning is presented to demonstrate the application of the proposed method.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Pier Giuseppe Sessa, Maryam Kamgarpour, Andreas Krause
Summary: In this study, we investigate model-based multi-agent reinforcement learning, where the environment transition model is unknown and can only be learned through interactions with the environment. We propose H-MARL (Hallucinated Multi-Agent Reinforcement Learning), an efficient algorithm that balances exploration and exploitation in a general-sum Markov game. By constructing high-probability confidence intervals and updating them based on new data, H-MARL creates an optimistic hallucinated game for the agents to compute equilibrium policies. Experimental results on an autonomous driving simulation benchmark demonstrate that H-MARL learns successful equilibrium policies with only a few interactions and significantly improves performance compared to non-optimistic exploration methods.
INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 162
(2022)
Article
Automation & Control Systems
Heejin Ahn, Colin Chen, Ian M. Mitchell, Maryam Kamgarpour
Summary: The algorithm presents a motion planning approach that ensures safety for autonomous vehicles. By considering a multimodal distribution over uncertainties, it effectively handles discrete decisions in trajectory predictions and offers a computationally efficient solution. The approach demonstrates high safety probability and efficiency compared to conventional methods in simulations.
IEEE CONTROL SYSTEMS LETTERS
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Ilnura Usmanova, Maryam Kamgarpour, Andreas Krause, Kfir Yehuda Levy
Summary: The paper focuses on the important problem of Euclidean projection onto a convex set in constrained optimization tasks. It proposes a simple and efficient primal-dual approach for projection problems with smooth constraints and significantly fewer constraints than dimensions, with a runtime linear in dimension and logarithmic in the inverse of target accuracy. Empirical results demonstrate its performance compared to standard baselines.
INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 139
(2021)
Article
Computer Science, Information Systems
Atousa Elahidoost, Luca Furieri, Maryam Kamgarpour, Elisabetta Tedeschi
Summary: This study aims to minimize DC voltage oscillations in offshore multiterminal HVDC grids using optimal control techniques. Centralized and decentralized optimal linear feedback controllers are proposed, along with the introduction of a DC voltage oscillation index as a potential decision-support criterion.
