Article
Engineering, Electrical & Electronic
Tao Han, Wei Xing Zheng
Summary: This paper addresses the bipartite output consensus problem for heterogeneous MASs under antagonistic interactions by designing two distributed controllers with state feedback control and output feedback control. It is proven that bipartite output consensus for heterogeneous MASs can be guaranteed under structurally balanced graph through Lyapunov theory and output regulation technique. Numerical examples are provided to demonstrate the validity of the derived results.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2021)
Article
Automation & Control Systems
Jieshuai Wu, Maobin Lu, Fang Deng, Jie Chen
Summary: This article investigates the cooperative robust output regulation problem of multi-agent systems with general linear uncertain dynamics using event-triggered control. In comparison to the practical solution achieved by a distributed event-triggered control law, this study proposes a distributed event-triggered output feedback control law and a class of dynamic event-triggered mechanisms to solve the problem accurately.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Engineering, Multidisciplinary
Guanglei Zhao, Changchun Hua, Shuang Liu
Summary: This paper investigates the consensus problem in multi-agent systems using sampled-data dynamic output feedback control. A distributed control law is designed based on sampled relative output information between neighboring agents. A closed-loop system model with hybrid dynamics is constructed for consensus analysis and choice of sampling period. Lyapunov-based analysis is presented to develop stability conditions. Compared to existing works, observer design is not required, and only sampled relative output information is used, with the ability to explicitly compute the maximum allowable sampling period.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2022)
Article
Automation & Control Systems
Matteo Santilli, Mauro Franceschelli, Andrea Gasparri
Summary: A novel distributed local interaction protocol is proposed in this paper for networks of multi-agent systems in a multi-dimensional space under directed time-varying graph, aiming to achieve secure rendezvous or static containment of a set of leader agents. The paper considers the scenario where anonymous adversarial agents may intrude the network or be hijacked by a cyber-attack and shows that the proposed strategy guarantees the achievement of the global objective despite the influence of undetectable adversaries. The convergence properties of the protocol are characterized in terms of the characteristics of the underlying network topology, and the theoretical results are verified through numerical simulations and examples.
Article
Automation & Control Systems
Zhen-Guo Liu, Hongli Dong, Weixing Chen, Weidong Zhang
Summary: This article investigates the adaptive regulation problem of uncertain delayed nonlinear systems and presents two unified adaptive control methods to achieve global asymptotic stability by introducing dynamic gain transformation and using homogeneous domination method.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2022)
Article
Engineering, Electrical & Electronic
Subashish Datta
Summary: This brief addresses the output consensus problem in a multi-agent system, proposing a distributed dynamic output feedback control architecture. The architecture consists of local controllers for agents and a network gain, designed based on the number of zeros in the agent transfer function and the root-locus of a unity feedback system. Output consensus is achievable with an integral type controller for a class of agents, without the need for communication between local controllers, as demonstrated through numerical examples.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2022)
Article
Mathematics
Long Jian, Yongfeng Lv, Rong Li, Liwei Kou, Gengwu Zhang
Summary: This paper investigates the containment control problem of linear multi-agent systems subject to external disturbances in a directed graph communication network, where distributed disturbance observer-based event-triggered controller is utilized to save communication costs and energy. The utilization of relative outputs of neighboring followers at the same triggered instant helps to avoid Zeno behavior. The effectiveness of the proposed methodology is demonstrated through a simulation example.
Article
Automation & Control Systems
Yutao Tang, Kui Zhu
Summary: This article focuses on the distributed optimal output consensus problem for high-order multi-agent systems with parametric uncertainties, proposing a distributed output feedback integral controller to solve the problem effectively under mild graph connectivity conditions.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2022)
Article
Automation & Control Systems
Hanfeng Li, Lihua Xie, Xianfu Zhang, Weihao Pan
Summary: This article focuses on the distributed consensus control problem for nonlinear multi-agent systems subject to sensor uncertainty. A new time-varying gain approach is proposed to achieve leader-follower consensus of nonlinear multi-agent systems and handle the unknown growth rate and uncertain sensor sensitivity.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Automation & Control Systems
Junjie Jiao, Harry L. Trentelman, M. Kanat Camlibel
Summary: This paper addresses the H-2 suboptimal output synchronization problem for heterogeneous linear multi-agent systems, providing a design method to calculate output feedback-based protocols that ensure cost is less than a given upper bound while achieving output synchronization. Experimental results demonstrate the performance of the proposed protocols.
SYSTEMS & CONTROL LETTERS
(2021)
Article
Automation & Control Systems
Jan Lunze
Summary: This paper investigates the robustness of synchronised linear systems in the face of parameter deviations, revealing that even infinitesimally small changes can disrupt synchronization in oscillator networks. The explanation given in the paper shows that synchronization occurs in a part of the state space unobservable by the networked controller.
Article
Automation & Control Systems
Xiufeng Mu, Zhou Gu, Lingzhi Hua
Summary: This paper investigates the problem of fuzzy model-based leader-following consensus control for multi-agent systems under deception attacks. A novel memory-based event-triggered scheme is proposed to reduce redundant data transmission and achieve faster leader-following consensus. Sufficient conditions for achieving consensus in the presence of deception attacks are derived using Lyapunov-Krasovskii technique.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2022)
Article
Computer Science, Information Systems
Qiuzhen Wang, Jiangping Hu, Yanzhi Wu, Yiyi Zhao
Summary: This paper investigates the communication network in a linear heterogeneous clustered multi-agent system (CMAS) with several clusters, each represented by a strongly connected digraph and a leader. Intra-cluster agents continuously communicate with their neighbors, while inter-cluster leaders communicate at discrete reset time. The paper proposes a reduced-order observer-based reset output feedback controller to solve the output synchronization problem in the CMAS. A reset internal model and a reset reduced-order observer are developed to address output synchronization and estimate agent states. An output feedback control strategy utilizes output information, internal model state, and observer state. Sufficient conditions for output synchronization are obtained and the efficiency of the proposed strategy is demonstrated through an example.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Luka Martinovic, Zarko Zecevic, Bozo Krstajic
Summary: In this paper, the cooperative output regulation of heterogeneous linear multi-agent systems is studied. A novel distributed observer approach is proposed to synchronize agents' outputs to a reference trajectory generated by a leader, while rejecting disturbance. A unified framework based on H-8 theory is established for both introspective and non-introspective agent networks, with protocols that reduce communication costs and satisfy local stability conditions.
Article
Automation & Control Systems
Tianping Zhang, Yu Hua, Xiaonan Xia, Yang Yi
Summary: This article presents a unified adaptive neural event-triggered control strategy for uncertain MIMO nonlinear systems, transforming constrained systems into unconstrained ones using a nonlinear mapping, employing dynamic signals and control techniques, and proving the boundedness of signals in the closed-loop system through stability analysis, while also ensuring compliance with constraint conditions.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2021)
Article
Energy & Fuels
Carlos A. Ruiz-Zea, Edgar Medina, Abner Ramirez, Ali Mehrizi-Sani, Jose de Jesus Chavez, Ali Davoudi, Mohamed Abdel-Rahman
IEEE TRANSACTIONS ON ENERGY CONVERSION
(2020)
Article
Automation & Control Systems
Hamidreza Modares, Bahare Kiumarsi, Frank L. Lewis, Frank Ferrese, Ali Davoudi
IEEE TRANSACTIONS ON CYBERNETICS
(2020)
Article
Engineering, Electrical & Electronic
Tuncay Altun, Ramtin Madani, Ali Davoudi
Summary: This paper addresses the challenges of state estimation and topology identification in DC networks, introducing a non-convex nuclear norm estimator to tackle the nonlinear relations between sensor measurements and state variables. The proposed technique is validated through numerical results on modified IEEE systems and experimental validation on a real-time hardware-in-the-loop platform for a converter-augmented 14-bus system.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2021)
Article
Automation & Control Systems
Pappa Natarajan, Rohollah Moghadam, S. Jagannathan
Summary: An online adaptive deep neural network (DNN) scheme is introduced for the tracking control of a nonlinear bioprocess, demonstrating closed-loop tracking control for a desired yield profile can be achieved with only two inputs. The proposed controller exhibits self-learning capability under closed loop conditions and does not require an offline learning phase.
JOURNAL OF PROCESS CONTROL
(2021)
Article
Energy & Fuels
Paolo Roberto Massenio, David Naso, Frank L. Lewis, Ali Davoudi
Summary: This article provides a data-driven optimal solution to reduce interactions between different control loops of power buffers while minimizing a closed-loop performance function. Reinforcement learning methods are used to deal with optimal control of nonlinear systems, and a Tabu Search method is employed to address the resulting combinatorial problem. The proposed solutions are validated in a controller/hardware-in-the-loop environment for a DC microgrid.
IEEE TRANSACTIONS ON ENERGY CONVERSION
(2021)
Article
Engineering, Electrical & Electronic
Tuncay Altun, Ramtin Madani, Ali Davoudi
Summary: This paper proposes a topology-cognizant optimal power flow paradigm for multi-terminal direct current grids, addressing voltage violations by optimizing controller set-points and transmission line switching status. The method tackles non-convexities in power flows and converter loss equations through a mixed-integer second-order cone programming approach, determining optimal switching statuses using branch-and-bound search. Numerical results support the effectiveness of the proposed method, with experimental validation on a real-time hardware-in-the-loop platform.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2021)
Article
Automation & Control Systems
Ajay Pratap Yadav, Ramtin Madani, Navid Amiri, Juri Jatskevich, Ali Davoudi
Summary: This article identifies the parameters of an induction machine using limited and nonintrusive observations and proposes a method to solve the parameter extraction problem. The proposed method is experimentally verified on an induction machine with missing measured data, and the results show better performance compared to conventional testing.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2022)
Article
Computer Science, Artificial Intelligence
Rohollah Moghadam, Pappa Natarajan, Sarangapani Jagannathan
Summary: This article presents an online optimal adaptive regulation method based on multilayer neural networks, which can effectively handle partially uncertain dynamics in nonlinear discrete-time systems. The actor-critic framework is used to estimate control inputs and value functions, with weights of the networks adjusted using instantaneous control input error and temporal difference. The proposed approach does not require selecting basis functions or their derivatives, and the Lyapunov method proves that the state vector, critic, and actor NN weights are bounded.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Rohollah Moghadam, Sarangapani Jagannathan
Summary: This article introduces an actor-critic neural network-based online optimal adaptive regulation method for a class of nonlinear continuous-time systems. The method considers known state and input delays, as well as uncertain system dynamics. The temporal difference error (TDE) dependent on state and input delays is derived using actual and estimated value function and reinforcement learning. The critic neural network (NN) weights are tuned at each sampling instant based on the instantaneous integral TDE. A novel identifier is used to estimate the control coefficient matrices and obtain the estimated control policy. The boundedness of various components in the system is proved through Lyapunov analysis, and simulation results demonstrate the effectiveness of the proposed approach.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Yang-Yang Qian, Mushuang Liu, Yan Wan, Frank L. Lewis, Ali Davoudi
Summary: This article investigates differential graphical games for linear multiagent systems with a leader on fixed communication graphs. A distributed adaptive Nash equilibrium solution is proposed, which is not only Nash but also fully distributed in the sense that each agent only uses local information of its own and its immediate neighbors. The solution achieves both asymptotic stability and global Nash equilibrium properties.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Rohollah Moghadam, Vignesh Narayanan, Sarangapani Jagannathan
Summary: This article proposes an event-triggered optimal adaptive output-feedback control design approach for linear time-invariant systems with state delay and uncertain internal dynamics, using integral reinforcement learning (IRL). The approach formulates the general optimal control problem into a game-theoretic framework and derives the output game delay algebraic Riccati equation (OGDARE) and optimal control policy. A hybrid learning scheme is proposed to relax the knowledge of internal dynamics and compute the estimated optimal control policy. The effectiveness of the approach is demonstrated through a simulation example.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Information Systems
Omar Ali Beg, Luan Viet Nguyen, Taylor T. Johnson, Ali Davoudi
Summary: Modern cyber-physical microgrids are vulnerable to cyber-attacks and device failures, and this study utilizes the parametric time-frequency logic framework to detect these anomalies.
Proceedings Paper
Omar Ali Beg, Ajay P. Yadav, Taylor T. Johnson, Ali Davoudi
2020 RESILIENCE WEEK (RWS)
(2020)
Proceedings Paper
Automation & Control Systems
Paolo R. Massenio, Gianluca Rizzello, David Naso, Frank L. Lewis, Ali Davoudi
2020 IEEE 16TH INTERNATIONAL CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING (CASE)
(2020)
Article
Engineering, Electrical & Electronic
Tuncay Altun, Ramtin Madani, Ajay Pratap Yadav, Adnan Nasir, Ali Davoudi
IEEE TRANSACTIONS ON POWER SYSTEMS
(2020)