4 edition of Multiagent reconfiguration using hybrid optimal control found in the catalog.
Multiagent reconfiguration using hybrid optimal control
Thesis (M.A.Sc.) -- University of Toronto, 2003.
|Series||Canadian theses = -- Thèses canadiennes|
|The Physical Object|
|Pagination||2 microfiches : negative.|
To implement the proposed ideas, hybrid fuzzy-evolutionary models of forming agents and agencies based on the use of fuzzy coding principles are created and described in the paper. The authors developed a software system to support evolutionary design of agents and multi-agent systems for estimating the effectiveness of the hybrid : Leonid A. Gladkov, Nadezhda V. Gladkova, S. A. Gromov. Cost Performance Based Control Reconfiguration in Multi-Agent Systems Conference Paper (PDF Available) in Proceedings of the American Control Conference June with 77 Reads.
?Hybrid Particle Swarm Optimization and Genetic Algorithm for Multi-UAV Formation Reconfiguration Abstract: The initial state of an Unmanned Aerial Vehicle (UAV) system and the relative state of the system, the continuous inputs of each flight unit are piecewise linear by a Control Parameterization and Time Discretization (CPTD) by: In , a multiagent optimal method based on reinforcement learning was proposed to control a grid connected energy system. e fault isolation and tolerant control applied to the wind turbines has.
1 Consensus of Hybrid Multi-agent Systems Yuanshi Zheng, Jingying Ma, and Long Wang Abstract In this paper, we consider the consensus problem of hybrid multi-agent system. First, the hybrid multi-agent system is proposed which is composed of continuous-time and discrete-time dynamic Size: KB. Multi-agent systems are commonly modeled as hybrid systems with time-driven dynamics describing the motion of the agents or the evolution of physical processes in a given environment, while event-driven behavior characterizes events that may occur randomly (e.g., an agent failure) or in accordance to control policies (e.g., an agent stopping.
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He has experience in Applied Mathematics: optimal control, optimization, numerical methods nonlinear analysis, convex analysis, differential equations and differential inclusions, engineering mathematics; and Control Engineering: hybrid and switched dynamic systems, systems optimization, robust control, control over networks, multiagent systems, robot control, Lagrange mechanics, stochastic.
As reported in earlier work, methods for hybrid optimal control can be used for optimal path planning of multi-agent systems. This paper considers a multi-vehicle transport scenario in which a hybrid model is used to represent the continuous dynamics of the vehicle motion and the dynamics arising from the docking-events between vehicles and a transported by: 1.
Multi-Agent Reconfigurable Embedded Systems: From Modelling to Implementation: /ch The chapter deals with reconfigurable embedded control systems following component-based technologies and/or Architecture Description Languages used today inAuthor: Mohamed Khalgui.
A methodology for designing hybrid controllers large scale, multiagent systems is presented. Our ap- proach is based on optimal control and game theory. The hybrid design is seen as a game between two play- ers: the control, which is to be chosen by the designer and the disturbances that encode the actions of other agents (in a multi-agent.
More formally the control dnsign Multiagent reconfiguration using hybrid optimal control book to regulate the following outputs to zero: =[-),] [-),v] (3) y x+ 0 =Cx+D Verijimtwn of Safety A general framework for det;igning hierarchical hybrid control s,ystemfi using game theory and optimal control principles was Cited by: 7.
In with the optimal control of tie-switches and capacitor banks on the feeders of a large radially operated meshed distribution system, the power losses and voltage deviations are minimized using adopted Evolutionary Algorithm for optimization and fuzzy set theory for scaling of by: The simulation results are given from Fig.
4, Fig. 5, Fig. 6, Fig. 7, Fig. 8, Fig. 9, Fig. 10, Fig. 11, Fig. 12, Fig. And Fig. 3 is our simulink diagram. As can be seen in Fig. 4, Fig. 5, the proposed optimal control law clearly drives some agent (especially agent 5) away from the obstacle and then all five agents reach a consensus.
Fig. 6, Fig. 7 present the time histories of the five Cited by: The new edition of an introduction to multiagent systems that captures the state of the art in both theory and practice, suitable as textbook or reference.
Multiagent systems are made up of multiple interacting intelligent agents—computational entities to some degree autonomous and able to cooperate, compete, communicate, act flexibly, and exercise control over their behavior within the.
Multiagent Systems, Second Edition, 2e by, Toggle navigation. Cart (0 scope of the book—to the best of my knowledge there is no comparable book on the market—it is as comprehensive as a book on multiagent systems can get without becoming more than one book.
Characterizing Multiagent Planning and Control (pg. ) 3. Abstract: This paper addresses the problem of distributed optimal consensus control for a continuous-time heterogeneous linear multiagent system subject to time varying input delays.
First, by discretization and model transformation, the continuous-time input-delayed system is converted into a discrete-time delay-free by: Abstract.
In order to cope with load management in power grid systems, this paper presents a hybrid multi-agent framework. This framework integrates the advantages of both centralized and decentralized architectures to achieve both accurate decisions and quick response, and avoid the single point of Cited by: 5.
In particular, we consider control systems with monotonically increasing dimensions of the state vector. The change of the state dimension has the character of a jump and is modeled by an impulsive hybrid system.
The paper proposes an effective computational procedure for the above optimal tracking control problem in a multiagent setting. Our approach is based on optimal control and game theory. The hybrid design is seen as a game between two players: the control, which is to be chosen by the designer and the disturbances that encode the actions of other agents (in a multi-agent setting), the actions of high level controllers or the usual unmodeled environmental disturbances.
Hybrid online learning control in networked multiagent systems: A survey Jorge I. Poveda Department of Electrical and Computer Engineering, University of Cited by: 4. To solve the optimal reactive power dispatch (ORPD), this article proposes a new method, called hybrid multiagent-based particle swarm optimization, which does not allow the search to struck at.
Abstract. We present an optimal control framework for persistent monitoring problems where the objective is to control the movement of multiple cooperating agents to minimize an uncertainty metric in a given mission space, while seeking to maintain some upper bound constraints on uncertainty by: 4.
A design and verification methodology for hybrid dynamical systems, based on optimal control and game theory, is presented. The hybrid design is seen as a game between two players. One is the disturbances that enter the by: 2. The control law solves the optimal consensus problem for multiagent systems with measured I/O information, and does not rely on the model of multiagent systems.
A numerical example is provided to illustrate the effectiveness of the proposed by: Optimal Synchronization Control of Multiagent Systems With Input Saturation via Off-Policy Reinforcement Learning Abstract: In this paper, we aim to investigate the optimal synchronization problem for a group of generic linear systems with input saturation.
To seek the optimal controller, Hamilton-Jacobi-Bellman (HJB) equations involving Cited by: system. Developing learning-based control protocols that can ﬁnd an optimal control strategy in adversarial environments using only measured data is of utmost importance to increase autonomy for multi-agent systems.
In this paper, a uniﬁed resilient model-free RL-based dis-tributed control protocol for leader-follower multi-agent sys-Author: Rohollah Moghadam, Hamidreza Modares.
y discusses how to use the framework to model multiagent systems, and notes that this vocabulary provides a fresh perspective on partially observable Markov decision processes. Then, section 9 concludes the chapter with a series of observations about reinforcement learning, stochastic optimal control, and our universal by: 1.Multiagent systems with hybrid interacting dynamics are groups of systems whose in-dividual components have hybrid dynamics that affect each other’s behavior at both the continuous and discrete levels.
These systems are found in a wide range of applications including multivehicle systems, networks of sensors, actuators and embedded control sys.Apart from those indicated above, MAS might be applied to power generation control in microgrid, with the exemplification of the power control of the interconnected line between a microgrid and a large power system using mobile agent technologies and a MAS solution to service restoration by: