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American control Conference (ACC)

作者： Seok-beom Lee G.G. Yen Intelligent Systems and Control Laboratoly School of Electrical and Computer Engineering Oklahoma State University Stillwater OK USA

Takagi-Sugeno (TS) fuzzy models have been the popular choice for multi-model based nonlinear system identification for controller design. However, the interpretation of the model from a dynamical systems point of view is still unclear. In this paper, we investigate the interpretation of the TS fuzzy models in terms of local dynamics of nonlinear systems in state space. The investigation is based on the comparison between the constant-affine recursive i/o equation and constant-affine state space representations. We show that the true local dynamics is not identifiable by TS fuzzy models by analysis and example. For the identification of constant-affine state space models, a subspace identification algorithm is proposed to identify the true local dynamics with zero initial conditions.

关键词： Takagi-Sugeno model Fuzzy systems Least squares approximation control system synthesis Nonlinear systems State-space methods Equations Function approximation Fuzzy control Nonlinear control systems

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Reinforcement learning algorithms for robotic navigation in dynamic environments

Reinforcement learning algorithms for robotic navigation in ...

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International Joint Conference on Neural Networks (IJCNN)

作者： G. Yen T. Hickey Intelligent System and Control Laboratory School of Electrical and Computer Engineering Oklahoma State University Stillwater OK USA

An issue of primary importance in the field of reinforcement learning (RL) is the tradeoff between exploration and exploitation. Balancing exploration and exploitation becomes even more crucial in an intelligent agent designed to operate in dynamic environments. Three methods are proposed to improve the performance of traditional RL methods such as Q-learning. The proposed improvements are: the addition of a forgetting mechanism, the use of feature-based state inputs, and the addition of a hierarchical structure to an RL agent. Experimental results are presented and utilized in evaluation of the proposed methods. The proposed algorithms are compared to established methods and to theoretically optimal solutions.

关键词： Navigation Robot sensing systems Automatic control control systems Mobile robots intelligent robots Machine learning algorithms Optimal control Orbital robotics Robot control

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An extension of weighted strategy sharing in cooperative Q-learning for specialized agents

An extension of weighted strategy sharing in cooperative Q-l...

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International Conference on Neural Information Processing

作者： S.M. Eshgh M.N. Ahmadabadi School of Intelligent Systems Institute of Studies in Theoretical Physics and Mathematics Tehran Iran Control and Intelligent Processing Center of Excellence and Robotics Laboratory Department of Electrical and Computer Engineering University of Tehran Tehran Iran

Using other agents' experiences and knowledge, a learning agent may learn faster, make fewer mistakes, and create some rides for unseen situations. These benefits will be gained if the learning agents know the area of expertise and the expertness values of each other. In this paper, some Q-learning agents with different skills and expertness levels cooperate in learning. The agents use some criteria to judge others information and knowledge. Four expertness criterion, certainty and entropy measures are used to assign degrees of importance to others' Q-Tables. Effects of measuring these values based on their whole Q-Table, a portion of Q-Tables that reflects their proficiencies, and the states in Q-Tables on the learning quality are studied. Simple strategy sharing and two different weighted strategy-sharing methods are used to combine the acquired knowledge from different agents.

关键词： intelligent robots intelligent agent Process control control systems intelligent control Laboratories intelligent systems Physics Mathematics Entropy

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Certainty and expertness-based credit assignment for cooperative Q-Learning agents with an AND-type task

Certainty and expertness-based credit assignment for coopera...

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International Conference on Neural Information Processing

作者： A. Harati M.N. Ahmadabadi School of Intelligent Systems Institute of Studies in Theoretical Physics and Mathematics Tehran Iran Control and Intelligent Processing Center of Excellence & AI and Robotics Laboratory Department of Electrical and Computer Engineering University of Tehran Tehran Iran

In multiagent reinforcement learning, inter-agent credit assignment is a fundamental problem, since a single scalar reinforcement signal is the only reliable feedback that teams of learning agents receive. This problem is more critical in groups of independent learners with a joint task. In this research, it is assumed that a critic agent receives the environment feedback and assigns a proper credit to each agent using some measures. Three of such measures for a team of cooperative agents with a parallel and AND-type task are introduced. These measures somehow compare the agents' knowledge. One of these criteria, called normal expertness, is a non-relative measure while two other ones (certainty and relative normal expertness) are relative measure. It is experimentally shown that relative measures work better as they contain more information for the critic agent.

关键词： Feedback intelligent agent intelligent robots Process control control systems intelligent control Artificial intelligence Robot kinematics Physics computing Reliability engineering

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ROBUST CONGESTION control IN HIGH SPEED COMMUNICATION NETWORKS: A MODEL PREDICTIVE control APPROACH

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IFAC Proceedings Volumes 2002年第1期35卷 103-108页

作者： Hua O. Wang Yongru Gu Huajing Fang Laboratory for Intelligent and Nonlinear Control (LINC) Department of Electrical and Computer Engineering Duke University Durham NC 27708-0291 USA Center for Nonlinear and Complex Systems Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China

In this paper, the design of explicit rate-based congestion control in high speed communication networks is considered. At a bottleneck node, there are multiple best-effort sources competing with other high priority cross traffic sources. The goal of congestion control is to achieve high link utilization, low packet loss, low delay, and fairness among the best-effort sources. In this paper, the high priority traffic is described by an autoregressive integrated moving average (ARIMA) process. To deal with the propagation delays associated with the best-effort sources, model predictive control, particularly, generalized predictive control, techniques are proposed to solve the congestion problem here. It is demonstrated that the proposed controller performs well and is robust to delay uncertainties. In addition, in a multiple-nodes configuration, the controller provides max-min fairness.

关键词： Communication control applications Model-based control Predictive control Communication networks Time delay Robustness

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Implementation of virtual factory using MMS companion standard

Implementation of virtual factory using MMS companion standa...

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IEEE International Workshop on Factory Communication systems (WFCS)

作者： Kwan-Joo Myoung Dong-Sung Kim Wook Hyun Kwon Engineering Research Center for Advanced Control Instrument Control Information Systems Laboratory School of Electrical Engineering and Computer Science Seoul National University Seoul South Korea

In this paper, a virtual factory using the manufacturing message specification (MMS) companion standard is implemented. The MMS companion standard (9506-3/spl sim/9506-7) and virtual machines are designed and implemented for robot, numerical controller (NC), programmable logic controller (PLC), and process control (PC). Finally, the MMS Internet monitoring system (MIMS) is developed for the testing system.

关键词： Production facilities Virtual manufacturing TCPIP Programmable control Virtual machining Condition monitoring Virtual machine monitors Manufacturing automation computer integrated manufacturing control systems

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Entire region filling in indoor environments using neural networks

Entire region filling in indoor environments using neural ne...

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World Congress on intelligent control and Automation (WCICA)

作者： Chaomin Luo S.X. Yang M. Meng Advanced Robotics and Intelligent Systems (ARIS) Laboratory School of Engineering University of Guelph Guelph Canada Department of Electrical and Computer Engineering University of Alberta Edmonton Canada

Entire region filling is a special type of robot path planning strategy that requires the mobile robot to cover every part of the whole workspace, which has many applications such as cleaning robots, vacuum cleaners, painter robots, land mine detectors, lawn mowers, and window cleaners. In this paper, a novel biologically inspired neural network approach is proposed for entire region filling with obstacle avoidance of a mobile cleaning robot in a nonstationary environment. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation or an additive equation derived from Hodgkin and Huxley's (1952) membrane equation. There are only local lateral connections among neurons. Thus the computational complexity linearly depends on the neural network size. The robot path is autonomously generated from the dynamic activity landscape of the neural network and the previous robot location. The proposed model algorithm is computationally efficient. It can deal with an unstructured environment with irregular obstacles. The effectiveness of the proposed model is demonstrated by simulation results.

关键词： Filling Indoor environments Neural networks Mobile robots Equations Cleaning Landmine detection Neurons Computational modeling Path planning

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Cooperative Q-learning with heterogeneity in actions

Cooperative Q-learning with heterogeneity in actions

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IEEE International Conference on systems, Man and Cybernetics

作者： S.M. Reza MirFattah M.N. Ahmadabadi Institute for Studies on Theoretical Physics and Mathematics School of Intelligent Systems Tehran Iran Robotics and AI Laboratory Department of Electrical and Computer Engineering University of Tehran Tehran Iran

Cooperation in learning improves the speed of convergence and the quality of learning. Special care is needed when heterogeneous agents cooperate in learning. It is discussed that, cooperation in learning may cause the learning process to diverge if heterogeneity is not handled properly. In this paper, it is assumed that two heterogeneous Q-learning agents cooperate to learn. The heterogeneity is assumed in their action order (and not in their action set). A Q-learning-based method is introduced for the agents to learn the mapping among their actions. It is shown that, the agents are able to learn this mapping while cooperating in learning. Some simulation results are reported to show the effectiveness of the proposed method.

关键词： intelligent agent intelligent robots Humans Telecommunication control Artificial intelligence Laboratories intelligent systems Physics Mathematics Convergence

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A new approach to credit assignment in a team of cooperative Q-learning agents

A new approach to credit assignment in a team of cooperative...

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IEEE International Conference on systems, Man and Cybernetics

作者： A. Harati M.N. Ahmadabadi Institute for Studies on Theoretical Physics and Mathematics School of Intelligent Systems Tehran Iran Robotics and AI Laboratory Department of Electrical and Computer Engineering University of Tehran Tehran Iran

Q-learning is widely used in many multi agent systems. In most cases, a separate critic is considered for qualifying each individual agent behavior or it is assumed that the critic is completely aware of effects of all agents' actions on the team qualification. But, in many cases, the role of each team member in the group performance is not known. In order to distribute a common credit among the agents, a suitable criterion must be provided to estimate the role of each agent in the team performance and to judge if an agent has done a wrong action. In this paper two such criteria, named certainty and expertness, for a team of agents with parallel tasks are introduced. In addition, two methods for reinforcing the agents based on the proposed measures are provided. Some simulation results are also reported to show the effectiveness of the proposed measures and methods.

关键词： intelligent agent Learning systems Multiagent systems intelligent sensors Artificial intelligence Laboratories intelligent systems intelligent robots Physics Mathematics

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A new model of the visual feedback coordinate transformation in humans based on disturbance noise and feedback error that accounts for time delays

A new model of the visual feedback coordinate transformation...

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IEEE International Workshop on intelligent Robots and systems (IROS)

作者： E. Oyama K.F. MacDorman T. Maeda S. Tachi A. Agah Science and Technology (AIST) National Institute for Advanced Industrial Science and Technology Tsukuba Ibaraki Japan Intelligent Robotics Laboratory Graduate School of Systems Engineering Wakayama University Wakayama Japan School of Engineering University of Tokyo Bunkyo Tokyo Japan Department of Electrical Engineering and Computer Science University of Kansas Lawrence KS USA

An essential factor in understanding the motor learning capability of humans, is the coordinate transformation learning of the visual feedback controller. Although a number of learning models for the visual feedback controller have been proposed, none has been able to establish a definitive approach. In our previous work, we have suggested a learning model that uses disturbance noise and the feedback error signal to learn the human visual feedback controller's coordinate transformation. However, the model does not fully consider the time delay in the visual feedback control loop. This paper presents a modified learning model taking into account the time delay and the convergence properties of the model. Numerical simulations are presented to illustrate the effectiveness of the proposed approach.

关键词： Humans Delay effects Adaptive control Error correction Biological system modeling Feedback control Convergence Kinematics intelligent robots Velocity control

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