'Lyapunov function' diaporamas de présentation

Memory

Memory. Hopfield Network. Content addressable Attractor network. Hopfield Network. Hopfield Network. General Case: Lyapunov function. Neurophysiology. Mean Field Approximation. Null Cline Analysis. What are the fixed points?. E. I. C I. C E. Null Cline Analysis.

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Formations and Obstacle Avoidance in Mobile Robot Control

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Stability

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Distributed Motion Coordination: From Swarming to Synchronization

Hamilton Institute Seminar 06/24/2005. Distributed Motion Coordination: From Swarming to Synchronization. Ali Jadbabaie. Department of Electrical and Systems Engineering and GRASP Laboratory University of Pennsylvania. Distributed Coordination in nature.

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A Convergent Dynamic Window Approach to Obstacle Avoidance & Obstacle Avoidance in Formation

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Revision:

Revision:. What are Lyapunov conditions for stability? What are Lyapunov conditions for GAS? How can we estimate DOA via a Lyapunov function?. Lecture 6. Linear systems Lyapunov matrix equation LaSalle invariance principle. Recommended reading. Khalil Chapter 3 (2 nd edition).

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Intelligent Packet Dropping for Optimal Energy-Delay Tradeoffs for Wireless

A(t). Energy. m (p(t), s(t)). Delay. Intelligent Packet Dropping for Optimal Energy-Delay Tradeoffs for Wireless. Michael J. Neely University of Southern California http://www-rcf.usc.edu/~mjneely/ ( full paper to appear in WiOpt 2006 ). *Sponsored by NSF OCE Grant 0520324. Good. A(t).

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Modeling the Evolution of Decision Rules in the Human Brain

Modeling the Evolution of Decision Rules in the Human Brain. Daniel S. Levine Department of Psychology University of Texas at Arlington Arlington, TX 76019-0528 levine@uta.edu www.uta.edu/psychology/faculty/levine

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Construction of Lyapunov functions with linear optimization

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High Order Sliding Mode, Relative Degree, Finite Time Convergence and Disturbance Rejection

High Order Sliding Mode, Relative Degree, Finite Time Convergence and Disturbance Rejection. CHATTERING !!!. Second Order Sliding Mode, Relative Degree, Finite Time Convergence and Disturbance Reject. R. R. and relative degree is equal to 1. The Problem:.

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Applications of Cellular Neural/Nonlinear Networks in Physics

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Lecture 07 Analysis (III) -- Stability

Lecture 07 Analysis (III) -- Stability. 7.1 Bounded-Input Bounded-Output (BIBO) Stability 7.2 Asymptotic Stability 7.3 Lyapunov Stability 7.4 L inear Approximation of a Nonlinear System. Bounded-Input Bounded-Output (BIBO) stablility. Definition: For any constant N , M >0.

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Resource Allocation for E-healthcare Applications

Resource Allocation for E-healthcare Applications. Qinghua Shen. content. Intro: e-healthcare system Research issues Preliminary results conclusion. Intro: e-healthcare system. Randomness of the requests. Computing: Medical information processing. Body channel. Limited sensor energy.

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Delay Analysis for Max Weight Opportunistic Scheduling in Wireless Systems

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Homework 9

Chapter 10. Optimal Control. Homework 9. Consider again the control system as given before, described by. Assuming the linear control law. Determine the constants k 1 and k 2 so that the following performance index is minimized.

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AUTOMATIC CONTROL THEORY II

Slovak University of Technology Faculty of Material Science and Technology in Trnava. AUTOMATIC CONTROL THEORY II. Adaptive Control. Model reference adaptive control - MRAC one of the main approaches to adaptive control

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EXAMPLES:

EXAMPLES:. Example 1: Consider the system. Calculate the equilibrium points for the system. Plot the phase portrait of the system. Solution:. The equilibrium points must be stationary. Therefore for the first system we have. roots([-1/16 0 0 0 1]) ans = -2.0000

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EXAMPLES:

EXAMPLES:. Example 1: Consider the system. Calculate the equilibrium points for the system. Plot the phase portrait of the system. Solution:. The equilibrium points must be stationary. Therefore for the first system we have. roots([-1/16 0 0 0 1]) ans = -2.0000

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Demand Based Rate Allocation

Demand Based Rate Allocation. Arpita Ghosh and James Mammen {arpitag, jmammen}@stanford.edu. EE 384Y Project 4 th June, 2003. Outline. Motivation Previous work Insight into proportional fairness Demand-based max-min fairness Decentralized algorithm

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