scholarly journals Control and Optimization of Network in Networked Control System

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Wang Zhiwen ◽  
Sun Hongtao
Keyword(s):  
Numerical Simulation ◽  
Linear Time ◽  
Networked Control System ◽  
Network Congestion ◽  
Linear Quadratic ◽  
Linear Time Invariant ◽  
Simulation Results ◽  
And Control ◽  
Time Invariant

In order to avoid quality of performance (QoP) degradation resulting from quality of service (QoS), the solution to network congestion from the point of control theory, which marks departure of our results from the existing methods, is proposed in this paper. The congestion and bandwidth are regarded as state and control variables, respectively; then, the linear time-invariant (LTI) model between congestion state and bandwidth of network is established. Consequently, linear quadratic method is used to eliminate the network congestion by allocating bandwidth dynamically. At last, numerical simulation results are given to illustrate the effectiveness of this modeling approach.

Dynamic Polytope Function and Control Modeling

2001 ◽  
Author(s):  
Du Wang ◽  
Zhongyang Guo ◽  
Ichiro Hagiwara
Keyword(s):  
Linear Time ◽  
Synthesis Method ◽  
Suspension System ◽  
Observer Design ◽  
Vehicle Suspension ◽  
Linear Time Invariant ◽  
Vehicle Suspension System ◽  
Linear Time Invariant System ◽  
And Control ◽  
Time Invariant

Abstract This study is motivated from the investigation of vehicle suspension system with changeable damping and variant stiffness parameters. Such suspension system can be modeled as a dynamic polytope based on the mapping of affinely changed parameters. According to the polytopic dynamics decomposition, knowledge of linear time invariant system can be applied to each polytope vertex and the time variant system is solved by the polytope convex synthesis method. For time variant vehicle suspension system, the different model structures for control purposes are formulated. A quarter-car is taken as the example for demonstration in observer design and nonlinear control design.

Modeling Human Multichannel Perception and Control Using Linear Time-Invariant Models

2008 ◽  
Vol 31 (4) ◽  
pp. 999-1013 ◽  
Author(s):  
F. M. Nieuwenhuizen ◽  
P. M. T. Zaal ◽  
M. Mulder ◽  
M. M. Van Paassen ◽  
J. A. Mulder
Keyword(s):  
Linear Time ◽  
Linear Time Invariant ◽  
And Control ◽  
Time Invariant

Adaptive Consensus Tracking for Fractional-Order Linear Time Invariant Swarm Systems

2014 ◽  
Vol 9 (3) ◽  
Author(s):  
Mojtaba Naderi Soorki ◽  
Mohammad Saleh Tavazoei
Keyword(s):  
Fractional Order ◽  
Linear Time ◽  
Adaptive Controller ◽  
Order Linear ◽  
Linear Time Invariant ◽  
Consensus Tracking ◽  
Simulation Results ◽  
Interactive Dynamics ◽  
Time Invariant ◽  
Adaptive Stabilizer

This paper presents an adaptive controller to achieve consensus tracking for the fractional-order linear time invariant swarm systems in which the matrices describing the agent dynamics and the interactive dynamics between agents are unknown. This controller consists of two parts: an adaptive stabilizer and an adaptive tracker. The adaptive stabilizer guarantees the asymptotic swarm stability of the considered swarm system. Also, the adaptive tracker enforces the system agents to track a desired trajectory while achieving consensus. Numerical simulation results are presented to show the effectiveness of the proposed controller.

scholarly journals On calculation of recursive M- and GM-estimates in LQG control systems

2008 ◽  
Vol 48 ◽  
Author(s):  
Rimantas Pupeikis
Keyword(s):  
Maximum Likelihood ◽  
Linear Time ◽  
Parametric Identification ◽  
Parameter Estimates ◽  
Linear Quadratic ◽  
Unknown Parameters ◽  
Linear Time Invariant ◽  
Identification Approach ◽  
Recursive Techniques ◽  
Time Invariant

The aim of the given paper is development of a parametric identification approach for a closedloop system when the parameters of a discrete-time linear time-invariant (LTI) dynamic system as well as that of LQG (Linear Quadratic Gaussian) controller are not known and ought to be calculated. The recursive techniques based on an the maximum likelihood(M) and generalized maximum likelihood(GM) estimator algorithms are applied here in the calculation of the system as well as noise filter parameters. Afterwards, the recursive parameter estimates are used in each current iteration to determine unknown parameters of the LQG-controller, too. The results of numerical simulation by computer are discussed.

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