Network sensitivity function, optimization and robust performance in dynamic average consensus

2021 ◽  
Author(s):  
Shiqi Zhang ◽  
Zhongkui Li ◽  
Bing Li ◽  
Zhengmin Liu
Keyword(s):  
Sensitivity Function ◽  
Function Optimization ◽  
Robust Performance ◽  
Average Consensus ◽  
Network Sensitivity

Wideband CDMA network sensitivity function

1999 ◽  
Vol 17 (10) ◽  
pp. 1781-1793 ◽  
Author(s):  
S. Glisic ◽  
P. Pirinen
Keyword(s):  
Sensitivity Function ◽  
Wideband Cdma ◽  
Network Sensitivity

A New Formulation of Multiple-Input Multiple-Output Quantitative Feedback Theory

1996 ◽  
Vol 118 (4) ◽  
pp. 748-752 ◽  
Author(s):  
Yongdong Zhao ◽  
Suhada Jayasuriya
Keyword(s):  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Sensitivity Function ◽  
Constructive Approach ◽  
Robust Performance ◽  
Existence Of A Solution ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Performance Problem ◽  
New Formulation

The QFT robust performance problem in its entirety may be reduced to an H∞ problem by casting each specification as a frequency domain constraint on the nominal sensitivity function and the complementary sensitivity function. It is shown that the existence of a solution to a standard Ha. problem guarantees a solution to the QFT problem whereas the existence of a QFT solution does not necessarily guarantee an H∞ solution. A solution obtained via this formulation for the QFT problem is in general more conservative when compared to what may be obtained from classical QFT loopshaping. However, one does not have to restrict the QFT controller to be diagonal as is usually done in MIMO-QFT. In addition, a simple constructive approach is provided for the design of a prefilter matrix for MIMO systems. In the standard QFT approach, the synthesis of a prefilter matrix for the MIMO case is much more involved than that of the SISO case.

A Performance Enhancement Scheme for Robust Repetitive Control System

2004 ◽  
Vol 126 (1) ◽  
pp. 224-229 ◽  
Author(s):  
Byung-Sub Kim ◽  
Tsu-Chin Tsao
Keyword(s):  
Control System ◽  
Performance Enhancement ◽  
Repetitive Control ◽  
Sensitivity Function ◽  
Robust Performance ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Model Stability ◽  
Filter Structure ◽  
Low Pass

This paper presents a method for enhancing the performance of a digital repetitive control system. The performance at the fundamental frequency and its harmonics of repetitive exogenous signals is improved by applying a modified low-pass qz,z−1 filter structure in the repetitive signal generator of the internal model. Stability and robust performance is achieved through μ-synthesis. The modified low-pass filter is motivated by the attempt to reduce the sensitivity function by squaring it. The magnitude of the sensitivity function is substantially reduced at the periodic reference frequencies since the value is already much less than unity. The proposed approach is demonstrated by its implementation on an electrohydraulic actuator for tracking periodic cam-like profiles.

An H∞ Formulation of Quantitative Feedback Theory

1998 ◽  
Vol 120 (3) ◽  
pp. 305-313 ◽  
Author(s):  
Yongdong Zhao ◽  
Suhada Jayasuriya
Keyword(s):  
Stability Criterion ◽  
Parametric Uncertainty ◽  
Sensitivity Function ◽  
Robust Performance ◽  
Existence Of A Solution ◽  
Weighting Functions ◽  
Performance Constraints ◽  
Small Gain ◽  
Performance Problem ◽  
Frequency Weighting

The QFT robust performance problem in its entirety may be reduced to an H∞ problem by casting each specification as a frequency domain constraint either on the nominal sensitivity function or the complementary sensitivity function. In order to alleviate the conservative nature of a standard H∞ solution that is obtainable for a plant with parametric uncertainty we develop a new stability criterion to replace the small gain condition. With this new stability criterion it is shown that the existence of a solution to the standard H∞ problem guarantees a solution to the QFT problem. Specifically, we provide an explicit characterization of necessary frequency weighting functions for an H∞ embedding of the QFT specifications. Due to the transparency in selecting the weighting functions, the robust performance constraints can be easily relaxed, if needed, for the purpose of assuring a solution to the H∞ problem. Since this formulation provides only a sufficient condition for the existence of a QFT controller one can then use the resulting H∞ compensator to initiate the QFT loop shaping step.

Differentiation Coherence Algorithm for Steady Power Flow Control

2015 ◽  
Vol 9 (1) ◽  
pp. 107-116 ◽  
Author(s):  
Yang Liu-Lin ◽  
Hang Nai-Shan
Keyword(s):  
Flow Control ◽  
Extreme Point ◽  
Power Flow ◽  
Reactive Power ◽  
Small Deviation ◽  
Function Optimization ◽  
Flow Mode ◽  
Power Flow Control ◽  
Active And Reactive Power ◽  
Variable Inequality

This paper researched steady power flow control with variable inequality constraints. Since the inverse function of power flow equation is hard to obtain, differentiation coherence algorithm was proposed for variable inequality which is tightly constrained. By this method, tightly constrained variable inequality for variables adjustment relationships was analyzed. The variable constrained sensitivity which reflects variable coherence was obtained to archive accurate extreme equation for function optimization. The hybrid power flow mode of node power with branch power was structured. It also structured the minimum variable model correction equation with convergence and robot being same as conventional power flow. In fundamental analysis, the effect of extreme point was verified by small deviation from constrained extreme equation, and the constrained sensitivity was made for active and reactive power. It pointed out possible deviation by using simplified non-constrained sensitivity to deal with the optimization problem of active and reactive power. The control solutions for power flow for optimal control have been discussed as well. The examples of power flow control and voltage management have shown that the algorithm is simple and concentrated and shows the effect of differential coherence method for extreme point analysis.

A microbial treatment population dynamics optimization approach

2021 ◽  
pp. 1-15
Author(s):  
Jinding Gao
Keyword(s):  
Population Dynamics ◽  
Information Exchange ◽  
Information Diffusion ◽  
Optimization Problems ◽  
Microbial Control ◽  
Function Optimization ◽  
Optimization Approach ◽  
Dynamics Model ◽  
Population Dynamics Model ◽  
Number Of Individuals

In order to solve some function optimization problems, Population Dynamics Optimization Algorithm under Microbial Control in Contaminated Environment (PDO-MCCE) is proposed by adopting a population dynamics model with microbial treatment in a polluted environment. In this algorithm, individuals are automatically divided into normal populations and mutant populations. The number of individuals in each category is automatically calculated and adjusted according to the population dynamics model, it solves the problem of artificially determining the number of individuals. There are 7 operators in the algorithm, they realize the information exchange between individuals the information exchange within and between populations, the information diffusion of strong individuals and the transmission of environmental information are realized to individuals, the number of individuals are increased or decreased to ensure that the algorithm has global convergence. The periodic increase of the number of individuals in the mutant population can greatly increase the probability of the search jumping out of the local optimal solution trap. In the iterative calculation, the algorithm only deals with 3/500∼1/10 of the number of individual features at a time, the time complexity is reduced greatly. In order to assess the scalability, efficiency and robustness of the proposed algorithm, the experiments have been carried out on realistic, synthetic and random benchmarks with different dimensions. The test case shows that the PDO-MCCE algorithm has better performance and is suitable for solving some optimization problems with higher dimensions.

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