Abstract
The performance assessment of any control system plays a key role in industrial control systems. To meet the real-time requirements of modern control systems, a quick and accurate evaluation of the performance of a system is necessary. In this paper, a performance assessment method of a non-Gaussian control system based on mixture correntropy is proposed for non-Gaussian stochastic systems. Mixture correntropy can solve the problem of minimum entropy translation invariance. When the expected output of a system is unavailable, mixture correntropy combined with the estimation of distribution algorithm (EDA) is used for system identification and noise distribution estimation so as to calculate the benchmark of entropy-based performance assessment. When the expected output of a system is available, the mixture correntropy is directly used as the index to evaluate the performance of the system. To improve the real-time aspect of the performance assessment, an improved EDA is presented to obtain the evaluation index more quickly. For both Gaussian and non-Gaussian systems, the mixture correntropy and the improved identification algorithm are used for system performance assessment, and the results are compared with the minimum entropy index and the probability density function (PDF) curve coincident area index. The comparisons verify the rationality and effectiveness of the correntropy index and the rapidity of the improved EDA algorithm.
Highlights
With the rapid development of communications, microelectronics, and computer technology, industrial control systems are constantly changing, and the level of automation is constantly increasing.Fast transmission of information, rapid sampling, and control of field devices, rapid display and operation of the host computer, and increasingly higher requirements for the real-time performance of a control system are being introduced for increasingly greater numbers of industrial control systems [1].The real-time performance of a control system is the key to the performance of the entire control system, directly affecting the control quality of the system
Gaussian and non-Gaussian systems, the mixture correntropy and the improved identification algorithm are used for system performance assessment, and the results are compared with the minimum entropy index and the probability density function (PDF) curve coincident area index
In order to satisfy the real-time demands of modern control systems, improve the accuracy and rapidity of the evaluation indicators, mixture correntropy is chosen for performance assessment, which is a more accurate and rapid evaluation index for the non-Gaussian random distribution control system, whether the output distribution of the system is known or unknown
Summary
With the rapid development of communications, microelectronics, and computer technology, industrial control systems are constantly changing, and the level of automation is constantly increasing. When the method is applied to an actual system, the amount of data is larger This complex calculation requires considerable time to obtain a system performance evaluation index, and cannot meet the real-time requirements of industrial control systems. In order to satisfy the real-time demands of modern control systems, improve the accuracy and rapidity of the evaluation indicators, mixture correntropy is chosen for performance assessment, which is a more accurate and rapid evaluation index for the non-Gaussian random distribution control system, whether the output distribution of the system is known or unknown. We focus on the case where the expected output distribution of the system is available and unavailable, and mixture correntropy is used for the performance assessment of the non-Gaussian control system. The proposed index and improved EDA are used in a numerical simulation to verify the validity and accuracy
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