Abstract
Generally, the controller design should be performed to narrow the shape of the probability density function of the tracking error. A small information entropy value corresponds to a narrow distribution function, which means that the uncertainty of the related random variable is small. In this paper, information entropy is introduced in the field of control performance assessment (CPA). For the unknown time delay case, the minimum information entropy (MIE) benchmark is presented, and a MIE-based performance index is defined. For the known time delay case, a tight upper bound of MIE is derived and adopted as a performance benchmark to assess the stochastic control performance. Based on these, the control performance assessment procedures are developed for both the steady and the transient processes. Simulation tests and an industrial case study of a main steam pressure system of a 1,000MW power unit are utilized to verify the effectiveness of the proposed procedures.
Highlights
The performance of the control system has a direct impact on the security and the economy of an industrial process
The purpose of this paper is to propose an information-entropy-based control performance assessment (CPA) method with its advantage of generalized random performance description, and focuses on the selection of the MinimumInformation-Entropy (MIE) benchmark and the design of minimum information entropy index
Compared to the Harris index, the advantages of the new CPA index are as follows: (1) If the upper bound of the MIE is ln( 2 exp(1) mv ), which is selected as the performance benchmark to assess the control performance, the new CPA index will have the similar computational complexity and assessment result with the Harris index
Summary
The performance of the control system has a direct impact on the security and the economy of an industrial process. Many other indexes were proposed based on variance, such as the Relative Variance Index [8], the General Minimum Variance [9] For the latter branch Desborough and Harris [10] studied the data-based monitoring for SISO systems. The controller design should be performed so that the shape of the probability density function (PDF) of the tracking error is as narrow as possible This is because a narrow distribution function generally indicates that the uncertainty of the related random variable is small, which corresponds to a small entropy value [14]. The purpose of this paper is to propose an information-entropy-based CPA method with its advantage of generalized random performance description, and focuses on the selection of the MinimumInformation-Entropy (MIE) benchmark and the design of minimum information entropy index.
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