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Abstract
This paper addresses a modified auxiliary model stochastic gradient recursive parameter identification algorithm (M-AM-SGRPIA) for a class of single input single output (SISO) linear output error models with multi-threshold quantized observations. It proves the convergence of the designed algorithm. A pattern-moving-based system dynamics description method with hybrid metrics is proposed for a kind of practical single input multiple output (SIMO) or SISO nonlinear systems, and a SISO linear output error model with multi-threshold quantized observations is adopted to approximate the unknown system. The system input design is accomplished using the measurement technology of random repeatability test, and the probabilistic characteristic of the explicit metric value is employed to estimate the implicit metric value of the pattern class variable. A modified auxiliary model stochastic gradient recursive algorithm (M-AM-SGRA) is designed to identify the model parameters, and the contraction mapping principle proves its convergence. Two numerical examples are given to demonstrate the feasibility and effectiveness of the achieved identification algorithm.
Highlights
IntroductionPetroleum-chemical and steel industries, there are technologically complicated, highly energy-consuming and polluting large-scale equipments such as electrolytic tank, sintering machine, blast furnace cement rotary kiln and so on
In view of various metric methods of pattern class, the linear autoregressive model with exogenous input (ARX) or interval ARX (IARX) model was established, and the parameter identification algorithm based on least square [6], minimumvariance-based controller [5], optimal controller [10], state-feedback controller [7] and predictive controller [11] were designed
Compared with the existed research results, the main differences and contributions of this paper are summarized as follows: 1) Different from the previous system identification problem of ARX or IARX models based on pattern moving and single metric [6,7], this paper considers hybrid metrics, the model noise distribution and proves the convergence of the designed M-AM-SGRPIA
Summary
Petroleum-chemical and steel industries, there are technologically complicated, highly energy-consuming and polluting large-scale equipments such as electrolytic tank, sintering machine, blast furnace cement rotary kiln and so on. 1) The complex system mechanism beyonds the accurate description of mathematical and physical equations; 2) Working conditions and quality parameters are in large quantities, and the system moving mode is full of distributiveness, nonlinearity and parameter perturbations; 3) Some physical and chemical processes are in conformity with statistical law of moving. CMES, 2022, vol.130, no.3 the pattern recognition technology for these considered processes [2] and most researchers’ practice is to design the corresponding model and controller according to the different pattern class of the system working condition [3,4]. In view of various metric methods of pattern class, the linear autoregressive model with exogenous input (ARX) or interval ARX (IARX) model was established, and the parameter identification algorithm based on least square [6], minimumvariance-based controller [5], optimal controller [10], state-feedback controller [7] and predictive controller [11] were designed
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