Parameter identification of systems with preload nonlinearities based on the finite impulse response model and negative gradient search

Chen, Jing; Lu, Xianling; Ding, Rui

doi:10.1016/j.amc.2012.08.085

Cited by 14 publications

(3 citation statements)

References 76 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To solve the above problems, the [mite impulse response (FIR) model is selected as the whole grinding system model in this paper [4], and the unknown parameters of the model are obtained by the least square method. Through the improvement of the FIR model, a new parameter is observed which can reflect the working condition of the ball mill, and the working condition of the ball mill can be gained directly according to the value of this parameter [5].…”

Section: Machinementioning

confidence: 99%

Judgement of ball mill working condition in combined grinding system

Wanjie

Qingjin

et al. 2013

Proceedings of 2013 2nd International Conference on Measurement, Information and Control

View full text Add to dashboard Buy / Rent full text

In the cement grinding system, the quantity of raw material in the ball mill has an important effect on the cement production. Based on the quantity, the working conditions of the ball mill are divided into three states, the full state, the normal state, the empty state. Normally, it's hard to predict the working station of the ball mill and the precision is low. This paper, least square method and its improved algorithm is used as a new method to judge the working condition of ball mill. A parameter a is added to the finite impulse response model. Through the value of the ball mill working condition can be judged so the operator can change the parameters of the equipment in time and an on-line software is programmed by VC++. As a result, both the quality of the cement and the stability of the equipment are improved.

show abstract

Section: Machinementioning

confidence: 99%

Judgement of ball mill working condition in combined grinding system

Wanjie

Qingjin

et al. 2013

Proceedings of 2013 2nd International Conference on Measurement, Information and Control

View full text Add to dashboard Buy / Rent full text

show abstract

“…The identification and control of nonlinear system have been widely studied in recent years [1,2,3,4]. Before the design of a controller, it is necessary to achieve system identification.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear system identification and control using state transition algorithm

Zhou

Yang

Gui

2014

Applied Mathematics and Computation

View full text Add to dashboard Buy / Rent full text

By transforming identification and control for nonlinear system into optimization problems, a novel optimization method named state transition algorithm (STA) is introduced to solve the problems. In the proposed STA, a solution to a optimization problem is considered as a state, and the updating of a solution equates to a state transition, which makes it easy to understand and convenient to implement. First, the STA is applied to identify the optimal parameters of the estimated system with previously known structure. With the accurate estimated model, an off-line PID controller is then designed optimally by using the STA as well. Experimental results have demonstrated the validity of the methodology, and comparisons to STA with other optimization algorithms have testified that STA is a promising alternative method for system identification and control due to its stronger search ability, faster convergence rate and more stable performance.

show abstract

“…The key term separation principle is often utilized to simplify the hard nonlinearities. The basic idea of the key term separation principle is first to apply some mathematical functions to turn the model of the hard nonlinear systems into an identification model and then use the linear system identification methods to estimate the unknown parameters [14,15]. For example, Chen et al defined switching function to simplify a saturation and dead-zone nonlinearity and proposed an identification method for the nonlinear system [16].…”

Section: Introductionmentioning

confidence: 99%