A neural network approach to control performance assessment. (English) Zbl 1157.93525
Summary: The purpose of this paper is to present a neural network approach to control performance assessment.
The performance index under study is based on the minimum variance control benchmark, a Radial Basis Function Network (RBFN) is used as the pre-whitening filter to estimate the white noise sequence, and a stable filtering and correlation analysis method is adopted to calculate the performance index by estimating innovations sequence using the RBFN pre-whitening filter. The new approach is compared with the auto-regressive moving average model and the Laguerre model methods, for both linear and nonlinear cases.
Simulation results show that the RBFN approach works satisfactorily for both linear and nonlinear examples. In particular, the proposed scheme shows merits in assessing controller performance for nonlinear systems and surpasses the Laguerre model method in parameter selection.
A RBFN approach is proposed for control performance assessment. This new approach, in comparison with some well-known methods, provides satisfactory performance and potentials for both linear and nonlinear cases.
The performance index under study is based on the minimum variance control benchmark, a Radial Basis Function Network (RBFN) is used as the pre-whitening filter to estimate the white noise sequence, and a stable filtering and correlation analysis method is adopted to calculate the performance index by estimating innovations sequence using the RBFN pre-whitening filter. The new approach is compared with the auto-regressive moving average model and the Laguerre model methods, for both linear and nonlinear cases.
Simulation results show that the RBFN approach works satisfactorily for both linear and nonlinear examples. In particular, the proposed scheme shows merits in assessing controller performance for nonlinear systems and surpasses the Laguerre model method in parameter selection.
A RBFN approach is proposed for control performance assessment. This new approach, in comparison with some well-known methods, provides satisfactory performance and potentials for both linear and nonlinear cases.
MSC:
| 93E10 | Estimation and detection in stochastic control theory |
| 93E11 | Filtering in stochastic control theory |
| 92B20 | Neural networks for/in biological studies, artificial life and related topics |
| 93C10 | Nonlinear systems in control theory |
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