Model Predictive Control for Hammerstein Systems with Unknown Input Nonlinearities

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• 作者：Haokun Wang ; Jun Zhao ; Zuhua Xu ; Zhijiang Shao
• 刊名：Industrial & Engineering Chemistry Research
• 出版年：2014
• 出版时间：May 7, 2014
• 年：2014
• 卷：53
• 期：18
• 页码：7714-7722
• 全文大小：529K
• 年卷期：v.53,no.18(May 7, 2014)
• ISSN：1520-5045

文摘

In this study, a model predictive control scheme for Hammerstein systems with unknown nonlinearities is presented. The Hammerstein model is transformed into a linear model with one known input and another unknown input, and then a minimum-variance unbiased (MVU) filter is introduced to estimate both the state and the unknown input. This enables the use of linear techniques for controlling these systems. The nonlinear dynamics are treated as unknown input and can be estimated online by the MVU filter. The proposed approach is therefore applicable for handling model mismatch and time-varying operation conditions. The proposed approach can achieve offset-free control in the presence of unknown nonlinearities and/or asymptotically constant unmeasured disturbances. Simulation results demonstrate the potential of the proposed approach for application to the control of linear systems with unknown input nonlinearities.