Identification and Control Using Volterra Models

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Because they repre­ sent a highly structured extension ofthe class oflinear finite impulse response (FIR) models on which industrially popular linear MPC implementations are based, this book is devoted to the class of discrete-time Volterra models and a fewother, closelyrelated, nonlinear model classes. Much has been written about the general difficulty of developing the models required for model-based control of processes whose dynamics exhibit signif­ icant nonlinearity (for further discussion and references, see Chapter 1). In fact, the development ofthese models stands as a significant practical imped­ iment to widespread industrial application oftechniques like nonlinear model predictive control (NMPC), whoselinear…

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Because they repre­ sent a highly structured extension ofthe class oflinear finite impulse response (FIR) models on which industrially popular linear MPC implementations are based, this book is devoted to the class of discrete-time Volterra models and a fewother, closelyrelated, nonlinear model classes.

Much has been written about the general difficulty of developing the models required for model-based control of processes whose dynamics exhibit signif­ icant nonlinearity (for further discussion and references, see Chapter 1). In fact, the development ofthese models stands as a significant practical imped­ iment to widespread industrial application oftechniques like nonlinear model predictive control (NMPC), whoselinear counterpart has profoundly changed industrial practice. One ofthe reasons for this difficulty lies in the enormous variety of \

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