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Chemical Engineering  Advanced Process Control
Title: Advanced Process Control
Department: Chemical Engineering
Author: Prof. Sachin C. Patwardhan
University: IIT Bombay
Type: WebLink
Abstract: This course on Advanced Process Control has been designed to introduce concepts of multivariable state feedback controller synthesis using discrete time state space models. Development of control relevant dynamic models is viewed as integral part of the process of controller synthesis. Thus, the course begins with development of continuous time and discrete time linear perturbation models (state space and transfer functions) starting from mechanistic models commonly used in engineering. However, in practice, a mechanistic dynamic model may not be available for a system. In such a situation, control relevant discrete dynamic black'box models can be developed using perturbation test data. Development of output error, ARX and ARMAX models from time series data and constructing state realizations of the identified models is dealt next.
Before moving to controller synthesis, concepts of unforced and BIBO stability are introduced. In particular, stability analysis using Lyapunov functions is briefly discussed. While aim of this course is to introduce multivariable controller synthesis, multi'loop PID controllers still forms the backbone of industrial process control. Thus, tools of interaction analysis and their application to multi'loop control structure design are introduced.
Synthesis of state feedback controller is dealt in two parts. To begin with, state estimation using discrete time state space models is introduced with particular emphasis on Kalman filtering. Development of linear quadratic optimal control (LQOC) and, its popular industrial variant, model predictive control (MPC) are introduced next. In particular, emphasis is given on methods to achieve offset free control in the face model plant mismatch and unmeasured disturbances.
At the end of this course, a student is expected to have sufficient background to understand complexities associated with an industrial MPC scheme or undertake further research and development in this area.
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