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Introduction and Motivation.
Linearization of Mechanistic Models.
Linearization of Mechanistic Models (Contd.).
Introduction to z-transforms and Development of Grey-box models.
Introduction to Stability Analysis and Development of Output Error Models.
Introduction to Stochastic Processes.
Introduction to Stochastic Processes (Contd.).
Development of ARX models.
Statistical Properties of ARX models and Development of ARMAX models.
Development of ARMAX models (contd.) and Issues in Model Development.
Model Structure Selection and Issues in Model Development (contd.).
Issues in Model Development (contd.) and State Realizations of Transfer Function Models.
Stability Analysis of Discrete Time Systems.
Lyapunov Functions and Interaction Analysis and Multi-loop Control.
Interaction Analysis and Multi-loop Control (contd.).
Multivariable Decoupling Control and Soft Sensing and State Estimation.
Development of Luenberger Observer.
Development of Luenberger Observer (contd.) and Introduction to Kalman Filtering.
Kalman Filtering.
Kalman Filtering (contd.).
Kalman Filtering (contd.).
Pole Placement State Feedback Control Design and Introduction to Linear Quadratic Gaussian Control.
Linear Quadratic Gaussian (LQG) Regulator Design.
Linear Quadratic Gaussian (LQG) Controller Design.
Model Predictive Control (MPC).Model Predictive Control (contd.).
Instructor: Prof. Sachin Patwardhan, Department of Chemical Engineering, IIT Bombay.
This course 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 an integral part of the process of controller synthesis. Thus, the course begins with the 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.
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