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Advanced Process Control

Advanced Process Control. 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 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. (from nptel.ac.in)

Lecture 07 - Introduction to Stochastic Processes (cont.)

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Introduction

Lecture 01 - Introduction and Motivation

Development of Control Relevant Linear Perturbation Models

Lecture 02 - Linearization of Mechanistic Models

Lecture 03 - Linearization of Mechanistic Models (cont.)

Lecture 04 - Introduction to z-Transforms and Development of Grey-box Models

Development of Linear Black-box Dynamic Models

Lecture 05 - Introduction to Stability Analysis and Development of Output Error Models

Lecture 06 - Introduction to Stochastic Processes

Lecture 07 - Introduction to Stochastic Processes (cont.)

Lecture 08 - Development of ARX Models

Lecture 09 - Statistical Properties of ARX Models and Development of ARMAX Models

Lecture 10 - Development of ARMAX Models (cont.), Issues in Model Development

Lecture 11 - Model Structure Selection and Issues in Model Development

Lecture 12 - Issues in Model Development and State Realizations of Transfer Function Models

Stability Analysis, Interaction Analysis and Multi-loop Control

Lecture 13 - Stability Analysis of Discrete Time Systems

Lecture 14 - Lyapunov Functions and Interaction Analysis and Multi-loop Control

Lecture 15 - Interaction Analysis and Multi-loop Control

Lecture 16 - Multivariable Decoupling Control and Soft Sensing and State Estimation

State Estimation and Kalman Filtering

Lecture 17 - Development of Luenberger Observer

Lecture 18 - Development of Luenberger Observer (cont.), Introduction to Kalman Filtering

Lecture 19 - Kalman Filtering

Lecture 20 - Kalman Filtering (cont.)

Lecture 21 - Kalman Filtering (cont.)

Linear Quadratic Optimal Control and Model Predictive Control

Lecture 22 - Pole Placement State Feedback Control Design and Introduction to Linear Quadratic Gaussian Control

Lecture 23 - Linear Quadratic Gaussian (LQG) Regulator Design

Lecture 24 - Linear Quadratic Gaussian (LQG) Controller Design

Lecture 25 - Model Predictive Control (MPC)

Lecture 26 - Model Predictive Control (cont.)