NPTEL : NOC:Model Predictive Control: Theory and Applications (Multi-Disciplinary)

Co-ordinators : Dr. Niket S.Kaisare


Lecture 1 - Course Background: Model Predictive Control

Lecture 2 - Course Outline

Lecture 3 - Additional MATLAB Video - Array Operations

Lecture 4 - Additional MATLAB Video - Array Operations

Lecture 5 - Recap: Linear Algebra

Lecture 6 - Recap: Differential and Difference Equations

Lecture 7 - Recap: Process Control Basics

Lecture 8 - Introduction to Model Predictive Control

Lecture 9 - MPC: Salient Features

Lecture 10 - MPC: Historical Perspective

Lecture 11 - Vectors and Matrices

Lecture 12 - Vector Spaces

Lecture 13 - Linear Operation

Lecture 14 - Null and Image Spaces

Lecture 15 - Eigenvalues and Eigenvectors

Lecture 16 - Eigenvalue Decomposition and Tutorial

Lecture 17 - Recap of Week-2

Lecture 18 - Model Classification

Lecture 19 - Discrete-Time Models Overview

Lecture 20 - Discrete-Time Models

Lecture 21 - Finite Impulse Response Models

Lecture 22 - Finite Step Response Models

Lecture 23 - Recap and Plan for Week-4

Lecture 24 - State Space and Step Response Models

Lecture 25 - Nonlinear Models and Model Linearization

Lecture 26 - Model Types and Model Conversion

Lecture 27 - Model Conversion - 2

Lecture 28 - Model Conversion: TF to SS

Lecture 29 - How to handle MIMO systems

Lecture 30 - Discretization of State-Space Models

Lecture 31 - Introduction to Dynamic Matrix Control (DMC)

Lecture 32 - The DMC Algorithm: Future Predictions

Lecture 33 - The DMC Algorithm: Objective and Constraints

Lecture 34 - The DMC Algorithm: Optimization

Lecture 35 - Coding for DMC Algorithm: Setup

Lecture 36 - Coding for DMC Algorithm: Populate Matrices

Lecture 37 - Recap of DMC Algorithm

Lecture 38 - Extensions of DMC Algorithm

Lecture 39 - LTI Models and Coordinate Transform

Lecture 40 - LTI Models: Stability

Lecture 41 - LTI Models: Controllability

Lecture 42 - LTI Models: Conditions for controllability

Lecture 43 - Tutorial by Arvind (Recap of Controllability)

Lecture 44 - LTI Models: Observability

Lecture 45 - Linear Control: Introduction

Lecture 46 - Pole Placement Controller

Lecture 47 - Linear Quadratic Regulator: Batch Solution

Lecture 48 - LQR: Dynamic Programming Solution

Lecture 49 - State Estimation: Introduction

Lecture 50 - Stochastic Processes and Random Variables

Lecture 51 - State Estimation: Pole Placement Observer

Lecture 52 - Kalman Filter: Terminology

Lecture 53 - Kalman Filter: Derivation

Lecture 54 - Recap of Modules 7-9

Lecture 55 - Recap and Plan for this week

Lecture 56 - Linear Quadratic Gaussian

Lecture 57 - LQG Derivation and Separation Principle

Lecture 58 - Setpoint Tracking in LQ Control

Lecture 59 - Disturbance Rejection in LQ Control

Lecture 60 - Disturbance Modeling for Estimation

Lecture 61 - Estimation with Disturbance Modeling

Lecture 62 - Recap and Plan for this week

Lecture 63 - State-Space MPC: Deterministic case

Lecture 64 - Extension to Measured Disturbances

Lecture 65 - Offset-Free State Space MPC

Lecture 66 - Comparison of State-Space MPC with DMC

Lecture 67 - State-Space MPC: Disturbance Modeling

Lecture 68 - Disturbance Modeling: Background and Setup

Lecture 69 - Stochastic Output-Feedback State-Space MPC

Lecture 70 - Bonus Video: Disturbance Modeling for State Space MPC

Lecture 71 - Self-Guided Tutorial of MPC Toolbox

Lecture 72 - Help Session: Using MPC Toolbox

Lecture 73 - Recap of LQ Control and Linear MPC

Lecture 74 - Linear MPC - Key Features and Results

Lecture 75 - Practical Issues: Inferential Control

Lecture 76 - Practical Issues: Measurement Delay

Lecture 77 - Other Practical Issues

Lecture 78 - Some Classical Examples of MPC