Learn · Model Predictive Control: Theory and Applications
Lecture 9 of 84Week 1
MPC: Salient Features
Prof. Niket Kaisare · IIT Madras
Credits and licence
- From
- Model Predictive Control: Theory and Applications, IIT Madras (at NPTEL)
- Licence
- CC BY-NC-SA, as NPTEL states it. Non-commercial use only.
- Attribution
- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
- Who can watch
- Teens profiles and older. How courses are placed
All the lectures
- Course Background: Model Predictive Control
- Course Outline
- Additional MATLAB Video - Array Operations
- Additional MATLAB Video - Array Operations
- Recap: Linear Algebra
- Recap: Differential and Difference Equations
- Recap: Process Control Basics
- Introduction to Model Predictive Control
- MPC: Salient Features
- MPC: Historical Perspective
- Vectors & Matrices
- Vector Spaces
- Linear Operation
- Null and Image Spaces
- Eigenvalues and Eigenvectors
- Eigenvalue Decomposition and Tutorial
- Recap of Week-2
- Model Classification
- Discrete-Time Models Overview
- Discrete-Time Models
- Finite Impulse Response Models
- Finite Step Response Models
- Recap and Plan for Week-4
- State Space and Step Response Models
- Nonlinear Models and Model Linearization
- Model Types and Model Conversion
- Model Conversion - 2
- Model Conversion: TF to SS
- How to handle MIMO systems
- Discretization of State-Space Models
- Introduction to Dynamic Matrix Control (DMC)
- The DMC Algorithm: Future Predictions
- The DMC Algorithm: Objective & Constraints
- The DMC Algorithm: Optimization
- Coding for DMC Algorithm: Setup
- Coding for DMC Algorithm: Populate Matrices
- Recap of DMC Algorithm
- Extensions of DMC Algorithm
- LTI Models and Coordinate Transform
- LTI Models: Stability
- LTI Models: Controllability
- LTI Models: Conditions for controllability
- Tutorial by Arvind (Recap of Controllability)
- LTI Models: Observability
- Linear Control: Introduction
- Pole Placement Controller
- Linear Quadratic Regulator: Batch Solution
- LQR: Dynamic Programming Solution
- State Estimation: Introduction
- Stochastic Processes and Random Variables
- State Estimation: Pole Placement Observer
- Kalman Filter: Terminology
- Kalman Filter: Derivation
- Recap of Modules 7-9
- Recap and Plan for this week
- Part-1. Linear Quadratic Gaussian
- Part-1. LQG Derivation and Separation Principle
- Part-2. Setpoint Tracking in LQ Control
- Part-2. Disturbance Rejection in LQ Control
- Part-3. Disturbance Modeling for Estimation
- Part-3: Estimation with Disturbance Modeling
- Recap and Plan for this week.
- State-Space MPC: Deterministic case
- Extension to Measured Disturbances
- Offset-Free State Space MPC
- Comparison of State-Space MPC with DMC
- State-Space MPC: Disturbance Modeling
- Disturbance Modeling: Background and Setup
- Stochastic Output-Feedback State-Space MPC
- Bonus Video: Disturbance Modeling for State Space MPC
- Self-Guided Tutorial of MPC Toolbox
- Help Session: Using MPC Toolbox
- Recap of LQ Control and Linear MPC
- Linear MPC - Key Features and Results
- Practical Issues: Inferential Control
- Practical Issues: Measurement Delay
- Other Practical Issues
- Some Classical Examples of MPC
- Live Session 28-01-2021
- Live Session 11-02-2021
- Live Session 25-02-2021
- Live Session 18-03-2021
- Live Session 01-04-2021
- Live Session 08-04-2021