Model Predictive Control: Theory and Applications
Indian Institute of Technology Madras and NPTEL via Swayam
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Overview
The objectives of this course include
• Provide historical insight into MPC and its role in industry and research
• To develop linear state estimation and linear quadratic control theories
• To introduce the concept of receding horizon in MPC and its practical implementation
• To discuss tools for model building for MPC• To introduce tools for parameter identification
• To provide hands-on learning using practically relevant examples
• To discuss challenges and opportunities in research as well as industrial applications
INTENDED AUDIENCE :Post-Graduate students; final year UG; industry / research professionalsPREREQUISITES : UG Math (covering linear algebra) and Any of the following courses: Process Control; Control Engineering / Systems; Digital Control INDUSTRIES SUPPORT :Automation companies, such as: ABB, Honeywell, Yokogawa, Aspen Tech, Siemens, Emerson, Rockwell, Schnieder and GE. Chemical Process Companies, such as: Shell, IOCL, HPCL, BPCL, Reliance, ONGC, Exxon Mobil, Praxair, etc.
Syllabus
COURSE LAYOUT
Week 0:a. Introduction to Model Predictive Controlb. Recap of Linear AlgebraWeek 1:Models for MPC: Step-Response Models
Finite impulse and step response models; Model prediction; Parameter estimationWeek 2:Models for MPC: Linear Time Invariant (LTI) models
State-space models; Transfer function models; Model transformationWeek 3:Model analysis and Disturbance Modeling
Model stability; Observability and controllabilityRepresenting uncertainty; White, colored and integrating noiseWeek 4:Dynamic Matrix Control
Step-response based MPC
Week 5:Linear State Estimation
State observer; Pole placement; StabilityWeek 6:Optimal Linear State Estimation
Kalman Filter; Stochastic filtering theoryWeek 7:Linear Control Systems
Linear control; pole placement; stabilityWeek 8:Unconstrained linear quadratic control
LQ control theory
Week 9:Constrained LQ control
Constrained LQ control theoryWeek 10:State-Space MPC
State-space MPC; deterministic formulation; state feedback controlWeek 11:State-Space Output-Feedback MPC
Separation principle; Implementation of output feedback MPCWeek 12:Practical Implementation
Nonlinear systems; Multi-rate system; Inferential control
Taught by
Prof. Niket Kaisare
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