Model-based predictive control is a modern industrial control method, along with classical and well-known methods such as PID, and its application is increasing everyday. It is introduced as an appropriate control method for situations where a relative model of a controlled system is known. The main idea behind this approach is based on predicting future behavior in the production of optimal control signals in biological systems. This method has the main stages, including modeling, predicting future behavior and finding the optimal control signal with regard to the prediction done. The main reasons to pay attention to this advanced method, along with traditional methods such as PID, are its ability to deal with MIMO systems, compensate the uncertainties such as disturbances, dealing with constraints, controlling non-minimum phase, and delayed systems. Although the current application of this method is in the slow and moderate dynamic systems, its use in fast systems is the research topic of the day. This course contains information about different approaches and methods in model-based predictive control.
Course Goals and Objectives
The main purpose of this course is to describe model-based predictive-control algorithms and their applications in real examples.
Course Topics
Introduction
Overview of Classical Control
Introduction of model-based methods
Principles of predictive control
Linear predictive control
Model Algorithmic control method
Dynamic Matrix control Method
Generalized Predictive Method
Linear, time-varying, and nonlinear predictive control
An overview of the optimization problem
Linear programming
Quadratic Programming
Nonlinear programming
Direct and indirect methods
Linear controller for nonlinear systems
Predictive control
Nonlinear controller for nonlinear systems
Predictive control and robustness
Predictive control and stability
Neural predictor control
Online Method
Predictive control applications
in human motor control
in blood pressure control
in path planning and navigation
The course aims to:
Familiarize with the role of model in controlling strategies
Explain advantages of predictive control
Explain disadvantages of predictive control
Familiarize students with predictive control algorithms
Reading Resources
Camacho, E. F., & Alba, C. B. (2013). Model predictive control. Springer Science & Business Media.
Maciejowski, J. M. (2002). Predictive control: with constraints. Pearson education.
Sâanchez, J. M. M., & Rodellar, J. (1995). Adaptive Predictive Control: From the concepts to plant optimization. Prentice Hall PTR.
Allgöwer, F., & Zheng, A. (Eds.). (2012). Nonlinear model predictive control (Vol. 26). Birkhäuser.
Evaluation
Midterm Exam, Final Exam, Seminar, Final Project, Exercises
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