Predictive Control

(Department)  Biomedical Engineering         (Division)      Bioelectric
 (Level and Major)  Master and PhD

Course Title                  Predictive control    
Number of Credits       3             Prerequisite -
Lecturer Farzad Towhidkhah

Course Description
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
  1. Camacho, E. F., & Alba, C. B. (2013). Model predictive control. Springer Science & Business Media.
  2. Maciejowski, J. M. (2002). Predictive control: with constraints. Pearson education.
  3. Sâanchez, J. M. M., & Rodellar, J. (1995). Adaptive Predictive Control: From the concepts to plant optimization. Prentice Hall PTR.
  4. Allgöwer, F., & Zheng, A. (Eds.). (2012). Nonlinear model predictive control (Vol. 26). Birkhäuser.
Midterm Exam, Final Exam, Seminar, Final Project, Exercises

View: 266 Time(s)   |   Print: 46 Time(s)   |   Email: 0 Time(s)   |   0 Comment(s)