Introduction
Model predictive control (MPC) has become the most popular advanced control method in use today. Its main attractive features are (i) optimization of a model forecast over the available actuators (ii) estimation of the state of the system and disturbances from the process measurements, (iii) accounting for the process and actuator constraints, and (iv) accounting for full multivariable interactions. After its introduction in the process industries in the 1970s, MPC has today become a pervasive control technology in many industries, and is now being increasingly deployed for optimization of high-level functions such as minimizing energy consumption and maximizing product quality.
This short course is intended to introduce graduate students and practitioners to the theory and design of MPC systems.
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Submission Topics
The two days of lectures will cover the following topics.
- Introduction, dynamic modeling, predictive control versus classical PID control.
- Model predictive control: regulation problem, linear quadratic regulator, constraints, dynamic programming, infinite horizon, LQR, constrained regulation.
- State estimation: least-squares estimator, Kalman filter, observability and convergence.
- Putting regulation and estimation together, industrial practice, disturbance models, and offset.
- Nonlinear MPC. introduction, stability, Lyapunov function theory, disturbances and robust stability, nominal stability, suboptimal MPC, inherent robustness of optimal and suboptimal MPC, some examples.
- Other topics: distributed MPC, economic MPC, and hybrid MPC.
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Important Date
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Conference Date
Jun 29
2015
toJun 30
2015
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Jun 30 2015
Registration deadline
Sponsored By
Contact Information
- Randal W. Beard
- be******@byu.edu
Previous Conferences
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2017-05-22 United States Seattle
Model Predictive Control Workshop
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