Model Predictive Control

• Analyze and describe MPC control structures
• Select processes that can be controlled by MPC
• Apply convex optimization to estimation, control and system identification
• Identify a linear model from data
• Design and tune a linear model based estimator and predictor
• Design and tune a constrained regulator
• Synthesize and implement model predictive control systems
• Test a linear model predictive control system by simulation
• Implement Nonlinear Model Predictive Controllers
• Use SQP optimization methods for NMPC
• Compute solutions and sensitivities in nonlinear ODE models
• Apply MPC to industrial, biomedical, and financial problems

Modeling of dynamic systems
Convex optimization algorithms
System identification
Design and tune linear MPC
Implement linear MPC
Simulation and test of linear MPC
Nonlinear Model Predictive Control (NMPC)
SQP optimization algorithms for NMPC
Numerical compution of the solution and sensitivities of ODE systems
Industrial, biomedical and financial problems.
Implementation using MATLAB, Python, or Julia

02612, A course in constrained optimization

Lectures and exercies