Covers model predictive control for multi-region Macroscopic Fundamental Diagrams in traffic flow modeling and its application in handling non-linear control problems.
Explores offset-free tracking in multivariable control, covering necessary conditions and feedforward compensation to reject disturbances and achieve constant setpoints.
Covers the computation of cost function for multivariable control systems using the LQR framework and applying gradient descent for controller improvement.
Introduces Data-Enabled Predictive Control (DEEPC) as a method to design controllers directly from measured input/output data, reducing the cost of design and commissioning.
Explores the significance of modeling and predicting uncertain environments for ensuring safe and high-performance autonomy in modern autonomous systems.
