Explores offset-free tracking in multivariable control, covering necessary conditions and feedforward compensation to reject disturbances and achieve constant setpoints.
Explores the significance of modeling and predicting uncertain environments for ensuring safe and high-performance autonomy in modern autonomous systems.
Covers industrial automation hierarchy, control systems, and plant categories, emphasizing the importance and applications of automation in various industries.
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.
Covers model predictive control for multi-region Macroscopic Fundamental Diagrams in traffic flow modeling and its application in handling non-linear control problems.
