Covers the fundamentals of optimal control theory, focusing on defining OCPs, existence of solutions, performance criteria, physical constraints, and the principle of optimality.
Explores the Extended Kalman Predictor algorithm and the linearized Kalman Filter for multivariable control systems, discussing the challenges and applications.
Explores the stability of Ordinary Differential Equations, focusing on solution dependence, critical data, linearization, and control of nonlinear systems.
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.
