Lectures related to Optimal Control Theory: Basics

Markov Decision Processes: Foundations of Reinforcement Learning

Covers Markov Decision Processes, their structure, and their role in reinforcement learning.

Linear Quadratic Optimal Control: Analysis and Solution

Explores Linear Quadratic optimal control, analyzing cost, and presenting the solution to the FH-LQ problem.

Linear Quadratic Optimal Control: Theory and Applications

Explores Linear Quadratic optimal control theory, covering FH-LQ and IH-LQ problems and the importance of observability in control systems.

Advanced Machine Learning: Discrete Reinforcement Learning

Introduces the basics of Reinforcement Learning, covering discrete states, actions, policies, value functions, MDPs, and optimal policies.

Differential Calculus: Applications and Reminders

Covers differential calculus applications and reminders, emphasizing the importance of differentiability in mathematical analysis.

Value Iteration Acceleration: PID and Operator Splitting

Explores accelerating the Value Iteration algorithm using control theory and matrix splitting techniques to achieve faster convergence.

Infinite-Horizon LQ Control: Solution & Example

Explores Infinite-Horizon Linear Quadratic (LQ) optimal control, emphasizing solution methods and practical examples.

Multivariable Control: System Theory and Applications

Covers system theory, classic feedback control, and applications in green building and natural gas refrigeration plants.

Networked Control Systems: Fundamentals and Stability Analysis

Covers the fundamentals and stability analysis of Networked Control Systems, including software installation, dynamical systems, equilibrium states, and stability testing.

Linear Quadratic (LQ) Optimal Control: Proof of Theorem

Covers the proof of the recursive formula for the optimal gains in LQ control over a finite horizon.

Control Theory in Protein Aggregation: Insights and Applications

Explores the application of control theory to manage protein aggregation processes, focusing on amyloid fibers and their implications in various diseases.

Optimal Control Theory: OCPs Log in to Mediaspace to watch this video

Covers Optimal Control Theory, focusing on Optimal Control Problems (OCPs) and the calculus of variations.

Optimal Control: OCPs Log in to Mediaspace to watch this video

Covers Optimal Control Problems focusing on necessary conditions, existence of optimal controls, and numerical solutions.

Calculus of Variations: Some Topics Log in to Mediaspace to watch this video

Covers fundamental topics in calculus of variations, including minimizers and the Euler-Lagrange equation.

Nonlinear Model Predictive Control Log in to Mediaspace to watch this video

Explores Nonlinear Model Predictive Control, covering stability, optimality, pitfalls, and examples.

Optimal Control: NMPC Log in to Mediaspace to watch this video

Covers Nonlinear Model Predictive Control (NMPC) principles, including setpoint stabilization and Pontryagin's Maximum Principle.

Introduction to Reinforcement Learning: Key Concepts and Applications Log in to Mediaspace to watch this video

Introduces reinforcement learning, covering its definitions, applications, and theoretical foundations, while outlining the course structure and objectives.

Dynamic Programming: Optimal Control Log in to Mediaspace to watch this video

Explores Dynamic Programming for optimal control, focusing on stability, stationary policy, and recursive solutions.

Calculus of Variations: Ground States in Quantum Mechanics Log in to Mediaspace to watch this video

Covers the Calculus of Variations to find ground states in quantum mechanics by minimizing energy, discussing the Euler Lagrange equation and the Fundamental Theorem of Young Measure Theory.

Controlled Stochastic Processes Log in to Mediaspace to watch this video

Explores controlled stochastic processes, focusing on analysis, behavior, and optimization, using dynamic programming to solve real-world problems.