Dynamic Games: Theory and Applications

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Related lectures (32)

State Representation and Controller Design

Covers state representation, controller design, LQR controllers, and optimal control criteria.

Separating Hyperplanes: Theory and Applications

Explores separating hyperplanes for two sets X and Y, discussing conditions for separation and implications of convexity.

Feedback Control Systems: PID Regulator Synthesis

Explores closed-loop control systems, PID regulator design, and performance analysis.

Bidding Strategies in Power Systems

Explores strategic bidding, market power, and Game Theory in power systems, emphasizing optimal bidding strategies and market imperfections.

Market Dynamics: Social Welfare and Economic Models

Examines the interplay between market dynamics and social welfare, highlighting the roles of willingness to pay and willingness to accept in economic models.

Convex Games: Applications and Equilibria

Covers convex games, their equilibria, and applications in various fields such as traffic and electricity markets.

Linear Quadratic Optimal Control: Analysis and Solution

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

Model Reference Method: PID Regulator Design

Explores the Model Reference Method for designing PID regulators and cascade controllers for complex systems.

Value Iteration Acceleration: PID and Operator Splitting

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

Multivariable Control: Eigenvalue Assignment and Disturbance Estimation

Discusses closed-loop eigenvalue selection and disturbance impact in multivariable control systems.

Performances of closed-loop systems

Covers the performance of closed-loop systems, set-point tracking, disturbance rejection, and perfect control.

Markov Decision Processes: Foundations of Reinforcement Learning

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

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