Lectures related to Lyapunov Stability Theory: Energy Functions and Stability Analysis

Explores Linear Matrix Inequalities in networked control systems and analyzes control networks' basics and performance.

Explores stability analysis for linear time-varying and switched systems using Lyapunov theory and Linear Matrix Inequalities.

Explores the link between linear systems and optimization through elimination and LU decomposition.

Covers the review of linear discrete-time systems and Lyapunov theory, focusing on stability and equilibria.

Explores stability in linear 2D systems, covering fixed points, vector fields, and phase portraits.

Explores learning and adaptive control for robots through SEDS and LPV-DS, emphasizing stability, non-linear dynamics, and optimization.

Covers iterative methods for solving linear equations and analyzing convergence, including error control and positive definite matrices.

Explores optimal control and KKT conditions for non-linear optimization with constraints.

Covers semi-definite programming and optimization over positive semidefinite cones.

Covers the design of SISO polynomial controllers using concepts such as the Sylvester matrix and model matching.

Covers equilibria, stability analysis, and Lyapunov theory in linear systems, focusing on eigenvalues and stability properties.

Covers linear systems in continuous time, stability analysis, and mass-spring system examples.

Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.

Covers Lyapunov stability analysis, focusing on Lyapunov functions and stability criteria in robotic systems.

Explores Lyapunov stability and Chebyshev inequalities in system analysis and control design.

Covers essential linear algebra concepts for convex optimization, including vector norms, eigenvalue decomposition, and matrix properties.

Covers the concept of passivity in linear systems and the circle criterion for absolute stability.

Explores stability, reachability, and controllability in linear systems, emphasizing their significance in system analysis.

Covers the KKT conditions for optimization with constraints, essential for solving constrained optimization problems efficiently.

Introduces dynamical systems, equilibrium points, stability, vector fields, phase plots, and Lyapunov stability.