Fossen's Marine Systems Simulator
Linear, Extended & Unscented Kalman filter Fusion Models for 2D tracking
Implemented pole placement and linear quadratic regulator on several real-world systems.
Optimal LQR control for drone landing using MATLAB, emphasizing strategic pole placement for precise descent.
This Mathematica notebook provides development of a Forward Kinematic model, Inverse Kinematic Model, and Dynamic model using Generalized Momenta method of a Universal Omni Wheeled mobile robot. Also, a PID trajectory tracking controller was developed to track different trajectories with very small error.
A 1998 undergraduate thesis on inverted pendulum control using MATLAB and state-space methods
MATLAB and Python simulation code for state feedback control design — pole placement, LQR, integral servo, observer-based feedback, and LMI-based design. Companion code for blog.control-theory.com.
State-space modelling, controllability & observability analysis, EESA and GCCF state feedback design, and Luenberger observer synthesis for a 2-DOF inverted pendulum, implemented in MATLAB and Simulink.
The Pole's Shadow: The distance of dominant poles from the imaginary axis sets the cognitive budget of linear systems. Determines how long they can remember and integrate slow inputs before transients decay. Explores the robustness vs memory tradeoff with theory, math, simulations and visualizations.
Provide MATLAB code for state observer design and estimation, covering Luenberger observers, Kalman filters, and robust multi-rate methods.
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