Control of Rotary Pendulum - Parameter Estimation Using Maximum-Likelihood - Part 5

Learn how to estimate pendulum parameters through maximum-likelihood estimation in this 15-minute technical video from JuliaHub. Explore the process of implementing control design for a rotary pendulum using Julia, focusing on unscented Kalman filtering and gradient-based optimization with automatic differentiation. Progress through key topics including data loading, disturbance analysis, state estimation techniques, simulation comparisons, and both parameter and covariance estimation using maximum likelihood methods. Part of a comprehensive series covering system interfacing, identification, stabilization control, energy-based swingup, sliding-mode control, and linear MPC arm-position control, this video leverages open-source tools like ControlSystems.jl, RobustAndOptimalControl.jl, and ControlSystemIdentification.jl to demonstrate advanced control system concepts.