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Learn system identification techniques using the Julia programming language through this comprehensive video lecture series spanning over 6 hours. Master fundamental concepts including experimental design, input signal selection, model estimation, and result validation. Explore linear ARX models, prediction versus simulation approaches, and the prediction-error method while understanding prefiltering techniques. Dive into advanced topics such as subspace-based identification, parameter calibration for nonlinear ordinary differential equations, and time-varying parameter estimation. Gain practical experience with adaptive estimation and control, fault detection systems, and sensor fusion methodologies. Apply your knowledge through hands-on examples including rotary pendulum control scenarios covering friction estimation, chirp system identification, and parameter estimation. Conclude with specialized applications in model-predictive control using JuliaSim, providing you with a complete toolkit for system identification in Julia.
