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MPC and genetic algorithm for flying pendulum control

US & West ·

An approach combining model predictive control (MPC) and an online genetic algorithm is proposed to estimate the unknown pendulum length in a flying inverted pendulum control problem. Experiments on a real setup confirmed the accuracy and robustness of the method under various initial conditions and disturbances.

Why it matters

Demonstrates real-time adaptation of MPC to uncertain parameters, which is important for robust control in complex dynamic systems.

Relevant to DT products

Original headline
Balancing a Flying Inverted Pendulum with an Unknown Length Using Model Predictive Control and a Genetic Algorithm Estimator
Read the original: arXiv — Systems and Control (eess.SY)

Translation and summary are machine-generated from the source. The full article is not republished; its rights belong to the source.

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Parallel branch MPC on GPUs

A GPU-based solver for trajectory planning problems using branch model predictive control is presented. The solver is based on iterative LQR, multiple-shooting formulation, and augmented Lagrangian method for constraint handling. Numerical experiments show superiority over a CPU-based solver on large-scale problems.

DT.ForecastarXiv — Systems and Control (eess.SY) ↗