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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Data-enabled predictive control for quadcopters.

Ezzat Elokda¹, Jeremy Coulson¹, Paul N Beuchat¹

¹Automatic Control Lab (IfA) ETH Zürich Zürich Switzerland.

International Journal of Robust and Nonlinear Control

|July 25, 2022

Summary

This summary is machine-generated.

Data-enabled predictive control (DeePC) effectively manages nano-quadcopter position. This optimal control method uses real-time data, avoiding complex system modeling for reliable trajectory tracking.

Keywords:

data‐driven control predictive control quadcopters

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Area of Science:

Robotics
Control Systems Engineering
Artificial Intelligence

Background:

Nano-quadcopters present complex control challenges due to nonlinear dynamics and noisy real-world environments.
Traditional control methods often require accurate system models, which are difficult to obtain for such systems.
Data-driven approaches offer a promising alternative for robust control without explicit system identification.

Purpose of the Study:

To investigate the efficacy of the data-enabled predictive control (DeePC) algorithm for real-world nano-quadcopter position control.
To demonstrate the necessity and benefits of a regularized DeePC variant for handling nonlinear dynamics and measurement noise.
To validate the reliability and performance of DeePC against established control methods.

Main Methods:

Application of a finite-horizon, optimal control DeePC algorithm utilizing input/output measurements.
Prediction of future quadcopter trajectories by linearly combining previously measured trajectories (motion primitives).
Implementation of a regularized DeePC variant to address system nonlinearities and noisy data, validated through simulations and real-world experiments.

Main Results:

The regularized DeePC algorithm successfully achieved precise position control of real-world nano-quadcopters.
Simulation insights regarding regularization effects were confirmed in physical experiments, highlighting robustness.
DeePC demonstrated reliable performance, even with continuously updated input/output measurements for each experiment.

Conclusions:

The data-enabled predictive control (DeePC) algorithm, particularly its regularized form, is a viable and effective method for nano-quadcopter position control.
DeePC offers a robust alternative to model-based control, excelling in handling nonlinear dynamics and noisy conditions.
The study confirms DeePC's reliability and successful trajectory tracking capabilities in real-world robotic applications.