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Related Experiment Video

Updated: Mar 26, 2026

Finite Element Modeling for the Simulation of the Quasi-Static Compression of Corrugated Tapered Tubes
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Iterated non-linear model predictive control based on tubes and contractive constraints.

M Murillo1, G Sánchez1, L Giovanini1

  • 1Research Institute for Signals, Systems and Computational Intelligence (sinc(i)), National Scientific and Technical Research Council (CONICET), Ciudad Universitaria UNL, 4° piso FICH, (S3000) Santa Fe, Argentina.

ISA Transactions
|February 7, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a novel predictive control algorithm for non-linear systems. It enhances quadcopter control by using successive linearizations and iterative adjustments for improved accuracy and stability.

Keywords:
Contractive constraintIterative processNon-linear model predictive controlRobust model predictive control

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Area of Science:

  • Control Systems Engineering
  • Robotics
  • Non-linear Dynamics

Background:

  • Controlling complex non-linear systems presents significant challenges.
  • Existing methods often struggle with trajectory tracking and robustness.
  • Accurate modeling and control are crucial for unmanned aerial vehicles (UAVs).

Purpose of the Study:

  • To develop an effective predictive control algorithm for non-linear systems.
  • To enhance the robustness and accuracy of control for UAVs.
  • To analyze the convergence and stability of the proposed control method.

Main Methods:

  • Successive linearization of non-linear dynamics around a reference trajectory.
  • Transformation of non-convex optimization problems into a sequence of locally convex problems.
  • Inclusion of a convex contractive constraint for robustness and an inner iteration loop for accuracy.

Main Results:

  • The algorithm successfully transforms complex optimization problems.
  • A methodology for outer bounding-tube state trajectories is presented.
  • Simulation results demonstrate effective control of a quadcopter UAV.

Conclusions:

  • The proposed predictive control algorithm is effective for non-linear systems.
  • The method ensures robustness and accuracy through iterative linearization and constraints.
  • The algorithm shows promise for real-world applications, particularly in UAV control.