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Related Experiment Video

Updated: Jul 12, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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Published on: October 14, 2017

Extended Kalman Filtering-Based Nonlinear Model Predictive Control for Underactuated Systems With Multiple

Meng Zhai, Tong Yang, Qingxiang Wu

    IEEE Transactions on Cybernetics
    |March 3, 2025
    PubMed
    Summary

    This study introduces a new control method for underactuated systems, enhancing both performance and safety by addressing constraints and obstacle avoidance. The approach integrates extended Kalman filtering with nonlinear model predictive control for robust operation.

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    Area of Science:

    • Robotics and Control Systems
    • Applied Mathematics

    Background:

    • Underactuated systems have fewer control inputs than degrees of freedom, complicating control.
    • Existing methods often fall short in optimizing transient performance and ensuring safety constraints.
    • Sensor noise and obstacle avoidance are significant challenges in practical applications.

    Purpose of the Study:

    • To develop an advanced control method for underactuated systems that addresses limitations in steady-state and transient performance.
    • To ensure accurate positioning while managing multiple system and safety constraints, including obstacle avoidance.
    • To mitigate the effects of sensor noise on control performance.

    Main Methods:

    • An extended Kalman filtering-based nonlinear model predictive control (EKF-NMPC) strategy is proposed.
    • An artificial potential field is incorporated into the cost function for effective obstacle avoidance.
    • Dynamic weight coefficient assignment and joint application with EKF handle sensor noise.

    Main Results:

    • The EKF-NMPC method successfully ensures accurate positioning, multiple constraints, and obstacle avoidance simultaneously.
    • Efficient collision avoidance is achieved through the artificial potential field and dynamic weighting.
    • The method demonstrates robust performance on complex underactuated systems like overhead and tower cranes, even with sensor noise.

    Conclusions:

    • The developed control method is the first to simultaneously address full-state constraints, composite variable constraints, control input constraints, and obstacle avoidance in underactuated systems.
    • The proposed approach offers a significant advancement in controlling complex underactuated systems, enhancing safety and performance.
    • Validation on overhead and tower cranes confirms the method's practical applicability and effectiveness.