Enhancing the Efficiency and Reliability of UAV Systems: A Lyapunov-Based Stabilizing Model Predictive Control Framework
July 7 @ 10:30 am - 11:30 am
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Join the IEEE Toronto Instrumentation & Measurement – Robotics & Automation Joint Chapter for a technical talk presented by Dr. Binyan Xu from University of <a href="http://Guelph.
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Monday, July 7, 2025 @ 10:30 – 11:30 AM (EST)
Abstract: The use of Unmanned Aerial Vehicles (UAVs) has expanded significantly over recent decades, driven by their flexibility, efficiency, cost-effectiveness, and capability to operate in dangerous or inaccessible environments. With rising demands, UAV systems are increasingly expected to achieve higher levels of autonomy. Model predictive control (MPC), an advanced control methodology that leverages online optimization, provides notable advantages such as optimal performance, efficient handling of multivariable systems, and explicit constraint management, making it a promising solution for UAV control challenges. However, ensuring closed-loop performance with manageable computational demands remains challenging due to the highly nonlinear dynamics of UAVs and the computational complexity of <a href="http://MPC.
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This talk introduces a Lyapunov-based MPC framework designed specifically to address these challenges, offering stabilized and computationally efficient MPC strategies tailored for UAV applications. Applications of this framework, including trajectory tracking and formation control, will be demonstrated to illustrate its effectiveness. Additionally, the integration of this framework with other Lyapunov-based control techniques for handling unexpected actuator faults and communication disruptions will be discussed, highlighting its potential to further enhance UAV operational efficiency and <a href="http://reliability.
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Speaker(s): Binyan Xu, Ph.D.,