- The paper introduces a real-time control strategy leveraging Koopman theory and sparse identification to continuously update a high-dimensional model.
- It integrates Control Lyapunov and Barrier Functions with Quadratic Programming for robust error compensation and precise trajectory tracking.
- Experimental simulations show significant improvements in control precision and reduced computational demands in dynamic USV environments.
Introduction
The field of Unmanned Surface Vehicles (USVs) has seen remarkable growth, spurred by their potential applications in domains like scientific exploration, search and rescue, and defense. These vehicles face challenging environments characterized by complex surface obstacles and dynamic conditions such as shifting winds and unpredictable water currents. Controlling these vehicles accurately under such fluctuating conditions is a critical research area.
Real-time Control Strategy
A novel control method is proposed based on Koopman theory, a concept from dynamical systems theory that transforms nonlinear dynamics into a high-dimensional linear representation. This transformation facilitates real-time control by providing a simplified yet dynamic model.
The proposed control system enhances the accuracy of trajectory tracking through an overview of online learning and error compensation. It employs a sparse identification process to construct a high-dimensional linear dynamical model. This model is continuously updated with real-time data to maintain its precision.
Control Framework
To manage the complexity of the system and track the vehicle accurately, the paper integrates a constructive control technique using a Control Lyapunov Function (CLF), a Control Barrier Function (CBF), and Quadratic Programming (QP). This method makes use of an error threshold to determine when to utilize newly observed state quantities for model re-evaluation. This process not only ensures the fidelity of the system's model to environmental changes but also streamlines computation.
Experimental Validation
Simulations were conducted to evaluate the effectiveness of the proposed control approach. These simulations showed a significant enhancement in control precision compared to traditional methods that do not incorporate this dynamic system identification. With the integration of an error domain identification system, the proposed control solution maintained lower overall system load requirements.
Conclusion
The method introduced in this paper offers a robust framework for the real-time identification and control of Unmanned Surface Vehicles' dynamics, capable of adapting to complex and time-varying environmental conditions. The control method eschews the need for high-frequency system identification and real-time parameter updates, thereby reducing computational demands and enabling more accurate and reliable navigation and control of USVs. The simulation results confirmed the benefits of the online data-driven control strategy, paving the way for future work to potentially implement these findings in physical USV models under real-world conditions.