The proposed DualLQR method can be extended to accommodate more complex target motions by integrating adaptive control strategies and enhancing the dual reference frame setup. One approach is to implement a dynamic model that can predict changes in the target's motion based on historical data and real-time feedback. This could involve using machine learning techniques to analyze the target's movement patterns and adjust the control parameters accordingly.
Additionally, incorporating a more sophisticated state estimation technique, such as a Kalman filter, could improve the accuracy of the target's pose estimation, especially in the presence of noise and uncertainty. By continuously updating the state estimates, the DualLQR can adapt to sudden changes in direction or velocity, allowing for tighter tracking and more responsive control.
Furthermore, the method could benefit from a multi-modal approach, where multiple DualLQR instances operate in parallel, each tuned for different motion characteristics. This would enable the system to switch between controllers based on the detected motion dynamics, ensuring optimal performance regardless of the target's behavior.