The International Journal of Adaptive Control and Signal Processing recently published an article titled “Adaptive position control using backstepping technique for the leader‐follower multiple quadrotor unmanned aerial vehicle formation.” The article explores the complexities of maintaining formation control in multiple QUAV systems, addressing the inherent challenges posed by under-actuation dynamics and mismatched conditions. The research introduces a novel adaptive neural network strategy aimed at reducing computational burdens while achieving effective formation control. This innovative approach holds potential to streamline operations in various practical applications.
Challenges in Multi-QUAV Formation Control
Formation control of multi-quadrotor unmanned aerial vehicles (QUAVs) involves several complicated factors, primarily due to the translational dynamics of the system being under-actuated and not satisfying matching conditions. These dynamics create significant difficulties in designing effective formation position control. The state coupling problem further exacerbates these challenges, making the control of a multi-QUAV system considerably more complex than that of a single QUAV system.
Innovative Control Strategy
To tackle the control issues, the study combines backstepping techniques with a neural network (NN) approximation strategy, introducing an intermediary control mechanism. The neural network is utilized to address system uncertainties, with an adaptive NN control method proposed to reduce computational demands. Unlike traditional adaptive methods that require extensive parameter training, this approach only necessitates training a scalar adaptive parameter derived from the norm of the NN weight vector or matrix, thus significantly reducing the computational burden.
The implementation of this adaptive NN strategy is supported by Lyapunov stability proof and computer simulations, which demonstrate that the control tasks can be successfully executed. This indicates that the proposed method is not only effective but also efficient, potentially enhancing the performance of multiple QUAV systems in practical scenarios.
Comparing this study to earlier research, previous methods of QUAV formation control often struggled with excessive computational requirements due to the need for high approximation accuracy in adaptive parameters. Earlier approaches also faced difficulties in managing state coupling and under-actuation dynamics without significantly increasing system complexity. This new strategy offers a promising solution by simplifying the adaptive control process while maintaining robustness and efficiency.
Moreover, past attempts at addressing system uncertainties in QUAV formation control typically relied on more traditional neural network approaches, which were often computationally intensive and less practical for real-time applications. The current research’s focus on minimizing computational loads represents a significant step forward in making adaptive NN strategies more feasible for widespread use in unmanned aerial operations.
The findings of this research provide valuable insights into the development of efficient and reliable control systems for multiple QUAVs. Readers can benefit from understanding the balance between computational efficiency and control effectiveness achieved through the proposed adaptive NN strategy. This knowledge could inform future advancements in aerial robotics, particularly in applications requiring precise formation control.
