Distributed Optimal Attitude Synchronization Control of Multiple QUAVs via Adaptive Dynamic Programming

• Published in: IEEE transaction on neural networks and learning systems Vol. 35; no. 6; pp. 8053 - 8063
• Main Authors: Guo, Zijie, Li, Hongyi, Ma, Hui, Meng, Wei
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.06.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive control; Adaptive dynamic programming (ADP); Algorithms; attitude constraints; Attitude control; Attitudes; Closed loops; concurrent learning (CL); Constraints; Control systems; Cost function; distributed optimal synchronization control; Dynamic programming; Feedback control; Neural networks; Parameter uncertainty; Robustness; Safety; Stability; Synchronism; Synchronization; Uncertainty; Unmanned aerial vehicles; Unmanned helicopters
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2022.3224029

This article proposes a distributed optimal attitude synchronization control strategy for multiple quadrotor unmanned aerial vehicles (QUAVs) through the adaptive dynamic programming (ADP) algorithm. The attitude systems of QUAVs are modeled as affine nominal systems subject to parameter uncertainties and external disturbances. Considering attitude constraints in complex flying environments, a one-to-one mapping technique is utilized to transform the constrained systems into equivalent unconstrained systems. An improved nonquadratic cost function is constructed for each QUAV, which reflects the requirements of robustness and the constraints of control input simultaneously. To overcome the issue that the persistence of excitation (PE) condition is difficult to meet, a novel tuning rule of critic neural network (NN) weights is developed via the concurrent learning (CL) technique. In terms of the Lyapunov stability theorem, the stability of the closed-loop system and the convergence of critic NN weights are proved. Finally, simulation results on multiple QUAVs show the effectiveness of the proposed control strategy.