Neural Adaptive Finite-Time Formation Tracking Control for Manipulator End Effectors Under Input Constraints
针对多机器人机械臂末端执行器在输入约束下的编队跟踪问题，设计了分布式控制律，结合偏置径向基神经网络和抗饱和补偿器，实现跟踪误差的有限时间有界，并通过五平面机械臂实验验证了可行性。
This work investigates the formation tracking issue for multirobot manipulator end-effectors under input constraints. A distributed formation control law is designed to guarantee the finite-time boundedness of tracking errors within the framework. To estimate the significant bias of dynamics discovered during practical multirobot collaborative manipulation tasks, a bias radial basis function neural network (RBFNN) is integrated, along with a designed adaptive updating law for expeditious approximation. In addition, an anti-windup compensator within a finite-time framework is specifically introduced to mitigate the input saturation issue arising from torque limitations in joint actuators. Finally, the system’s semi-global practical finite-time boundedness (SGPFTB) is rigorously established through Lyapunov theory. Five planar manipulators are employed in comparative computational experiments to validate the feasibility of the presented control strategy.