A Precisely Predefined-Time Convergent Barrier RNN for Collaborative Position and Orientation Control of Dual-Arm Robots Under Unknown Bounded Noise
提出一种新型协同位置与姿态控制方案，并设计精确预定义时间收敛的屏障递归神经网络，在未知有界噪声下实现双臂机器人末端执行器的高精度位置控制与姿态保持。
A novel collaborative position and orientation control scheme (CPOCS) for dual-arm robots is proposed, which is capable of controlling the end-effectors' positions with high precision while preserving their orientations unchanged to some practical tasks (e.g., box handling). To solve the proposed CPOCS in real time while considering key factors such as unknown bounded noise and strict time response constraints in practical engineering environments, this article proposes a novel precisely predefined-time convergent barrier recurrent neural network (PCB-RNN) based on a newly developed piecewise barrier evolution formula. Unlike existing RNNs, the proposed PCB-RNN, owing to its piecewise barrier evolution formula, can achieve precisely predefined-time convergence (PPTC) when addressing the proposed CPOCS under unknown bounded noise conditions. Comprehensive theoretical analysis rigorously proves the PPTC ability of the PCB-RNN under both noise-free and unknown bounded noise conditions. Furthermore, extensive simulation and physical experiments on dual-arm robots validate the effectiveness of the proposed CPOCS and demonstrate the advanced PPTC capability of the proposed PCB-RNN under unknown bounded noises.