论文标题
无人驾驶水下车辆的混合跟踪控制策略有助于生物启发的神经动力学
A Hybrid Tracking Control Strategy for an Unmanned Underwater Vehicle Aided with Bioinspired Neural Dynamics
论文作者
论文摘要
跟踪控制一直是机器人技术的重要研究主题。本文为基于生物启发的神经动力学模型提供了一种新型的混合控制策略（UUV）。首先开发了增强的反向运动运动控制策略，以避免急速速度跳跃，并提供相对于传统方法的平滑速度命令。然后，提出了一种新颖的滑动模式控制，该控制能够提供平滑而连续的扭矩命令，而无需搅动。在比较研究中，提出的合并混合控制策略确保了控制信号的平滑度，这在现实世界中至关重要，特别是对于需要在复杂的水下环境中运行的无人水下车辆。
Tracking control has been a vital research topic in robotics. This paper presents a novel hybrid control strategy for an unmanned underwater vehicle (UUV) based on a bioinspired neural dynamics model. An enhanced backstepping kinematic control strategy is first developed to avoid sharp velocity jumps and provides smooth velocity commands relative to conventional methods. Then, a novel sliding mode control is proposed, which is capable of providing smooth and continuous torque commands free from chattering. In comparative studies, the proposed combined hybrid control strategy has ensured control signals smoothness, which is critical in real world applications, especially for an unmanned underwater vehicle that needs to operate in complex underwater environments.
