Event-Triggered Heading Control of an Energy-Efficient Underwater Gliding Robot

IF 8.6 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Anyan Jing;Jian Gao;Boxu Min;Jiarun Wang;Yimin Chen;Guang Pan
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引用次数: 0

Abstract

An energy-efficient event-triggered heading control system (ETHCS) is proposed for an underwater gliding robot. The robot steers by adjusting the position of an internal axially moving mass, rather than with the actuators commonly used in traditional underwater vehicles. Based on the dynamic model, an energy-efficient ETHCS framework, which includes three linear velocity observers and an event-triggered high-order backstepping heading controller, is established. The three linear state observers estimate the horizontal and angular velocities of the robot using depth and attitude angles measured by a depth sensor and an attitude and heading reference system (AHRS). The controller is developed by using the dynamic surface control technique to avoid differentiating virtual control laws. A neural network is used to approximate recursive model uncertainties and disturbances. Relative threshold event-triggering conditions are designed to reduce the frequency of actuator action and the controller working time and to improve the energy-saving efficiency. The simulation and sea trial results show the effectiveness of the proposed control system.
高能效水下滑行机器人的事件触发式航向控制
为水下滑行机器人提出了一种节能的事件触发航向控制系统(ETHCS)。该机器人通过调整内部轴向移动质量块的位置来转向,而不是使用传统水下航行器中常用的致动器。在动态模型的基础上,建立了一个高能效的 ETHCS 框架,其中包括三个线性速度观测器和一个事件触发的高阶反步航向控制器。三个线性状态观测器利用深度传感器和姿态与航向参考系统(AHRS)测量的深度和姿态角来估计机器人的水平速度和角速度。控制器的开发采用了动态表面控制技术,以避免区分虚拟控制律。神经网络用于近似递归模型不确定性和干扰。设计了相对阈值事件触发条件,以减少执行器动作频率和控制器工作时间,提高节能效率。仿真和海上试验结果表明了所提控制系统的有效性。
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来源期刊
IEEE Transactions on Systems Man Cybernetics-Systems
IEEE Transactions on Systems Man Cybernetics-Systems AUTOMATION & CONTROL SYSTEMS-COMPUTER SCIENCE, CYBERNETICS
CiteScore
18.50
自引率
11.50%
发文量
812
审稿时长
6 months
期刊介绍: The IEEE Transactions on Systems, Man, and Cybernetics: Systems encompasses the fields of systems engineering, covering issue formulation, analysis, and modeling throughout the systems engineering lifecycle phases. It addresses decision-making, issue interpretation, systems management, processes, and various methods such as optimization, modeling, and simulation in the development and deployment of large systems.
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