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IET Cyber-Systems and Robotics
IET Cyber-Systems and Robotics  2019, Vol. 1 Issue (2): 45-54    DOI: 10.1049/iet-csr.2019.0014
    
基于极限学习的自主式水下航行器非线性模型预测控制器:仿真与实验结果
Biranchi Narayan Rath, Bidyadhar Subudhi
Department of Electrical Engineering, National Institute of Technology Rourkela, Rourkela 769008, India
Extreme learning-based non-linear model predictive controller for an autonomous underwater vehicle: simulation and experimental results
Biranchi Narayan Rath, Bidyadhar Subudhi
Department of Electrical Engineering, National Institute of Technology Rourkela, Rourkela 769008, India
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摘要： 本研究提出了一种基于极限学习的非线性模型预测控制器(NMPC)，用于自主式水下航行器(AUV)的水平航路点路径跟踪规划。该控制器由运动控制器和动态控制器组成。运动控制器使用逆推法设计，而动态控制器使用NMPC法设计。使用极限学习机(ELM)结构对水下机器人的动力学特性进行实时辨识。为了提高ELM结构的性能，采用Jaya优化算法确定了其隐含层的最优参数。然后，将得到的ELM模型用于考虑舵面约束的NMPC设计。本文提出的控制器的跟踪性能与最近提出的两种控制算法，H∞状态反馈控制器和逆最优自整定比例积分微分(PID)控制器进行了比较。所提出的控制器由MATLAB实现，并在作者实验室研制的自主式水下航行器样机上进行了实时控制。仿真和实验结果都表明，与H∞状态反馈控制器和逆最优自整定PID控制器相比，所提出的控制器具有更优异的跟踪性能。
Abstract: In this study, an extreme learning-based non-linear model predictive controller (NMPC) is proposed for path following planning of an autonomous underwater vehicle (AUV) using horizontal way-points. The proposed controller comprises a kinematic controller and a dynamic controller. The kinematic controller is designed by using back-stepping approach whilst the dynamic controller is designed by employing the NMPC approach. The dynamics of the AUV is identified in real-time by employing an extreme learning machine (ELM) structure. In view of achieving improved performance of the ELM structure, its hidden layer parameters are optimally determined by applying Jaya optimisation algorithm. The resulting ELM model is then used to design a NMPC considering the constraint on rudder planes. The tracking performance of the proposed controller is compared with that of two recently reported control algorithms namely, H∞ state feedback controller and inverse optimal self-tuning proportional–integral–derivative (PID) controller. The proposed controller is implemented using MATLAB and then in real-time on a prototype AUV developed in the authors’ laboratory. From both the simulation and experimental results obtained, it is observed that the proposed controller exhibits superior tracking performance compared to both H∞ state feedback controller and inverse optimal self-tuning PID controller.
收稿日期: 2019-04-04 出版日期: 2020-01-13
通讯作者: Bidyadhar Subudhi     E-mail: bidyadhar@iitgoa.ac.in
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引用本文:

Biranchi Narayan Rath, Bidyadhar Subudhi. Extreme learning-based non-linear model predictive controller for an autonomous underwater vehicle: simulation and experimental results. IET Cyber-Systems and Robotics, 2019, 1(2): 45-54.

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http://www.zjujournals.com/iet-csr/CN/10.1049/iet-csr.2019.0014    或    http://www.zjujournals.com/iet-csr/CN/Y2019/V1/I2/45

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