Information fusion model-free adaptive control algorithm and unmanned surface vehicle heading control |
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| Institution: | 1. School of Electrical Engineering, Chongqing University, Chongqing 400044, China;2. State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100083, China;3. Korea Advanced Institute of Science and Technology, Daejeon 305701, Republic of Korea;4. Center for Automotive Research, The Ohio State University, OH 43212, USA;1. Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran;2. Department of Maritime Engineering, Amirkabir University of Technology, Tehran, Iran;1. APAC Research Group, Industrial Control Center of Excellence, Faculty of Electrical Engineering, K.N. Toosi University of Technology, 16317-14191 Tehran, Iran;2. Faculty of Electrical Engineering, K.N. Toosi University of Technology, 16317-14191 Tehran, Iran |
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| Abstract: | Based on the model-free adaptive control (MFAC) theory, the heading control problem of unmanned surface vehicles (USVs) with uncertainties is explored. First, as a USV’s heading subsystem does not satisfy the quasilinear assumption of the MFAC theory, a new type of input and output information fusion MFAC, i.e., the IOIF–MFAC algorithm is proposed. The novel algorithm proposed herein renders the MFAC theory applicable to the heading control of USVs. Next, the input and output information of the heading subsystem, namely the rudder angle and heading angle, are combined, and the data model of the heading subsystem is subsequently deduced using a compact format dynamic linearization method. Based on which, the stability of the control system is proved. Finally, the effectiveness and practicability of the IOIF–MFAC algorithm are verified by simulation and field experiments through the “Dolphin IB” test platform developed by our group. |
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| Keywords: | Unmanned surface vehicle Heading control Model free adaptive control Input and output information fusion |
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