Unscented Kalman Filter trained neural network control design for ship autopilot with experimental and numerical approaches |
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Institution: | 1. Iranian National Institute for Oceanography and Atmospheric Science, Tehran, Iran;2. School of Civil Engineering, University of Tehran, Tehran, Iran;1. Department of Civil Engineering, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece;2. Department of Naval Architecture and Ocean Engineering, Osaka University, Suita, Osaka, 5650871, Japan;1. College of Ocean Science and Engineering, Shanghai Maritime University, 1550 Haigang Rd., Shanghai, 201304, China;2. Institute for Ocean Engineering, China University of Petroleum, 18 Fuxue Rd., Beijing, 102249, China;3. Department of Aeronautics and Astronautics, Fudan University, 220 Handan Rd., Shanghai, 200433, China |
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Abstract: | In the recent decades, the application and research of unmanned surface vessels are experiencing considerable growth, which have caused the demands of intelligent autopilots to grow along with the ever-growing requirements. In this study, the design of an autopilot based on Unscented Kalman Filter (UKF) trained Radial Basis Function Neural Networks (RBFNN) was presented. In particular, in order to provide satisfactory control performance for surface vessels with random external disturbances, the modified UKF was utilised as the weights training mechanism for the RBFNN based controller. The configurations of the newly developed free running scaled model, as well as the online signal processing method, were introduced to enable the experimental studies. The experimental and numerical tests were carried out through using the physical scaled model and corresponding mathematical model to validate the capability of the designed control system under various sailing conditions. The results indicated that the UKF RBFNN based autopilot satisfied the functionalities of course keeping, course changing and trajectory tracking only using the rudder as the actuator. It was concluded that the developed control scheme was effective to track the desired states and robust against unpredictable external disturbances. Moreover, in comparison with Back-Propagation (BP) RBFNN and Proportional-Derivative (PD) based autopilots, the UKF RBFNN based autopilot has the comparable capability in the aspects of providing smooth and effective control laws. |
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Keywords: | Intelligent autopilot Neural networks Unscented Kalman filter training Free running model scaled ship Experimental test |
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