This paper focuses on the problem of linear track keeping for marine surface vessels. The influence exerted by sea currents on the kinematic equation of ships is considered first. The input-to-state stability(ISS) the...This paper focuses on the problem of linear track keeping for marine surface vessels. The influence exerted by sea currents on the kinematic equation of ships is considered first. The input-to-state stability(ISS) theory used to verify the system is input-to-state stable. Combining the Nussbaum gain with backstepping techniques,a robust adaptive fuzzy algorithm is presented by employing fuzzy systems as an approximator for unknown nonlinearities in the system. It is proved that the proposed algorithm that guarantees all signals in the closed-loop system are ultimately bounded. Consequently,a ship's linear track-keeping control can be implemented. Simulation results using Dalian Maritime University's ocean-going training ship 'YULONG' are presented to validate the effectiveness of the proposed algorithm.展开更多
Geomagnetic orbit determination fits for nanosatellites which pursue low cost and high-density ratio,but one of its disadvantages is the poor position accuracy introduced by magnetic bias.Here,a new method,named the f...Geomagnetic orbit determination fits for nanosatellites which pursue low cost and high-density ratio,but one of its disadvantages is the poor position accuracy introduced by magnetic bias.Here,a new method,named the fuzzy regulating unscented Kalman filter(FRUKF),is proposed.The magnetic bias is regarded as a random walk model,and a fuzzy regulator is designed to estimate the magnetic bias more accurately.The input of the regulator is the derivative of magnetic bias estimated from unscented Kalman filter(UKF).According to the fuzzy rule,the process noise covariance is adaptively determined.The FRUKF is evaluated using the real-flight data of the SWARMA.The experimental results show that the root-mean-square(RMS)position error is 3.1 km and the convergence time is shorter than the traditional way.展开更多
基金Supported by the National Natural Science Foundation of China under Grant No. 10572094.
文摘This paper focuses on the problem of linear track keeping for marine surface vessels. The influence exerted by sea currents on the kinematic equation of ships is considered first. The input-to-state stability(ISS) theory used to verify the system is input-to-state stable. Combining the Nussbaum gain with backstepping techniques,a robust adaptive fuzzy algorithm is presented by employing fuzzy systems as an approximator for unknown nonlinearities in the system. It is proved that the proposed algorithm that guarantees all signals in the closed-loop system are ultimately bounded. Consequently,a ship's linear track-keeping control can be implemented. Simulation results using Dalian Maritime University's ocean-going training ship 'YULONG' are presented to validate the effectiveness of the proposed algorithm.
基金supported by the National Natural Science Foundation of China(No.61673212).
文摘Geomagnetic orbit determination fits for nanosatellites which pursue low cost and high-density ratio,but one of its disadvantages is the poor position accuracy introduced by magnetic bias.Here,a new method,named the fuzzy regulating unscented Kalman filter(FRUKF),is proposed.The magnetic bias is regarded as a random walk model,and a fuzzy regulator is designed to estimate the magnetic bias more accurately.The input of the regulator is the derivative of magnetic bias estimated from unscented Kalman filter(UKF).According to the fuzzy rule,the process noise covariance is adaptively determined.The FRUKF is evaluated using the real-flight data of the SWARMA.The experimental results show that the root-mean-square(RMS)position error is 3.1 km and the convergence time is shorter than the traditional way.
