Heading MFA control for unmanned surface vehicle with angular velocity guidance. (November 2018)

• Record Type: Journal Article
• Authors: Li, Ye; Wang, Leifeng; Liao, Yulei; Jiang, Quanquan; Pan, Kaiwen
• Is Part Of: Applied ocean research. Volume 80(2018)
• Page Start: 57
• Page End: 65
• Publication Date: 2018-11
• Subjects: Unmanned surface vehicle -- Heading control -- Model-free adaptive control -- Angular velocity guidance -- Adaptive Kalman filter; USV unmanned surface vehicle -- AKF adaptive Kalman filter -- PPD pseudo partial derivative -- MFA model-free adaptive -- EKF extended Kalman filter -- BIBO bounded-input bounded-output -- CFDL-MFA compact form dynamic linearization based MFA -- TD tracking differentiator -- I/O input and output -- SISO single input and single output -- RMS root mean square; Ocean engineering -- Periodicals; 620.416205
• DOI: 10.1016/j.apor.2018.08.015
• Languages: English
• ISSNs: 0141-1187

Abstract

Highlights: The angular velocity guidance part improves the dynamic performance including overshoot and oscillation of MFA method. The control-oriented adaptive Kalman filter is proposed which is independent of the mathematical model. The proposed adaptive Kalman filter suppresses the heading sensor noise well with stochastic model parameter perturbation. The improved MFA method has strong robustness and adaptability with uncertainties. Abstract: Based on the model-free adaptive control (MFA) theory, the heading control problem of unmanned surface vehicle (USV) with uncertainties is studied. The compact form dynamic linearization based MFA (CFDL-MFA) control method and its difficulties in USV heading control applications are analyzed. Aiming at the time delay of rudder-heading control system and the overshoot characteristic of CFDL-MFA method, the angular velocity guidance algorithm is introduced. The outer loop PID guidance controller calculates the desired angular velocity, and the inner loop MFA controller is used for angular velocity control. The heading control is realized indirectly. Considering sensor noise in applications, a control-oriented adaptive Kalman filter (AKF) based on dynamic linearized model is proposed, which effectively suppresses the adverse effect of sensor noise on heading control of USV. Numerical simulations of filtering experiments and heading control experiments are completed which demonstrate the validity of the proposed heading MFA control method with angular velocity guidance and the proposed AKF method.