Path following for podded propulsion unmanned surface vehicle: Theory, simulation and experiment

Abstract

This paper addresses three interrelated problems concerning the path following of a podded propulsion unmanned surface vessel (USV), namely, the modeling of podded propulsion USV, the model parameter identification and the guidance law design. Based on hypotheses and simplification, three degree of freedom (DOF) manipulating mathematical model group (MMG) models of podded propulsion USV are reduced to the response model, and then, a series of field experiments (turning experiment and zig-zag experiment) is carried out to collect corresponding data. On this basis, the parameters of the model are obtained through the method of system identification. Meanwhile, the simulation data are compared with the actual data to verify the accuracy of the identification results. The conventional line-of-sight guidance is modified by adaptive sideslip angle and a time-varying lookahead distance, where the lookahead distance is adjusted by fuzzy algorithm. Lyapunov function is used to prove that all error signals in the system are uniformly ultimately bounded. Three numerical examples and the real ship field experiment have been described to illustrate the performance and effectiveness of the proposed scheme.

Filtered Extended State Observer Based Line-of-Sight Guidance for Path Following of Unmanned Surface Vehicles With Unknown Dynamics and Disturbances

“…In principle, a smaller should be selected when the USV is far from the desired path, and this will make the cross-track error y e decrease faster; a larger is ought to be chosen when USV is close to the desired path. D. Mu et al [4] considered the time-varying look-ahead distance by using fuzzy rules, but the variation tendency of y e was not taken into account. In this context, an improved fuzzy algorithm to optimize the value of is proposed, and the system has two input entries: y e anḋ y e , the gain = λ is the output entry.…”

Robust path‐following control based on trajectory linearization control for unmanned surface vehicle with uncertainty of model and actuator saturation

“…2 The LTV feedback regulator u lc = u(e) keeps the system stable and has certain response characteristics. With consideration of d (t), (7) can be expressed as:…”

“…Based on the above analysis, the state error stabilization for the system (13) has been transformed into the problem of the system uncertainties rejection. However, the LTV feedback regulator makes (7) only achieve local exponential stability. If ||g 2 (x )d (t)|| exceeds the controllable range of TLC, the control effect will be greatly reduced or invalid.…”

“…Owing to the simplicity and effectiveness of the LOS guidance law, it has been successfully applied to the design of a path-following controller [7][8][9][10]. In the respect of external disturbance, in [11], the neural network adaptive technique is used to handle dynamical uncertainty and ocean disturbances induced by unknown wind, waves, and ocean currents.…”

Vehicle‐localization‐based and DSRC‐based autonomous vehicle rear‐end collision avoidance concerning measurement uncertainties

“…Some significant breakthroughs have been reached in the last few years, especially in autonomous vehicles [1][2][3] and electrical vehicles [4]. Many companies and universities, including some giant information and technology companies, such as Google, Delphi, Bosch, and Baidu, have researched on autonomous drivingrelated algorithms and systems [1,3].…”