Improved Fuzzy Autopilot for Track-Keeping

Vukić, Zoran; Omerdić, Edin; Kuljača, Ljubomir

doi:10.1016/s1474-6670(17)38428-8

Cited by 10 publications

(7 citation statements)

References 4 publications

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“…One of the possibilities for improving the performance suggested in Vukic et al 15 was introducing an additional input (offset from the desired path) to enhance the information presented to the autopilot. In Vukic et al 16 an improved fuzzy autopilot for track-keeping was proposed, which uses three inputs (heading error, rate of change of heading and offset from the desired path)…”

Section: Evolution Of the Fuzzy Autopilotmentioning

confidence: 99%

“…In order to investigate different approaches for design of track-keeping control systems it is necessary to use a realistic model of the vessel and disturbances. In this paper the models of a Mariner-class vessel, steering mechanism, wave/current disturbances and Notch filter as in Fossen 17 , Vukic et al 16 and Omerdic 18 are used.…”

Section: Mathematical Modelsmentioning

confidence: 99%

“…A closed-loop control system with fuzzy autopilot for ship coursekeeping (Fig 2a) was successfully applied in Vukic et al 16 , and Omerdic. 18 This structure will serve as a basic scheme for building more complex control algorithms.…”

Section: Course-keepingmentioning

confidence: 99%

See 2 more Smart Citations

A fuzzy track-keeping autopilot for ship steering

Omerdić

Roberts

Vukić

2003

Journal of Marine Engineering & Technology

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In this paper a non-linear fuzzy autopilot for ship track-keeping is presented. The proposed autopilot has four inputs (actual and desired heading, rate of change of heading and offset from the desired path) and one output (command rudder angle). The track-keeping problem is decomposed into two subtasks: (i) follow the desired heading, and (ii) bring the ship onto the desired path and keep tracking. Internally, the autopilot consists of two autopilots that fulfil these tasks simultaneously. The proposed control scheme has been verified using a non-linear model of a Mariner-class vessel and steering mechanism under the influence of wave and current disturbances. Results presented show how such a control strategy enables improved tracking performance. 24 Journal of Marine Engineering and Technology No. A2

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Section: Evolution Of the Fuzzy Autopilotmentioning

confidence: 99%

Section: Mathematical Modelsmentioning

confidence: 99%

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A fuzzy track-keeping autopilot for ship steering

Omerdić

Roberts

Vukić

2003

Journal of Marine Engineering & Technology

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“…A common feature found in publications on strategies of ship control along a preset track is their strong dependence on the reliability of the mathematical model describing the maneuvering dynamics of the ship (linear quadratic Gaussian control [2][3][4][5], H-infinity control [6], sliding mode control [7,8], backstepping control [9][10][11][12], and modal control [13]). To avoid the above difficulties, which stem from the application of an exact mathematical model of ship dynamics, other control strategies have been developed, including the theory of fuzzy sets [14][15][16] or artificial neural networks [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Adaptive System for Steering a Ship Along the Desired Route

Borkowski

2018

Mathematics

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An adaptive ship steering system along a preset track is an example of an intelligent system. An optimal linear quadratic regulator (LQR) regulator with a symmetric indicator of control quality was adopted as the control algorithm. The model identification was based on the continuous version of the least squares method. A significant part of the article presents the proof of the stability of the proposed system. The results of the calculation experiments are provided to confirm the effective and correct working of the system.

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“…The track-keeping system could be obtained from a conventional ship course control system by simple use of the information coming from the positioning system [7]. A number of complicated controllers were designed to improve the operation of such autopilots, including the adaptive control [8,9], self-tuning [10], LQG [11,12], H ∞ [13] and those making use of fuzzy logic [14,15] and artificial neural networks [16].…”

Section: Introductionmentioning

confidence: 99%