Minimum time ship maneuvering using neural network and nonlinear model predictive compensator

Mizuno, Naoki; Kuroda, Masaki; Okazaki, Tadatsugi; Ohtsu, Kohei

doi:10.1016/s1474-6670(17)31748-2

Cited by 8 publications

(4 citation statements)

References 4 publications

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“…During the 1990s and early 2000s, AI-based control strategies were predominantly used in simulations. The physical trials of these AI-based control methods consisted of fuzzy control in 1992 [13], ANN in a small-scale test in 2003 [35], and a combination of ANN and SCGR in a full-scale test in 2004 [38]. Of note, traditional control methods, such as PID in combination with a guidance law, are commonly employed for trajectory tracking or waypoint following, while more sophisticated methods are used for path and trajectory planning, giving rise to a large number of traditional control methods employed both in simulations and physical trials.…”

Section: Discussionmentioning

confidence: 99%

“…The earliest uses of ANNs were so-called shallow networks, consisting of only one hidden layer, while modern methods use deeper networks with vastly more parameters, at the expense of requiring huge computational resources to train. ANNs were used in 52 of the publications [10], [14], [15], [17], [18], [20], [23], [24], [31]- [35], [37], [38], [43], [44], [49]- [51], [53], [67], [72]- [75], [80], [81], [85], [86], [103], [108], [115], [123], [127], [129], [145], [148], [150], [171], [172], [176], [182], [183], [185], [199], [200], [202], [211], [215], [216], [226].…”

Section: Ann (Artificial Neural Network)mentioning

confidence: 99%

“…It offers efficient solutions for control problems with nonlinear dynamics and constraints by iteratively updating the control inputs based on gradient information. SCGR was used in 5 publications [12], [30], [38], [44], [53].…”

Section: Cma-es (Covariance Matrix Adaption Evolution Strategy)mentioning

confidence: 99%

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Automated Docking for Marine Surface Vessels—A Survey

Lexau,

Breivik,

Lekkas

2023

IEEE Access

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Research on motion control systems for marine surface vessels has generated a vast academic literature and many industrial applications since the early 20th century. The recent focus on autonomous ships has sparked intensive research and innovation activities of dock-to-dock operations, including the final docking phase. Specifically, automated docking involves systems that enable vessels to dock safely, independently, and energy-efficiently at designated locations with a specific heading. Challenges include managing large sideslip angles, static and dynamic obstacles, and navigation in complex port geometries. Despite its complexity, the docking problem has received less attention compared to dynamic positioning or course-keeping systems. This paper therefore provides a thorough overview of the research on automated docking for marine surface vessels, from 1980 to June 2023. The paper introduces the Docking Characteristic Index (DCI) as a metric to identify the scope and approach of systems addressing various docking challenges, not as an evaluation of their overall quality. Using the calculated DCI-Scores, we discuss some of the works in more detail. The survey reveals a rising trend in publications and an increasing emphasis on experimental verification. Despite advancements, current methodologies exhibit trade-offs and limitations, particularly in handling dynamic obstacles, robustness against external forces, and situational awareness. The paper identifies the need for more comprehensive and integrated solutions to these challenges. As the demand for fully autonomous operations grows, the results suggest that future research should focus on developing holistic and robust docking strategies, that are verified experimentally, to achieve safe, efficient, and effective automated docking systems.

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Section: Discussionmentioning

confidence: 99%

Section: Ann (Artificial Neural Network)mentioning

confidence: 99%

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Automated Docking for Marine Surface Vessels—A Survey

Lexau,

Breivik,

Lekkas

2023

IEEE Access

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“…Zhang et al (2020a) applied model predictive control (MPC) for obstacle avoidance in tight environment. Mizuno et al (2004) used ANN (Artificial Neural Network)-based nonlinear model predictive control (NMPC) to compensate for the control error of ship manoeuvring. In similar scenarios, a two-level architecture MPC controller has been widely used in obstacle avoidance and time-optimal tracking of ground vehicles (Gao et al, 2012;Frasch et al, 2013;Verschueren et al, 2014;Zhang et al, 2020a).…”

Section: Introductionmentioning

confidence: 99%

Time-optimal obstacle avoidance of autonomous ship based on nonlinear model predictive control

Zhang

Hao

et al. 2022

Ocean Engineering

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Real time implementation of CTRNN and BPTT algorithm to learn on-line biped robot balance: Experiments on the standing posture

Hénaff

Scesa²,

Ouezdou³

et al. 2011

Control Engineering Practice

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This paper describes experimental results regarding the real time implementation of continuous time recurrent neural networks (CTRNN) and the dynamic back-propagation through time (BPTT) algorithm for the on-line learning control laws. Experiments are carried out to control the balance of a biped robot prototype in its standing posture. The neural controller is trained to compensate for external perturbations by controlling the torso's joint motions. Algorithms are embedded in the real time electronic unit of the robot. On-line learning implementations are presented in detail. The results on learning behavior and control performance demonstrate the strength and the efficiency of the proposed approach.

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Minimum time ship maneuvering using neural network and nonlinear model predictive compensator

Cited by 8 publications

References 4 publications

Automated Docking for Marine Surface Vessels—A Survey

Automated Docking for Marine Surface Vessels—A Survey

Time-optimal obstacle avoidance of autonomous ship based on nonlinear model predictive control

Real time implementation of CTRNN and BPTT algorithm to learn on-line biped robot balance: Experiments on the standing posture

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