Trajectory Tracking of a Fully-actuated Surface Vessel using Nonlinear Model Predictive Control

Kinjo, Leticia Mayumi; Wirtensohn, Stefan; Reuter, Johannes; Ménard, Tomas; Gehan, Olivier

doi:10.1016/j.ifacol.2021.10.072

Cited by 7 publications

(6 citation statements)

References 16 publications

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“…To minimize the cumulated costs (29) subject to the given dynamics (16a)-(16d), for the inequality constraints (26) and the equality constraints (28) using MPPI control, the OCP has to be formulated in the assumed structure (1a)-(1b). Consequently, the equality and inequality constraints must be considered in the cost function with…”

Section: Resulting Problem Formulationmentioning

confidence: 99%

“…In [26], a detailed description of the dynamics of the research vessel Solgenia including identification of the model parameters and the calculation of the actuator thrusts is outlined. Nevertheless, a short description of the modeling is given in the following since the model is an elementary component of the MPPI approach used in the scenario.…”

Section: Dynamics Of the Vesselmentioning

confidence: 99%

“…where a 1 , a 2 , b 1 , b 2 ∈ R denote the coefficients of the AT. The parameters identified by [26] are listed in Table 2. Note that, according to [26], the parameters b 1 , b 2 and c b are eliminated during the evaluation phase of the identification process.…”

Section: Dynamics Of the Vesselmentioning

confidence: 99%

“…The parameters identified by [26] are listed in Table 2. Note that, according to [26], the parameters b 1 , b 2 and c b are eliminated during the evaluation phase of the identification process. For more detailed information about the dynamics, the reader is referred to [24,26,27].…”

Section: Dynamics Of the Vesselmentioning

confidence: 99%

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Feature-Based MPPI Control with Applications to Maritime Systems

et al. 2022

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In this paper, a novel feature-based sampling strategy for nonlinear Model Predictive Path Integral (MPPI) control is presented. Using the MPPI approach, the optimal feedback control is calculated by solving a stochastic optimal control (OCP) problem online by evaluating the weighted inference of sampled stochastic trajectories. While the MPPI algorithm can be excellently parallelized, the closed-loop performance strongly depends on the information quality of the sampled trajectories. To draw samples, a proposal density is used. The solver’s and thus, the controller’s performance is of high quality if the sampled trajectories drawn from this proposal density are located in low-cost regions of state-space. In classical MPPI control, the explored state-space is strongly constrained by assumptions that refer to the control value’s covariance matrix, which are necessary for transforming the stochastic Hamilton–Jacobi–Bellman (HJB) equation into a linear second-order partial differential equation. To achieve excellent performance even with discontinuous cost functions, in this novel approach, knowledge-based features are introduced to constitute the proposal density and thus the low-cost region of state-space for exploration. This paper addresses the question of how the performance of the MPPI algorithm can be improved using a feature-based mixture of base densities. Furthermore, the developed algorithm is applied to an autonomous vessel that follows a track and concurrently avoids collisions using an emergency braking feature. Therefore, the presented feature-based MPPI algorithm is applied and analyzed in both simulation and full-scale experiments.

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Section: Resulting Problem Formulationmentioning

confidence: 99%

Section: Dynamics Of the Vesselmentioning

confidence: 99%

Section: Dynamics Of the Vesselmentioning

confidence: 99%

Section: Dynamics Of the Vesselmentioning

confidence: 99%

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Feature-Based MPPI Control with Applications to Maritime Systems

et al. 2022

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“…To consider real-world ship dynamics, several studies have incorporated system identification techniques. In Kinjo et al (2021), the hydrodynamic and propulsion parameters of a vessel were identified using a system identification method, and the results were used for the NMPC-based trajectory tracking of the vessel. Wang et al (2018) designed an unmanned surface vehicle (USV) called Roboat and formulated an NMPC strategy with experimentally identified hydrodynamic parameters.…”

Section: Related Workmentioning

confidence: 99%

Field experiment of autonomous ship navigation in canal and surrounding nearshore environments

Kim,

Lee,

Chung

et al. 2023

Journal of Field Robotics

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In this paper, we present the development of autonomous navigation capabilities for small cruise boats, and their verification by field experiments in a canal and its surrounding waters. A cruise boat was converted to an autonomous surface vehicle (ASV) by installing various sensors and actuators to enable autonomous navigation. Navigation and perception sensors, such as global positioning system, attitude and heading reference system, radar, light detection and ranging (LiDAR), and cameras, were mounted on the ASV to estimate its motion and perceive the surrounding environment. Motors and potentiometers were installed for active control of the ASV. Software system components including navigation filters, object‐detection, path‐planning, and control algorithms were designed and implemented. In the narrow canal region, LiDARs were used to detect the side walls and boundaries of the canal. In open areas outside the canal, obstacles and object features were detected using various combinations of onboard sensors. A model‐based path‐planning algorithm was designed to avoid the detected obstacles, and the line‐of‐sight guidance was employed to control the vehicle. The performance of the developed system was verified through a field experiment in a real‐world maritime environment.

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Positioning the Semi-Submersible Platform Using Model Predictive Control with Thruster Constraints

Do,

Dang,

et al. 2023

2023 Asia Meeting on Environment and Electrical Engineering (EEE-AM)

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Trajectory Tracking of a Fully-actuated Surface Vessel using Nonlinear Model Predictive Control

Cited by 7 publications

References 16 publications

Feature-Based MPPI Control with Applications to Maritime Systems

Feature-Based MPPI Control with Applications to Maritime Systems

Field experiment of autonomous ship navigation in canal and surrounding nearshore environments

Positioning the Semi-Submersible Platform Using Model Predictive Control with Thruster Constraints

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