Review of nonlinear tracking and setpoint control approaches for autonomous underactuated marine vehicles

Ashrafiuon, Hashem; Muske, Kenneth R.; McNinch, Lucas C.

doi:10.1109/acc.2010.5530450

Cited by 74 publications

(62 citation statements)

References 79 publications

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“…, (16) Non-adaptive velocity tracking controller Theorem 1. Consider the vehicle dynamic model (6), the kinematic relationship (7), and the velocity transformation (3) together with the following controller:…”

Section: Robustness Issuementioning

confidence: 99%

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Non-adaptive velocity tracking controller for a class of vehicles

Herman

Adamski

2017

Bulletin of the Polish Academy of Sciences Technical Sciences

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Abstract.A non-adaptive controller for a class of vehicles is proposed in this paper. The velocity tracking controller is expressed in terms of the transformed equations of motion in which the obtained inertia matrix is diagonal. The control algorithm takes into account the dynamics of the system, which is included into the velocity gain matrix, and it can be applied for fully actuated vehicles. The considered class of systems includes underwater vehicles, fully actuated hovercrafts, and indoor airship moving with low velocity (below 3 m/s) and under assumption that the external disturbances are weak. The stability of the system under the designed controller is demonstrated by means of a Lyapunov-based argument. Some advantages arising from the use of the controller as well as the robustness to parameters uncertainty are also considered. The performance of the proposed controller is validated via simulation on a 6 DOF robotic indoor airship as well as for underwater vehicle model.

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Section: Robustness Issuementioning

confidence: 99%

“…Some control strategies for surface vessel including ships and hovercrafts can be found in [5][6][7][8][9][10][11][12][13][14][15]. Tracking controllers useful for marine vessels are described also in [16][17][18][19][20]. Referring to the airship trajectory tracking problem control algorithms are shown, e.g., in [21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Non-adaptive velocity tracking controller for a class of vehicles

Herman

Adamski

2017

Bulletin of the Polish Academy of Sciences Technical Sciences

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“…Some of the other implementations of RANS code can be found in [25,26,27]. There are many research papers reported in the area of the underactuated controller design for the USVs that utilize 3 DOF simplified models which neglect the rolling, pitching, and heaving motions [28,29,30,31,32,33]. Strip theory is mainly used for slowly moving slender geometries [34,35,36].…”

Section: Fluid-rigid Body Interaction Simulationmentioning

confidence: 99%

“…We chose PID controller because of its widespread use and ease of implementation, however, in order to execute the commanded control actions any other controller such as the backstepping [68], sliding mode, etc. can be used [28]. 9 …”

Section: Motion Equations: Interaction Between Usvs and Ocean Wavesmentioning

confidence: 99%

GPU based generation of state transition models using simulations for unmanned surface vehicle trajectory planning

Thakur

Švec

Gupta

2012

Robotics and Autonomous Systems

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This paper describes GPU based algorithms to compute state transition model for unmanned surface vehicles (USVs) using 6 degree of freedom (DOF) dynamics simulations of vehicle-wave interaction. State transition model is a key component of Markov Decision Process (MDP), which is a natural framework to formulate the problem of trajectory planning under motion uncertainty. USV trajectory planning problem is characterized by the presence of large and somewhat stochastic forces due to ocean waves, which can cause significant deviations in their motion. Feedback controllers are often employed to reject disturbances and get back on the desired trajectory. However, the motion uncertainty can be significant and must be considered in the trajectory planning to avoid collisions with the surrounding obstacles. In case of USV missions, state transition probabilities need to be generated on-board, to compute trajectory plans that can handle dynamically changing USV parameters and environment (e.g., changing boat inertia tensor due to fuel consumption, variations in damping due to changes in water density, variations in sea-state, etc.). The 6 DOF dynamics simulations reported in this paper are based on potential flow theory. We also present a model simplification algorithm based on temporal coherence and its GPU implementation to accelerate simulation computation performance. Using the techniques discussed in this paper we were able to compute state transition probabilities in less than 10 minutes. Computed transition probabilities are subsequently used in a stochastic dynamic programming based approach to solve the MDP to obtain trajectory plan. Using this approach, we are able to generate dynamically feasible trajectories for USVs that exhibit safe behaviors in high sea-states in the vicinity of static obstacles.

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“…The planned path is a combination of basic track units representing waypoint navigation polygonal sequences and circular arcs with high accuracy. Lot of research has been done in the area of the underactuated controller design for USVs that utilize 3-DOF simplified models, which neglect the rolling, pitching, and heaving motions [17][18][19][20][21][22].…”

Section: Related Workmentioning

confidence: 99%