Comprehensive framework for tracking control and thrust allocation for a highly overactuated autonomous surface vessel

Feemster, M.; Esposito, Joel M.

doi:10.1002/rob.20369

Cited by 53 publications

(22 citation statements)

References 32 publications

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“…This is formulated in a straightforward manner in the control allocation framework by incorporating the constraints on the tug-boats capacity and direction, (Esposito, Feemster & Smith 2008, Feemster & Esposito 2011. Its implementation requires a supervisory strategy that coordinates the tub-boats that operate as effectors/actuators in this framework.…”

Section: Multi Vessel Operationsmentioning

confidence: 99%

Control allocation—A survey

2013

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The control algorithm hierarchy of motion control for over-actuated mechanical systems with a redundant set of effectors and actuators commonly includes three levels. First, a high-level motion control algorithm commands a vector of virtual control efforts (i.e. forces and moments) in order to meet the overall motion control objectives. Second, a control allocation algorithm coordinates the different effectors such that they together produce the desired virtual control efforts, if possible. Third, low-level control algorithms may be used to control each individual effector via its actuators. Control allocation offers the advantage of a modular design where the high-level motion control algorithm can be designed without detailed knowledge about the effectors and actuators. Important issues such as input saturation and rate constraints, actuator and effector fault tolerance, and meeting secondary objectives such as power efficiency and tear-and-wear minimization are handled within the control allocation algorithm. The objective of the present paper is to survey control allocation algorithms, motivated by the rapidly growing range of applications that have expanded from the aerospace and maritime industries, where control allocation has its roots, to automotive, mechatronics, and other industries. The survey classifies the different algorithms according to two main classes based on the use of linear or nonlinear models, respectively. The presence of physical constraints (e.g input saturation and rate constraints), operational constraints and secondary objectives makes optimization-based design a powerful approach. The simplest formulations allow explicit solutions to be computed using numerical linear algebra in combination with some logic and engineering solutions, while the more challenging formulations with nonlinear models or complex constraints and objectives call for iterative numerical optimization procedures. Experiences using the different methods in aerospace, maritime, automotive and other application areas are discussed. The paper ends with some perspectives on new applications and theoretical challenges.

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Section: Multi Vessel Operationsmentioning

confidence: 99%

Control allocation—A survey

2013

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“…Other work towards caging manipulation on the water [14] addresses the close quarters maneuvering inherent to self-assembly activities. Finally, research in highly overactuated autonomous surface vessels anticipates future applications in autonomous tugboats [15], or potentially the cooperative manipulation of linked structures such as ours.…”

Section: Related Workmentioning

confidence: 99%

Automated Self-Assembly of Large Maritime Structures by a Team of Robotic Boats

Paulos¹,

Eckenstein²,

Tosun³

et al. 2015

IEEE Trans. Automat. Sci. Eng.

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We present the methodology, algorithms, system design, and experiments addressing the self-assembly of large teams of autonomous robotic boats into floating platforms. Identical self-propelled robotic boats autonomously dock together and form connected structures with controllable variable stiffness. These structures can self-reconfigure into arbitrary shapes limited only by the number of rectangular elements assembled in brick-like patterns. An complexity algorithm automatically generates assembly plans which maximize opportunities for parallelism while constructing operator-specified target configurations with components. The system further features an complexity algorithm for the concurrent assignment and planning of trajectories from free robots to the growing structure. Such peer-to-peer assembly among modular robots compares favorably to a single active element assembling passive components in terms of both construction rate and potential robustness through redundancy. We describe hardware and software techniques to facilitate reliable docking of elements in the presence of estimation and actuation errors, and we consider how these local variable stiffness connections may be used to control the structural properties of the larger assembly. Assembly experiments validate these ideas in a fleet of 0.5 m long modular robotic boats with onboard thrusters, active connectors, and embedded computers.Note to Practitioners-This work addresses the deployment of large scale floating structures to accelerate humanitarian missions or disaster relief by assembling together many self-propelled ISO shipping containers equipped with actuators and sensors. Thousands of modules would be needed to form temporary bridges, harbors, or air strips in a full-scale deployment; we give efficient solutions to the ensuing large-scale assembly planning and multiboat routing problems. This work will be of interest to those considering assembly planning with many identical pieces. Our 1:12 scale experiments serve as a proof of concept system and a case study in the design of practical self-assembling components. The docking and maneuverability design elements will be of interest to those

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“…The equality constraints can be transformed into an inequality form and can be combined with other inequality constraints (32) Most heuristic search methods use the penalty function approach of handling constraints. The penalty function approach involves a number of penalty coefficients, which must be set right in any problem to obtain feasible solutions.…”

Section: Constraint Handling Using Superiority Of Feasible Solutionsmentioning

confidence: 99%

Optimal Thrust Allocation for Semisubmersible Oil Rig Platforms Using Improved Harmony Search Algorithm

Yadav

Kumar

Panda

et al. 2014

IEEE J. Oceanic Eng.

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Deep-water offshore drilling vessels, such as a semisubmersible drilling rig, use the dynamic positioning (DP) system and the thruster-assisted position-mooring system for maintaining a stationary position. In the DP system, the thrust allocator is used to distribute the desired generalized forces computed by the motion controller among the thrusters. However, to ensure safe operation of the vessel despite the thruster failure, the vessel is equipped with redundant thruster configuration and, therefore, is overactuated. For overactuated vessels, the choice of a particular solution for thrust allocation is found using some kind of optimization process. In this paper, the thrust allocator tries to minimize the power consumption and takes forbidden/spoil zones into account. The formulated thrust allocation problem becomes nonconvex due to thrust direction constraints on azimuth thrusters. The conventional methods get trapped in local minima and fail to find the optimum solution for the formulated nonconvex thrust allocation problem. In this paper, an improved harmony search (IHS) algorithm for solving the nonconvex thrust allocation problem is proposed. IHS is a variant of the harmony search (HS) algorithm. The HS algorithm is a music-based meta-heuristic optimization method, which is analogous with the music improvisation process where a musician continues to polish the pitches to obtain better harmony. Obtained numerical results show that the IHS algorithm has better convergence property when compared to the HS algorithm and the genetic algorithm (GA). Moreover, the power consumption for thrust allocation using the IHS algorithm is lower when compared with HS, GA, and Mincon (sequential quadratic programming) algorithms. The percentage savings in total power consumption for thruster allocation as compared to the Mincon algorithm for GA, HS, and IHS methods are 44.96%, 48.39%, and 51.58%, respectively.Index Terms-Dynamic positioning (DP), genetic algorithm (GA), harmony search (HS), optimization, thrust allocation problem.

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Comprehensive framework for tracking control and thrust allocation for a highly overactuated autonomous surface vessel

Cited by 53 publications

References 32 publications

Control allocation—A survey

Control allocation—A survey

Automated Self-Assembly of Large Maritime Structures by a Team of Robotic Boats

Optimal Thrust Allocation for Semisubmersible Oil Rig Platforms Using Improved Harmony Search Algorithm

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