Models for Simulation and Control of Underwater Vehicles

Silva, Jorge Estrela da; Sousa, João de Figueiredo Borges de Tasso

doi:10.5772/5395

Cited by 9 publications

(14 citation statements)

References 12 publications

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“…Confronting the numerical expression of the analytical value of the r s in (14) with the computed value ofr, shown in 6, we see they coincide. The numerics also show that the multiplier λ is 1 as shown in the right side of figure 6 where the Hamiltonian is plotted verifying (apart from some residual numerical chattering) (8). With the computed values we can determine the values of the bounded variations functions (see, in this respect, F2 in section III)…”

Section: Numerical Resultsmentioning

confidence: 99%

“…The velocity u has three boundary arcs: we have u(t) = −2 for (approximately) t ∈ [0.64, 4.53] and for t ∈ [8,89,16, 79] while u(t) = 2 for t ∈ [5.50, 7.91].…”

Section: Sensitivity With Respect To Initial Conditionsmentioning

confidence: 99%

“…We consider the following simplified kinematic model (see, for example, [8] and references therein):…”

Section: Auv Problemmentioning

confidence: 99%

“…Moreover, validation of the numerical solution may turn to be a enormous problem. It is however well accepted that for the task of path planning simple models capturing the main characteristics of the vehicle are good enough (see [8]). …”

Section: Introductionmentioning

confidence: 99%

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Optimal control problems for path planing of AUV using simplified models

Pinho

Foroozandeh

Matos

2016

2016 IEEE 55th Conference on Decision and Control (CDC)

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Abstract-Here we propose a simplified model for the path planning of an Autonomous Under Vehicle (AUV) in an horizontal plane when ocean currents are considered. The model includes kinetic equations and a simple dynamic equation. Our problem of interest is a minimum time problem with state constraints where the control appears linearly. This problem is solved numerically using the direct method. We extract various tests from the Maximum Principle that are then used to validate the numerical solution. In contrast to many other literature we apply the Maximum Principle as defined in [9].

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Section: Numerical Resultsmentioning

confidence: 99%

“…The velocity u has three boundary arcs: we have u(t) = −2 for (approximately) t ∈ [0.64, 4.53] and for t ∈ [8,89,16, 79] while u(t) = 2 for t ∈ [5.50, 7.91].…”

Section: Sensitivity With Respect To Initial Conditionsmentioning

confidence: 99%

“…We consider the following simplified kinematic model (see, for example, [8] and references therein):…”

Section: Auv Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimal control problems for path planing of AUV using simplified models

Pinho

Foroozandeh

Matos

2016

2016 IEEE 55th Conference on Decision and Control (CDC)

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“…Throughout the rest of the document we will be modeling the vehicle as a two-dimensional integrator (equation 1.3) for the sake of simplicity. However, two relevant vehicle models will be presented next, summarizing some of the main results in [20,12].…”

Section: Modelsmentioning

confidence: 99%

Multi-agent coordination with event-based communication

Teixeira

Dimarogonas

Johansson

et al. 2010

Proceedings of the 2010 American Control Conference

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In recent years there has been an increasing interest in mobile multi-agent systems, with the problem of how to drive a formation of N agents from one configuration to another being a key issue in many applications, particularly when each agent has limited (communication and sensing) capabilities. One such example is the navigation of glider formations, where N AUVs have to reach a target position within a given time, with sensing and communication being limited to discrete time instants. With this example as motivation, we begin by studying the single agent version of the problem, where reachability concepts are used to deal with the issue of position uncertainty, allowing us to develop two different control strategies. These strategies are then used to tackle the two-agent problem, which we generalize to obtain a partially decentralized approach to the multiagent problem. Sufficient conditions for the successful application of these strategies, as well as the corresponding bounds on the uncertainty are also derived. The problem of estimating the disturbance set is then studied, with the objective of providing the network with increased robustness to scenarios in which there is few information about the disturbance set. Finally, using normally distributed disturbances, some relevant scenarios of both the single and multi-agent problems are simulated, which confirm the results.

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Necessary conditions for a class of optimal multiprocess with state constraints

Cortez

Pinho

Matos

2020

Intl J Robust & Nonlinear

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In this article, we derive a maximum principle for a special class of free end time optimal control of multiprocesses involving a family of control systems acting in different regions defined by state constraints. We are mainly interested in problems with contiguous time intervals. The main feature of our maximum principle is that it covers the case where some of the regions considered may not be visited. This means that the intervals where the corresponding control systems are active may be reduced to a single point. The derivation of our maximum principle is done by reformulating the optimal multiprocess problem as an equivalent fixed time state constrained optimal control problem. This reformulated problem is also of interest since it provides the means to solve optimal multiprocess problems numerically via the direct method. We illustrate our findings with an example concerning the path planning of an autonomous underwater vehicle (AUV) using a simple kinematic model derived for simulations. We use simplified point-mass model for the motion of an AUV in a horizontal plane and we assume that the ocean currents are known. We recast this problem as the multiprocess optimal control problem of interest and we study it via simulations presenting computational results partially validated by the maximum principle.

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Models for Simulation and Control of Underwater Vehicles

Cited by 9 publications

References 12 publications

Optimal control problems for path planing of AUV using simplified models

Optimal control problems for path planing of AUV using simplified models

Multi-agent coordination with event-based communication

Necessary conditions for a class of optimal multiprocess with state constraints

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