Model predictive control of a hybrid autonomous underwater vehicle with experimental verification

Steenson, Leo V.; Turnock, S.R.; Phillips, Alexander B.; Harris, Catherine A.; Furlong, Maaten; Rogers, Eric; Wang, Liuping; Bodles, Kenneth; Evans, Derek W

doi:10.1177/1475090213506185

Cited by 38 publications

(40 citation statements)

References 18 publications

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“…Typical depth overshoots/variations of less than 0.2 m are observed when charging depth or speed. Such small deviations are acceptable operationally, and are consistent with or better than results using more computationally expensive MPC scheme for the same vehicle [22].…”

Section: Discussionsupporting

confidence: 77%

“…Note that, for the proposed hover-style control strategy, Z and M are proportional to (22) and (23) respectively. On the other hands, for the proposed flight-style control strategy, Z and M are proportional to (26) and (27) respectively.…”

Section: Stability and Convergence Analysismentioning

confidence: 99%

“…Model predictive control (MPC) has also been applied to depth control of over-actuated AUVs [22]. This has been shown to provide acceptable results over a range of speeds, but is computationally expensive due to the need to solve an optimisation problem before each control allocation.…”

Section: Introductionmentioning

confidence: 99%

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Depth control for an over-actuated, hover-capable autonomous underwater vehicle with experimental verification

et al. 2017

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A PI-D based control system is developed for over-actuated, hover-capable AUVs which enables a smooth transition from hover-style to flight-style operation. A system stability and convergence is proven using a Lyapunov-based approach. The performance of the controller is demonstrated by simulation but crucially is proven to provide satisfactory performance experimentally. The approach is able to operate over a range of vehicle ballasting configurations, and to imposed external disturbances. The proposed system is computationally inexpensive and does not require a detailed hydrodynamic model to implement. By monitoring the energy consumption on board, the cost of maintaining depth at a range of forward speeds with different buoyancy conditions can be quantified and their impact on cost of transport is highlighted for future optimisation of energy consumption.

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Section: Discussionsupporting

confidence: 77%

Section: Stability and Convergence Analysismentioning

confidence: 99%

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Depth control for an over-actuated, hover-capable autonomous underwater vehicle with experimental verification

et al. 2017

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“…With a combination of these actuators, the AUV is over-actuated and is capable of performing both flight-style missions and hovering missions. Extensive studies on the Delphin2 AUV are presented in [27], [28], [29], [30], [31].…”

Section: Delphin2 Auvmentioning

confidence: 99%

Grid-based GA path planning with improved cost function for an over-actuated hover-capable AUV

Tanakitkorn

Wilson

Turnock

et al. 2014

2014 IEEE/OES Autonomous Underwater Vehicles (AUV)

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Abstract-For an AUV to perform a long-range mission with its maximum endurance, its energy consumption during transit must be kept to a minimum. This paper presents an improved cost function for a grid-based genetic algorithm (GA) path planning in 2D static environments. The proposed function consists of energy consumption terms that are estimated according to dynamics of a hover-capable AUV -notably Delphin2 AUV. It seeks for a path that requires least effort for the vehicle to move along. A simulation was written in Matlab and the outcomes of the GA with the improved cost function are compared with the ones of a GA with an optimal distance approach as well as an A* approach. It is found that outcomes of an improved cost function require less energy compared with the other techniques.

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“…MPC predicts the optimal future control Composite model reference adaptive control for an unmanned underwater vehicle profile using a mathematical model of the system and current states. It has been simulated (Budiyono, 2011;Medagoda and Williams, 2012) and experimentally tested (Steenson et al, 2014) for UUVs with promising results. The major disadvantage of MPC is that if there is any modelling error or variation in model stability, then the performance is affected.…”

Section: Introductionmentioning

confidence: 99%

Composite model reference adaptive control for an unmanned underwater vehicle

Makavita¹,

Nguyen²,

Ranmuthugala³

et al. 2015

uw tech: int j soc uw tech

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The control of unmanned underwater vehicles (UUVs) is challenging due to the non-linear and time-varying nature of the hydrodynamic forces from the surrounding fluid. In addition, the presence of external disturbances makes the control even more difficult. Model reference adaptive control (MRAC) is an adaptive control technique that performs well in such situations, while the improved composite/combined model reference adaptive control (CMRAC) is capable of better transient performance. However, the latter is yet to be used in UUV controls. Thus, this paper tests the suitability of CMRAC in UUV applications using validated simulation models and compares its performance against the standard MRAC. Several test scenarios have been considered including initial operation, external disturbance and thruster failure. Simulation results show that CMRAC offers better tracking, faster disturbance rejection and quick recovery from thruster failure compared to MRAC. In addition, CMRAC is more robust against parameter uncertainties and thus the control signal shows fewer oscillations, which in turn reduces the probability of actuator damage.

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Model predictive control of a hybrid autonomous underwater vehicle with experimental verification

Cited by 38 publications

References 18 publications

Depth control for an over-actuated, hover-capable autonomous underwater vehicle with experimental verification

Depth control for an over-actuated, hover-capable autonomous underwater vehicle with experimental verification

Grid-based GA path planning with improved cost function for an over-actuated hover-capable AUV

Composite model reference adaptive control for an unmanned underwater vehicle

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