An online genetic algorithm based model predictive control autopilot design with experimental verification

Naeem, Wasif; Sutton, Robert I.; Chudley, J.; Dalgleish, Fraser; Tetlow, S.

doi:10.1080/00207170500228483

Cited by 32 publications

(19 citation statements)

References 7 publications

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“…Controlling underactuated vehicles is especially challenging because they are not fully feedback linearizable and show nonholonomic constraints, so standard tools to control nonlinear systems-such as feedback linearization and integrator backstepping-may cause poor performance (Aguiar and Hespanha 2003). Currently, the applied control schemes vary from rudimentary proportional-derivative designs (Fossen 1994) over fuzzy control (Vaneck 1997;Gyoungwoo et al 2009), linear quadratic Gaussian control (Naeem et al 2008) to nonlinear control theories such as sliding-mode (Ashrafiuon et al 2008) and model predictive control (MPC) (Naeem et al 2005).…”

Section: Introductionmentioning

confidence: 97%

Experimental test of a robust formation controller for marine unmanned surface vessels

et al. 2009

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Experiments with two formation controllers for marine unmanned surface vessels are reported. The formation controllers are designed using the nonlinear robust model-based sliding mode approach. The marine vehicles can operate in arbitrary formation configurations by using two leader-follower control schemes. For the design of these controller schemes 3 degrees of freedom (DOFs) of surge, sway, and yaw are assumed in the planar motion of the marine surface vessels. Each vessel only has two actuators; therefore, the vessels are underactuated and the lack of a kinematic constraint puts them into the holonomic system category. In this work, the position of a control point on the vessel is controlled, and the orientation dynamics is not directly controlled. Therefore, there is a potential for an oscillatory yaw motion to occur. It is shown that the orientation dynamics, as the internal dynamics of this underactuated system, is stable, i.e., the follower vehicle does not oscillate about its control point during the formation maneuvers. The proposed formation controller relies only on the state information obtained from the immediate neighbors of the vessel and the vessel itself. The effectiveness and robustness of formation control laws in the presence of parameter uncertainty and environmental disturbances are demonstrated by using both simulations and field experiments. The experiments were performed in a natural environment on a lake using a small test boat, and show robust performance to parameter uncertainty and disturbance.the first experimental verification of the above mentioned approach, whose unique features are the use of a control point, the zero-dynamic stability analysis, the use of leaderfollower method and a nonlinear robust control approach.

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Section: Introductionmentioning

confidence: 97%

Experimental test of a robust formation controller for marine unmanned surface vessels

et al. 2009

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“…H p is the prediction horizon or output horizon, and H c the control horizon. More details can be found in Naeem et al (2005). For completeness, the general structure of an MPC is shown in Figure 4(a).…”

Section: A Ro Bu S T Nav I G At I O N T E C H N I Qu E F O R G U I Damentioning

confidence: 99%

“…The concepts and techniques of MPC have been developed over the past three decades (Annamalai, 2012), and various authors such as Maciejowski (2002), Rawlings andMayne (2009), Wang (2009) and Allgower et al (2010) suggest that MPC is widely used in the process and petrochemical industries. In addition, the marine control system design fraternity has also embraced this approach since it offers the advantage of being capable of enforcing various types of constraints on the plant process as exemplified by Naeem et al (2005), Perez (2005), Oh and Sun (2005), Liu et al, (2011) and Li and Sun (2012). In general, the plant output is predicted by using a model of the plant to be controlled.…”

Section: A Ro Bu S T Nav I G At I O N T E C H N I Qu E F O R G U I Damentioning

confidence: 99%

A Robust Navigation Technique for Integration in the Guidance and Control of an Uninhabited Surface Vehicle

Motwani¹,

Sharma²,

Sutton³

et al. 2015

J. Navigation

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This paper proposes the novel use of a weighted Interval Kalman Filter (wIKF) in a robust navigational approach for integration with the guidance and control systems of an uninhabited surface vehicle named Springer. The waypoint tracking capability of this technique is compared with that of one that uses a conventional Kalman Filter (KF) navigational design, when the model of the sensing equipment used by the filter is incorrect. In this case, the KF fails to predict correctly the vehicle's heading, which consequently impacts negatively on the performance of its integrated navigation, guidance and control (NGC). However, the use of a wIKF technique that is immune to this kind of erroneous modelling endows the integrated NGC system with better accuracy and efficiency in completing a mission. K E Y WO R D S 1. Navigation, guidance and control.2. Interval Kalman filtering. 3. Robust estimation 4. Uninhabited surface vehicle

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“…These chromosomes are the encoded representations of all the parameters of the solution. Each chromosomes is compared to other chromosomes in the population and awarded fitness rating that indicates how successful this chromosomes to the latter [7][8][9][10][11][12][13]. There are three main stages of a genetic algorithm, these are known as reproduction, crossover and mutation [8].…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

Voltage Stability for a Photovoltaic System Connected to Grid by Using Genetic Algorithm Technique

Mostafa¹,

Bahgaat²,

sayed³

et al. 2017

IJGDC

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An online genetic algorithm based model predictive control autopilot design with experimental verification

Cited by 32 publications

References 7 publications

Experimental test of a robust formation controller for marine unmanned surface vessels

Experimental test of a robust formation controller for marine unmanned surface vessels

A Robust Navigation Technique for Integration in the Guidance and Control of an Uninhabited Surface Vehicle

Voltage Stability for a Photovoltaic System Connected to Grid by Using Genetic Algorithm Technique

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