Adaptive critic based neurocontroller for autolanding of aircraft with varying glideslopes

Saini, Gurinder; Balakrishnan, S. N.

doi:10.1109/icnn.1997.614409

Cited by 18 publications

(17 citation statements)

References 5 publications

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“…The DHP method has been successfully implemented to prevent cars from skidding when driving over unexpected patches of ice [36]. DHP-based controllers have been developed for missile interception [37] and for aircraft auto-landing [38]. Recently, adaptive critic designs have been used to replace the automatic voltage generator and the turbine governor of a turbogenerator connected to the power grid [24].…”

Section: Discussionmentioning

confidence: 99%

ModelBased Adaptive Critic Designs

Barto

Powell

et al. 2009

Handbook of Learning and Approximate Dynamic Programming

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Section: Discussionmentioning

confidence: 99%

ModelBased Adaptive Critic Designs

Barto

Powell

et al. 2009

Handbook of Learning and Approximate Dynamic Programming

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“…Adaptive critic designs constitute a class of approximate dynamic programming (ADP) methods that use incremental optimization combined with parametric structures that approximate the optimal cost and the control [36][37][38]. Both classical DP and ADP methods have been used to train neural networks for many nonlinear control applications, such as steering and controlling the speed of a two-axle vehicle [39], intercepting an agile missile [40], performing auto landing and control of an aircraft [41][42][43], controlling a turbogenerator [44], and tracking control with time delays [45]. As for GCC controllers, neural networks have been primarily used to generate external reference signals.…”

Section: Fig 1 Application Of Grid-connected Rectifier/inverter In mentioning

confidence: 99%

Artificial Neural Networks for Control of a Grid-Connected Rectifier/Inverter Under Disturbance, Dynamic and Power Converter Switching Conditions

Fairbank

Johnson

et al. 2014

IEEE Trans. Neural Netw. Learning Syst.

132

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link City Research OnlineAbstract --Three-phase grid-connected converters are widely used in renewable and electric power system applications. Traditionally, grid-connected converters are controlled with standard decoupled d-q vector control mechanisms. However, recent studies indicate that such mechanisms show limitations in their applicability to dynamic systems. This paper investigates how to mitigate such restrictions using a neural network to control a grid-connected rectifier/inverter. The neural network implements a dynamic programming algorithm and is trained by using backpropagation through time. To enhance performance and stability under disturbance, additional strategies are adopted, including the use of integrals of error signals to the network inputs and the introduction of grid disturbance voltage to the outputs of a well-trained network. The performance of the neural network controller is studied under typical vector control conditions and compared against conventional vector control methods, which demonstrates that the neural vector control strategy proposed in this paper is effective. Even in dynamic and power converter switching environments, the neural vector controller shows strong ability to trace rapidly changing reference commands, tolerate system disturbances, and satisfy control requirements for a faulted power system.

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“…Adaptive critic designs constitute a class of approximate dynamic programming (ADP) methods that use incremental optimization combined with parametric structures that approximate the optimal cost and the control [22,23]. Both classical DP and ADP methods have been used to train neural networks for a large number of nonlinear control applications, such as steering and controlling the speed of a two-axle vehicle [24], intercepting an agile missile [25], performing auto landing and control of an aircraft [26][27][28], and controlling a turbogenerator [29]. However, no research has been conducted regarding the vector control of grid-connected power electronic converters using DP or ADP-based neural networks.…”

mentioning

confidence: 99%

Vector control of a grid-connected rectifier/inverter using an artificial neural network

Wunsch

Fairbank

et al. 2012

The 2012 International Joint Conference on Neural Networks (IJCNN)

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This is the accepted version of the paper.This version of the publication may differ from the final published version. This paper investigates how to mitigate such problems using a neural network to control a grid-connected rectifier/inverter. The neural network implements a dynamic programming (DP) algorithm and is trained using backpropagation through time. Permanent repository linkThe performance of the DP-based neural controller is studied for typical vector control conditions and compared with conventional vector control methods. The paper also investigates how varying grid and power converter system parameters may affect the performance and stability of the neural control system. Future research issues regarding the control of grid-connected converters using DP-based neural networks are analyzed. Index Terms -grid-connected rectifier/inverter, decoupled vector control, renewable energy conversion systems, neural controller, dynamic programming, backpropagation through time I. INTRODUCTIONN renewable and electric power system applications, a three-phase grid-connected dc/ac voltage-source PWM converter is usually employed to interface between the dc and ac systems. Typical converter configurations containing the grid-connected converter (GCC) include: 1) a dc/dc/ac converter for solar, battery and fuel cell applications [1,2], 2) a dc/ac converter for STATCOM applications [3,4], and 3) an ac/dc/ac converter for wind power and HVDC applications [4][5][6][7][8]. Figure 1 demonstrates the grid-connected dc/ac converter used in a microgrid to connect distributed energy resources. Conventionally, this type of converters is controlled using the standard decoupled d-q vector control approach [5][6][7][8].Notwithstanding its merits, recent studies indicate that the conventional vector control strategy is inherently limited [9,10], particularly when facing uncertainties [11]. For instance,

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Adaptive critic based neurocontroller for autolanding of aircraft with varying glideslopes

Cited by 18 publications

References 5 publications

ModelBased Adaptive Critic Designs

ModelBased Adaptive Critic Designs

Artificial Neural Networks for Control of a Grid-Connected Rectifier/Inverter Under Disturbance, Dynamic and Power Converter Switching Conditions

Vector control of a grid-connected rectifier/inverter using an artificial neural network

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