Dynamic Tanker Steering Control Using Generalized Ellipsoidal-Basis-Function-Based Fuzzy Neural Networks

Wang, Weining; Er, Meng Joo; Han, Ming‐Yong

doi:10.1109/tfuzz.2014.2362144

Cited by 58 publications

(8 citation statements)

References 37 publications

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“…The combination of FLC and ANN, called fuzzy neural network (FNN), fuses the reasoning ability of FLC to handle uncertain information with the training capability of ANN to learn from the controlled process (Gaxiola et al, 2015). FNN has shown promising results as it adopts the advantages from both FLC and ANN (Wang et al, 2015;Kim and Chwa, 2015;Gaxiola et al, 2014).…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Novel Levenberg–Marquardt based learning algorithm for unmanned aerial vehicles

Sarabakha¹,

Imanberdiyev²,

Kayacan

et al. 2017

Information Sciences

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In this paper, a Levenberg-Marquardt inspired sliding mode control theory based adaptation laws are proposed to train an intelligent fuzzy neural network controller for a quadrotor aircraft. The proposed controller is used to control and stabilize a quadrotor unmanned aerial vehicle in the presence of periodic wind gust. A proportional-derivative controller is firstly introduced based on which fuzzy neural network is able to learn the quadrotor's control model on-line. The proposed design allows handling uncertainties and lack of modelling at a computationally inexpensive cost. The parameter update rules of the learning algorithms are derived based on a Levenberg-Marquardt inspired approach, and the proof of the stability of two proposed control laws are verified by using the Lyapunov stability theory. In order to evaluate the performance of the proposed controllers extensive simulations and real-time experiments are conducted. The 3D trajectory tracking problem for a quadrotor is considered in the presence of time-varying wind conditions.

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Section: Accepted Manuscriptmentioning

confidence: 99%

Novel Levenberg–Marquardt based learning algorithm for unmanned aerial vehicles

Sarabakha¹,

Imanberdiyev²,

Kayacan

et al. 2017

Information Sciences

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“…Thus, an important problem is to overcome the foregoing uncertainties. In recent years, some approaches have been proposed to overcome the restriction and a large amount of encouraging results have been reported [11][12][13][14][15][16][17][18][19][20][21][22][23][24]. When the considered systems are unknown, neural networks (NNs)/fuzzy logic systems are often used to approximate the uncertain function to construct a controller.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neural Control with Prespecified Tracking Accuracy for a Class of Switched Systems Subject to Input Delay

Zhang

et al. 2019

Complexity

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This paper is concerned with the adaptive tracking control design for a class of uncertain switched systems subject to input delay. Unlike the existing results on uncertain switched systems, the new proposed control scheme ensures that the tracking error converges to the accuracy given a priori according to the requirement. To achieve this aim, some nonnegative switching functions are introduced to replace the conventional Lyapunov function. In addition, neural networks are used to approximate the unknown simultaneous domination functions. By combining the backstepping technique and some common nonnegative switching functions, a stable adaptive neural controller is established. It can be shown that the closed-loop system is semiglobally uniformly ultimately bounded (SGUUB) and the tracking error satisfies the predefined accuracy. The effectiveness of the proposed control scheme is verified by a simulation example.

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“…In addition to NNs [12]- [14], a lot of efforts on adaptive approximation based tracking control have also been made via fuzzy systems (FS) [15]- [19], and fuzzy neural networks (FNN) [20]- [25], etc., and can roughly compensate unknown dynamics. Recently, a significant progress has been made by an innovative approximator termed self-constructing fuzzy neural network (SCFNN) [26]- [29] towards the dynamic-structure-approximation based adaptive control approaches [20] with much higher accuracy of both reconstruction and trajectory tracking. It should be highlighted that accurate tracking control can still hardly be achieved by the foregoing SCFNN-based control approaches since there still exist unexpected approximation residuals.…”

mentioning

confidence: 99%