Real Time Implementation of Type-2 Fuzzy Backstepping Sliding Mode Controller for Twin Rotor MIMO System (TRMS)

Benyettou, Loutfi; Zeghlache, Samir; Djerioui, Ali; Ghellab, Zinelaabidine

doi:10.18280/ts.360101

Cited by 10 publications

(7 citation statements)

References 19 publications

(29 reference statements)

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“…Henry et al [6] created an optimization model to minimize the vehicle delay at two-phase controlled intersections, which is the first optimization model for signal timing at intersections. Jiang et al applied fuzzy control to the signal timing at intersections, and designed a fuzzy signal controller, which mainly selects the fuzzy control rules from numerous rules [7]- [9]. Tan et al [10] proposed a timing rolling optimization algorithm to minimize the vehicle delay, paving the way to longterm global optimization of signal control parameters at intersections.…”

Section: Literature Reviewmentioning

confidence: 99%

Intersection Traffic Control Based on Multi-Objective Optimization

Mou

2020

IEEE Access

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Currently, most traffic control methods at intersections rely on the control of signal lights. However, most signal lights operate in the traditional fixed timing mode, which cannot adjust the timing based on the time-varying traffic flow. To solve the problem, this paper constructs a signal timing control model to optimize road capacity, delay time and the number of stops at the intersections, under the following constraints: cycle time, effective green light time and the maximum number of vehicles in each direction of intersection. To solve the model, the standard dragonfly algorithm (DA) was improved by a hybrid mutation operator, which ensures the diversity of solution set. The proposed model and algorithm were compared with the Webster model through simulations in an actual scene and on a virtual platform. The comparison fully proves the advantages of our model and algorithm.

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Section: Literature Reviewmentioning

confidence: 99%

Intersection Traffic Control Based on Multi-Objective Optimization

Mou

2020

IEEE Access

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“…Fuzzy inference is a reasoning method mimicking human thinking [11], [12]. A fuzzy inference system generally encompasses a reasoning rules base, a fuzzy inference engine, a fuzzifier and a defuzzifier as shown in Figure 3.…”

Section: B Fuzzy Inferencementioning

confidence: 99%

“…With the emergence of artificial intelligence (AI) techniques like fuzzy logic [11]- [13] and neural networks (NNs) [14], [15], more and more scholars have attempted to predict the VTV in ports by data-driven prediction methods (DDPMs) [16], [17]. The DDPMs can autonomously learn the nonlinear, dynamic changes in the historical data on the VTV in ports.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Vessel Traffic Volume in Ports Based on Improved Fuzzy Neural Network

Tian

Wang

2020

IEEE Access

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The accurate prediction of vessel traffic volume (VTV) is very helpful to rational scheduling of port resources and reducing vessel accidents. However, the traditional VTV prediction methods face problems like the overfitting of historical data and prediction inaccuracy. To solve these problems, this paper improved the fuzzy neural network (FNN) with quantum genetic algorithm (QGA). Firstly, the basic principles of neural network (NN), fuzzy inference and the QGA were introduced in turn. Then, the weights of the FNN were optimized by the QGA. On this basis, the authors established a VTV prediction model based on the improved FNN. Finally, the established model was applied to predict the VTV in an actual port of China, in comparison with several classic NNs. The VTV data were collected based on the length and gross tonnage of vessels. The results show that the improved FNN outperformed the contrastive methods in the accuracy of VTV prediction, thanks to the optimization by the QGA. The proposed method enjoys a great application potential in the prediction of the VTV in ports. INDEX TERMS Vessel traffic volume (VTV), prediction model, fuzzy neural network (FNN), quantum genetic algorithm (QGA).

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“…With a variable structure, the sliding mode controller (SMC) is a suitable nonlinear controller for inverter structure and generator [14,15]. This controller is more robust and reliable than the traditional linear controllers [16] and adapts well to changes in system parameters. Compared with traditional linear SMC, the terminal SMC converges fast and applies to an environment with complex interference.…”

Section: Introductionmentioning

confidence: 99%

A Terminal Sliding Mode Controller for Electric Spring

Zhang

2020

Teh. vjesn.

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This paper mainly designs a terminal sliding mode controller (SMC) that can optimize the dynamic and static performance of the electric spring (ES) under the complex load environment on the user side, and improve the anti-interference ability of the ES. Firstly, the user-side ES-containing supply and distribution network (SDN) was modelled and analyzed, and then, the sliding surface and control rate was designed. Next, a reference voltage generation method was introduced, the stabilization conditions of the ES-containing SDN were identified, and the principles of parameter setting for the SMC were expounded. Finally, the proposed terminal SMC was proved feasible through simulations and dSPACE-based physical experiments and found to outperform the linear SMC in adaptability and dynamic and static performance.

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Real Time Implementation of Type-2 Fuzzy Backstepping Sliding Mode Controller for Twin Rotor MIMO System (TRMS)

Cited by 10 publications

References 19 publications

Intersection Traffic Control Based on Multi-Objective Optimization

Intersection Traffic Control Based on Multi-Objective Optimization

Prediction of Vessel Traffic Volume in Ports Based on Improved Fuzzy Neural Network

A Terminal Sliding Mode Controller for Electric Spring

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