Fuzzy Controller for Autonomous Vehicle Based on Rough Sets

Xue, Hongtao; Zhang, Ziming; Wu, Mian; Chen, Peng

doi:10.1109/access.2019.2946663

Cited by 20 publications

(13 citation statements)

References 41 publications

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“…Researchers [ 26 , 27 ] proposed trajectory tracking control of a four-wheeled omnidirectional mobile robot based on the reference model approach.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy Logic for Intelligent Control System Using Soft Computing Applications

Dumitrescu

Ciotîrnae

Vizitiu

2021

Sensors

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When considering the concept of distributed intelligent control, three types of components can be defined: (i) fuzzy sensors which provide a representation of measurements as fuzzy subsets, (ii) fuzzy actuators which can operate in the real world based on the fuzzy subsets they receive, and, (iii) the fuzzy components of the inference. As a result, these elements generate new fuzzy subsets from the fuzzy elements that were previously used. The purpose of this article is to define the elements of an interoperable technology Fuzzy Applied Cell Control-soft computing language for the development of fuzzy components with distributed intelligence implemented on the DSP target. The cells in the network are configured using the operations of symbolic fusion, symbolic inference and fuzzy–real symbolic transformation, which are based on the concepts of fuzzy meaning and fuzzy description. The two applications presented in the article, Agent-based modeling and fuzzy logic for simulating pedestrian crowds in panic decision-making situations and Fuzzy controller for mobile robot, are both timely. The increasing occurrence of panic moments during mass events prompted the investigation of the impact of panic on crowd dynamics and the simulation of pedestrian flows in panic situations. Based on the research presented in the article, we propose a Fuzzy controller-based system for determining pedestrian flows and calculating the shortest evacuation distance in panic situations. Fuzzy logic, one of the representation techniques in artificial intelligence, is a well-known method in soft computing that allows the treatment of strong constraints caused by the inaccuracy of the data obtained from the robot’s sensors. Based on this motivation, the second application proposed in the article creates an intelligent control technique based on Fuzzy Logic Control (FLC), a feature of intelligent control systems that can be used as an alternative to traditional control techniques for mobile robots. This method allows you to simulate the experience of a human expert. The benefits of using a network of fuzzy components are not limited to those provided distributed systems. Fuzzy cells are simple to configure while also providing high-level functions such as mergers and decision-making processes.

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“…Researchers [ 26 , 27 ] proposed trajectory tracking control of a four-wheeled omnidirectional mobile robot based on the reference model approach.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy Logic for Intelligent Control System Using Soft Computing Applications

Dumitrescu

Ciotîrnae

Vizitiu

2021

Sensors

View full text Add to dashboard Cite

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“…Secondly, in terms of the input scaling factors Kω and Kdω, they are of fixed magnitude. Usually, to ensure In1 and In2 to stay between −1 and 1 [40], Kω is set as the reciprocal of the maximum speed value ωr_max, that is, As for the traditional controller, there are several features that need to be mentioned. First, the fuzzy controller contains five parts, namely, fuzzification, membership function, fuzzy control rules, defuzzification and adjustment.…”

Section: Traditional Fuzzy Adaptive Pi Controllermentioning

confidence: 99%

Section: Traditional Fuzzy Adaptive Pi Controllermentioning

confidence: 99%

Ripple Attenuation for Induction Motor Finite Control Set Model Predictive Torque Control Using Novel Fuzzy Adaptive Techniques

et al. 2021

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Finite control set model predictive torque control (FCS-MPTC) strategy has been widely used in induction motor (IM) control due to its fast response characteristic. Although the dynamics of the FCS-MPTC method are highly commended, its steady-state performance—ripple deserves attention in the meantime. To improve the steady-state performance of the IM drives, this paper proposes an improved FCS-MPTC strategy, based on a novel fuzzy adaptive speed controller and an adaptive weighting factor, tuning strategy to reduce the speed, torque and flux ripples caused by different factors. Firstly, a discrete predicting plant model (PPM) with a new flux observer is established, laying the ground for achieving an FCS-MPTC algorithm accurately. Secondly, after analyzing the essential factors in establishing a fuzzy adaptive PI controller, with high ripple suppression capacity, an improved three-dimensional controller is designed. Simultaneously, the implementation procedures of the fuzzy adaptive PI controller-based FCS-MPTC are presented. Considering that a weighting factor must be employed in the cost function of an FCS-MPTC method, system ripples increase if the value of the weighting factor is inappropriate. Then, on that basis, a novel fuzzy adaptive theory-based weighting factor tuning strategy is proposed, with the real-time torque and flux performance balanced. Finally, both simulation and hardware-in-loop (HIL) test are conducted on a 1.1 kW IM drive to verify the proposed ripple reduction algorithms.

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“…The ANFIS has several advantages such as robust performance, ability to capture the nonlinear structure of a process, adaptation capability, and fast learning capacity. The ANFIS has been successfully implemented for fault detection, function approximation, time series forecasting, control, and nonlinear processes modelling [23]- [27].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Method Based on Optimized Neuro-Fuzzy System and Effective Features for Fault Location in VSC-HVDC Systems

Rohani

Koochaki

2020

IEEE Access

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One of the most important issues with HVDC systems are the occurrence of various faults that can lead to considerable electrical power losses, serious damage to expensive equipment and huge financial losses. Hence, it is highly required to design an accurate and automatic fault location method in HVDC systems for maintaining uninterrupted supply of energy and protecting sensitive equipment such as rectifiers and inverters. Accordingly, this paper proposes a new hybrid system based on adaptive neuro-fuzzy inference system (ANFIS) with optimal parameters and Hilbert-Huang (HH) transform for fault location in voltage sourced converter-HVDC (VSC-HVDC) systems. The proposed fault location method consists of three major sections. In the first section, HH transform is applied to extract new features from current signal. In the second part, ANFIS uses the extracted features to estimate the fault location in transmission lines. Learning algorithm determines the accuracy and efficiency of each machine-learning algorithm. In the third section of the developed system, enhanced version of particle swarm optimization (PSO) algorithm named chaotic dynamic weight PSO (CDWPSO) algorithm is implemented as learning algorithm to train the ANFIS. The developed fault detection and location system was tested on a VSC-HVDC system with 250 km length and the obtained results using MATLAB simulations have shown that combination of new features, and CDWPSO-based ANFIS has high accuracy in fault detection and location in VSC-HVDC systems. High fault location accuracy, robust performance of neuro-fuzzy system, optimal training of ANFIS, extraction of novel effective features from current signal and fault location only with six features are the main contribution of the developed system.

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Fuzzy Controller for Autonomous Vehicle Based on Rough Sets

Cited by 20 publications

References 41 publications

Fuzzy Logic for Intelligent Control System Using Soft Computing Applications

Fuzzy Logic for Intelligent Control System Using Soft Computing Applications

Ripple Attenuation for Induction Motor Finite Control Set Model Predictive Torque Control Using Novel Fuzzy Adaptive Techniques

A Hybrid Method Based on Optimized Neuro-Fuzzy System and Effective Features for Fault Location in VSC-HVDC Systems

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