Neural network-based fuzzy inference system for speed control of heavy duty vehicles with electronic throttle control system

The objective of this study is to apply various control approaches to control the speed of a heavy duty vehicle using an electronic throttle control system. However, the DC servo motor is used for controlling the angular position of electronic throttle valve. Moreover, four control techniques are us...

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Bibliographic Details
Published in:Neural computing & applications 2017-12, Vol.28 (Suppl 1), p.907-916
Main Authors: Eski, İkbal, Yıldırım, Şahin
Format: Article
Language:English
Subjects:
Adaptive control
Angular position
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Computer simulation
Control systems
Controllers
D C motors
Data Mining and Knowledge Discovery
Fuzzy control
Fuzzy logic
Fuzzy systems
Image Processing and Computer Vision
Inference
Neural networks
Original Article
Probability and Statistics in Computer Science
Robust control
Robustness (mathematics)
Servocontrol
Servomechanisms
Servomotors
Speed control
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Summary:The objective of this study is to apply various control approaches to control the speed of a heavy duty vehicle using an electronic throttle control system. However, the DC servo motor is used for controlling the angular position of electronic throttle valve. Moreover, four control techniques are used to control prescribed two different random inputs of the heavy duty vehicle speed. These control structures are named as standard PID controller, model-based neural network controller, adaptive neural network-based fuzzy inference controller and proposed robust adaptive neural-based fuzzy inference control systems. On the other hand, the time performance specifications such as rise time, settling time, peak time, peak value and steady-state error are also examined for these control approaches. The results of the simulation for four approaches showed that the proposed robust adaptive neural network-based fuzzy inference control system has better performance rather than other standard control systems under varying speed conditions. Finally, the proposed control system structure will be implemented for speed control of DC servo motor.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-016-2362-0