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


NEURAL COMPUTING & APPLICATIONS, vol.28, 2017 (Journal Indexed in SCI) identifier

  • Publication Type: Article / Article
  • Volume: 28
  • Publication Date: 2017
  • Doi Number: 10.1007/s00521-016-2362-0
  • Keywords: Robust control, Neural network, ANFIS controller, Heavy duty vehicle, DC servo motor, Electronic throttle valve, CRUISE CONTROL, ROAD GRADE, DESIGN


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.