Recent Trends in Adaptive Control Applications

2017 ◽  
pp. 404-492
Author(s):  
V. V. Chalam
Keyword(s):  
Adaptive Control ◽  
Control Applications ◽  
Recent Trends

Data-Driven Retrospective Cost Adaptive Control for Flight Control Applications

2021 ◽  
pp. 1-27
Author(s):  
Syed Aseem Ul Islam ◽  
Tam W. Nguyen ◽  
Ilya V. Kolmanovsky ◽  
Dennis S. Bernstein
Keyword(s):  
Adaptive Control ◽  
Flight Control ◽  
Data Driven ◽  
Control Applications

scholarly journals Adaptive Control Using Fully Online Sequential-Extreme Learning Machine and a Case Study on Engine Air-Fuel Ratio Regulation

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Pak Kin Wong ◽  
Chi Man Vong ◽  
Xiang Hui Gao ◽  
Ka In Wong
Keyword(s):  
Adaptive Control ◽  
Extreme Learning Machine ◽  
Neural Control ◽  
Stochastic Gradient Descent ◽  
Initial Training ◽  
Node Number ◽  
Control Applications ◽  
Engine Model ◽  
Fuel Ratio ◽  
Learning Machine

Most adaptive neural control schemes are based on stochastic gradient-descent backpropagation (SGBP), which suffers from local minima problem. Although the recently proposed regularized online sequential-extreme learning machine (ReOS-ELM) can overcome this issue, it requires a batch of representative initial training data to construct a base model before online learning. The initial data is usually difficult to collect in adaptive control applications. Therefore, this paper proposes an improved version of ReOS-ELM, entitled fully online sequential-extreme learning machine (FOS-ELM). While retaining the advantages of ReOS-ELM, FOS-ELM discards the initial training phase, and hence becomes suitable for adaptive control applications. To demonstrate its effectiveness, FOS-ELM was applied to the adaptive control of engine air-fuel ratio based on a simulated engine model. Besides, controller parameters were also analyzed, in which it is found that large hidden node number with small regularization parameter leads to the best performance. A comparison among FOS-ELM and SGBP was also conducted. The result indicates that FOS-ELM achieves better tracking and convergence performance than SGBP, since FOS-ELM tends to learn the unknown engine model globally whereas SGBP tends to “forget” what it has learnt. This implies that FOS-ELM is more preferable for adaptive control applications.

Comparative Study on Control Methods for Air Conditioning of Industrial Paint Booths

2019 ◽  
Vol 11 (11) ◽  
pp. 1053-1059
Author(s):  
Murat Ayaz ◽  
Volkan Aygül ◽  
Ferhat Düzenli˙ ◽  
Erkutay Tasdemi˙rci˙
Keyword(s):  
Adaptive Control ◽  
Automotive Industry ◽  
Air Conditioning ◽  
Control Method ◽  
Ventilation System ◽  
Hvac Systems ◽  
Control Methods ◽  
Control Applications ◽  
Time Simulation ◽  
Simulation Results

It is of great importance that each product in industrial production facilities is to be produced in the same quality and standard. Especially in the automotive industry, the painting process needs to be done under certain environmental conditions according to the paint properties used. Therefore, the temperature, humidity and air quality values of the paint booth are very important for a quality painting operation. In this study, adaptive control has been proposed to control of one-zone heating-ventilation system for the paint booths. The system has been modelled by using the Matlab/Simulink. Performance of the proposed control method has been compared with conventional control methods such as On/Off, PID, fuzzy logic in terms of accuracy, efficiency and response time. Simulation results show that the proposed adaptive control is effective in the Heating, Ventilating, and Air Conditioning (HVAC) systems temperature control applications. In addition, energy efficiency in HVAC systems has been provided with the proposed control model. Furthermore, thermal analysis of the system has been done to corroborate simulation results.

Sign in / Sign up

Export Citation Format

Share Document