Improved Self-Tuning Fuzzy Proportional–Integral–Derivative Versus Fuzzy-Adaptive Proportional–Integral–Derivative for Speed Control of Nonlinear Hybrid Electric Vehicles

2016 ◽  
Vol 11 (6) ◽  
Author(s):  
Anil Kumar Yadav ◽  
Prerna Gaur
Keyword(s):  
Hybrid Electric Vehicle ◽  
Speed Control ◽  
Pid Controllers ◽  
Vehicle Speed ◽  
Fuzzy Pid ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Self Tuning ◽  
Hybrid Electric ◽  
Nonlinear Hybrid

The objective of this paper is to identify the suitable advance controller among optimized proportional–integral–derivative (O-PID), improved self-tuning fuzzy-PID (ISTF-PID), advanced fuzzy nonadaptive PID (AF-NA-PID), and AF-adaptive PID (AF-A-PID) controllers for speed control of nonlinear hybrid electric vehicle (HEV) system. The conventional PID (C-PID) controller cannot tackle the nonlinear systems effectively and gives a poor tracking and disturbance rejection performance. The performances of HEV with the proposed advance controllers are compared with existing C-PID, STF-PID, and conventional fuzzy PID (C-F-PID) controllers. The proposed controllers are designed to achieve the desired vehicle speed and rejection of disturbance due to road grade with reduced pollution and fuel economy.

Hardware-in-the-loop simulation for the analyzing of smart speed control in highly nonlinear hybrid electric vehicle

2018 ◽  
Vol 41 (2) ◽  
pp. 458-467 ◽  
Author(s):  
Mohammad Hassan Khooban
Keyword(s):  
Hybrid Electric Vehicle ◽  
Rapid Development ◽  
Test Procedure ◽  
Reference Signal ◽  
Speed Control ◽  
Hardware In The Loop ◽  
Intergovernmental Panel ◽  
Highly Nonlinear ◽  
Hybrid Electric ◽  
Nonlinear Hybrid

Owing to the severe limitations imposed by the Intergovernmental panel on climate change and the rapid development of the automobile industry, the utilize of energy storage units in vehicle systems has been increasingly attracting attention. Hence, this study proposes a new fuzzy Proportional Derivative + Integral (PD+I) controller based on a non-integer system for the robust speed control of highly nonlinear hybrid electric vehicles. In order to have an optimal and adaptive controller, the controller coefficients are tuned online by a novel optimization algorithm, which is called Adaptive Black Hole. In addition, the performance and robustness of the proposed method are tested by the experimental data, the Supplemental Federal Test Procedure (SFTP - US06). In order to prove the superiority and effectiveness of the suggested novel smart controller, a valid comparison is conducted between the results of the proposed method and recent studies on the same topic like the Model Predictive Control and the conventional online fuzzy PD+I (OFPD+I) controllers. Finally, extensive studies and hardware-in-the-loop simulations are presented to prove that the proposed controller can track a desired reference signal with lower deviation and show that the performance of suggested method is more robust in comparison with the prior-art controllers for all the case studies.

scholarly journals A Self-Tuning Proportional-Integral-Derivative-Based Temperature Control Method for Draw-Texturing-Yarn Machine

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Rong Song ◽  
Shuting Chen
Keyword(s):  
Parameter Identification ◽  
Temperature Control ◽  
Control Method ◽  
Fast Time ◽  
Fuzzy Pid ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Self Tuning ◽  
Set Up ◽  
Control Accuracy

Owing to the fast time-varying characteristics, the temperature control for draw-texturing-yarn (DTY) machine has higher technical difficulties and results in challenges for system energy optimization. To address the matter, a self-tuning proportional-integral-derivative- (ST-PID-) based temperature control method is proposed. Referring to the technical procedures of DTY machine, a thermodynamic model is set up. Then, a ST-PID minimum phase control system is constructed by the pole-point placement method. Subsequently, an artificial neural network based forgetting factor searching (ANN-FFS) algorithm is developed to optimize the system parameter identification. The numerical cases show that the proposed ANN-FFS algorithm can improve the parameter identification process, and the average identifying efficiency (K>15) can increase by more than 50%; compared with the fuzzy PID controller, the proposed ST-PID method can increase the control accuracy nearly 3 times for the static temperature ascending. The experimental results prove that the proposed ST-PID method has better abilities of characteristics tracing and anti-interference and can restrain the temperature fluctuation caused by objective switching and the factual control accuracy reaches 3 times that of fuzzy PID method.

scholarly journals Position Tracking of ML System using CSA Based PID Controller

2019 ◽  
Vol 8 (11) ◽  
pp. 1956-1959
Keyword(s):  
Pid Controller ◽  
Magnetic Levitation ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Pid Controllers ◽  
Position Tracking ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Simulation Results

The classical proportional integral derivative (PID) controllers are still use in various applications in industry. Magnetic levitation (ML) systems are rigidly nonlinear and sometimes unstable systems. Due to inbuilt nonlinearities of ML systems, tracking of position of ML Systems is still difficult. For the tracking purpose of position, PID controller parameters are found by choosing Cuckoo Search Algorithm (CSA) of optimization. The ranges of parameters are customized by z-n method of parameters. Simulation results show the tracking of position of ML systems using conventional and optimized parameters obtained with the CSA based controller.

Sign in / Sign up

Export Citation Format

Share Document