scholarly journals Neuro-PID Control for Electric Vehicle

2011 ◽  
Vol 15 (7) ◽  
pp. 846-853 ◽  
Author(s):  
Shigeru Omatu ◽  
◽  
Michifumi Yoshioka ◽  
Toru Fujinaka ◽  
◽  
...  
Keyword(s):  
Electric Vehicle ◽  
Pid Control ◽  
Control Method ◽  
Torque Control ◽  
Learning Ability ◽  
Control Problems ◽  
Process Industries ◽  
The Neural Network ◽  
Control Scheme ◽  
Self Tuning

In this paper we consider the neuro-control method and its application to control problems of an electric vehicle. The neuro-control methods adopted here is based on Proportional-plus-Integral-plus-Derivative (PID) control, which has been adopted to solve process control or intelligent control problems. In Japan about eighty four percent of the process industries have used the PID control. After deriving the self-tuning PID control scheme (neuro-PID) using the learning ability of the neural network, we will show the control results by using the speed and torque control of an electric vehicle.

Simulink Implementation of Direct Torque Control Drive for Induction Machines

2011 ◽  
Vol 367 ◽  
pp. 89-96
Author(s):  
U. Zangina ◽  
H.N. Yahaya ◽  
M. Aminu ◽  
Z.O. Niyi
Keyword(s):  
Control Method ◽  
Direct Torque Control ◽  
Induction Machine ◽  
Induction Machines ◽  
Torque Control ◽  
Control Technique ◽  
Computational Time ◽  
Suitable Alternative ◽  
Control Scheme ◽  
Transient Operations

Direct Torque Control (DTC) has emerged over the last two decades as a suitable alternative to the well-known Field Oriented Control (FOC) or vector control technique for electric drives mainly due to its simple control scheme, low computational time and reduced parameter sensitivity. In this paper, speed control of an induction machine based on DTC strategy has been developed and a comprehensive study is presented. The performance of the control method has been demonstrated by simulations using the Matlab/Simulink software package. Several numerical simulations have been carried out in steady state and transient operations.

Application of Active Mount Engine Vibration Isolation to Fuzzy Control Systems

2013 ◽  
Vol 341-342 ◽  
pp. 1023-1027
Author(s):  
Hui Guo ◽  
Guo Chun Sun ◽  
Min Fan
Keyword(s):  
Control Systems ◽  
Pid Control ◽  
Vibration Isolation ◽  
The Body ◽  
Fuzzy Pid Control ◽  
Power Train ◽  
Engine Vibration ◽  
Control Scheme ◽  
Self Tuning ◽  
Piezoelectric Stack Actuator

An automobile power-train active mount system with a piezoelectric stack actuator is introduced. The influence caused by power-train to the body of the car is analyzed by means of parameter self-tuning fuzzy PID control, on which the simulating results are based. It turns out that this control scheme can restrain the influence better caused by power-train to the body of the car.

Performance Enhancement of Pneumatic Vibration Isolators Using Self-Tuning PID Feedback and Time-Delay-Control (TDC)-Based Feedforward Scheme

2018 ◽  
Author(s):  
Jin-Wei Liang ◽  
Hung-Yi Chen ◽  
Lyu-Cyuan Zeng
Keyword(s):  
Mathematical Model ◽  
Performance Enhancement ◽  
Control Method ◽  
Hybrid Control ◽  
Model Performance ◽  
Experimental Investigations ◽  
Isolation System ◽  
Control Scheme ◽  
Self Tuning ◽  
Hybrid Control Scheme

A hybrid control scheme that combines a self-tuning PID-feedback loop and TDC-based feedforward scheme is proposed in this study to cope with an active pneumatic vibration isolator. In order to establish an effective TDC feedforward control a reliable mathematical model of the pneumatic isolator is required and developed firstly. Numerical and experimental investigations on the validity of the mathematical model are performed. It is found that although slight discrepancy exists between predicted and observed behaviors of the system, the overall model performance is acceptable. The resultant model is then applied in the design of the TDC feedforward scheme. A neuro-based adaptive PID control is integrated with the TDC feedforward algorithm to form the hybrid control. Numerical and experimental isolation tests are carried out to examine the suppression performances of the proposed hybrid control scheme. The results show that the proposed hybrid control method outperforms solely TDC feedforward while the latter outperforms the passive isolation system. Moreover, the proposed hybrid control scheme can suppress the vibration near the system’s resonance.

Computed Torque Control of a Caterpillar Robot Manipulator Using Neural Network

2016 ◽  
Vol 15 ◽  
pp. 106-118 ◽  
Author(s):  
Mehran Rahmani ◽  
Ahmad Ghanbari
Keyword(s):  
Neural Network ◽  
Trajectory Tracking ◽  
Control Method ◽  
Robot Manipulator ◽  
Setting Time ◽  
Torque Control ◽  
Learning Ability ◽  
Control Effort ◽  
Better Than ◽  
Computed Torque

This paper presents a neural computed torque controller, which employs to a Caterpillar robot manipulator. A description to exert a control method application neural network for nonlinear PD computed torque controller to a two sub-mechanisms Caterpillar robot manipulator. A nonlinear PD computed torque controller is obtained via utilizing a popular computed torque controller and using neural networks. The proposed controller has some advantages such as low control effort, high trajectory tracking and learning ability. The joint angles of two sub-mechanisms have been obtained by using the numerical simulations. The discovered figures show that the performance of the neural computed torque controller is better than a conventional computed torque controller in trajectory tracking and reduction of setting time. Finally, snapshots of gain sequences are demonstrated.

Design of Neural Network Controller for Space Robot with Flexible Manipulators

2013 ◽  
Vol 394 ◽  
pp. 393-397
Author(s):  
Jing Ma ◽  
Wen Hui Zhang ◽  
Zhi Hua Zhu
Keyword(s):  
Neural Network ◽  
Pid Control ◽  
Control Method ◽  
System Optimization ◽  
Space Robot ◽  
Learning Ability ◽  
Flexible Manipulators ◽  
Neural Network Controller ◽  
Non Linear System ◽  
Self Learning

Neural network self-learning optimization PID control algorithm is put forward for free-floating space robot with flexible manipulators. Firstly, dynamics model of space flexible robot is established, then, neural network with good learning ability is used to approach non-linear system. Optimization algorithm of network weights is designed to speed up the learning speed and the adjustment velocity. Error function is offered by PID controller. The neural network self-learning PID control method can improve the control precision.

Direct Torque Control of Interior Permanent Magnet Synchronous Motors Based on Position Sensorless Control

2013 ◽  
Vol 756-759 ◽  
pp. 627-631
Author(s):  
Zhao Jun Meng ◽  
Rui Chen ◽  
Yue Jun An
Keyword(s):  
Permanent Magnet ◽  
Electric Vehicle ◽  
Control Method ◽  
Sensorless Control ◽  
Permanent Magnet Synchronous Motor ◽  
Direct Torque Control ◽  
Torque Control ◽  
Permanent Magnet Synchronous Motors ◽  
Interior Permanent Magnet ◽  
Position Sensorless

The position sensorless control method based on direct torque control was carried out aiming at the interior permanent magnet synchronous motor (IPMSM) in this paper. To the consideration of electric vehicle space is limited, in order to reduce the controller size to save space, this paper studied the sensorless control. Meanwhile, in order to improve the control rapidity as much as possible of the electric vehicle, take direct torque control as a control method of the driving motor. Finally, designed the sensorless direct torque controller and studied its simulation. Simulation results show that the control system have good dynamic and static characteristics in the full speed range.

Smith Prediction Based on Genetic Algorithm Self-Tuning PID Control

2014 ◽  
Vol 602-605 ◽  
pp. 1186-1189
Author(s):  
Dong Sheng Wu ◽  
Qing Yang
Keyword(s):  
Genetic Algorithm ◽  
Time Delay ◽  
Pid Control ◽  
Pid Controller ◽  
Control Method ◽  
Delay System ◽  
Time Delay System ◽  
Self Tuning ◽  
On Line ◽  
Turning Control

Aiming at the phenomena of big time delay are normally existing in industry control, this paper proposes an intelligent GA-Smith-PID control method based on genetic algorithm and Smith predictive compensation algorithm and traditional PID controller. This method uses the ability of on line-study, a self-turning control strategy of GA, and better control of Smith predictive compensation to deal with the big time delay. This method overcomes the limitation of traditional PID control effectively, and improves the system’s robustness and self-adaptability, and gets satisfactory control to deal with the big time delay system.

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