Online self-tuning fuzzy proportional—integral—derivative control for hydraulic load simulator

Hydraulic systems play an important role in modern industry owing to the fact that hydraulic actuator systems have many advantages over other technologies with electric motors, high durability, and the ability to produce large force at high speeds. Therefore, the hydraulic actuator has a wide range of application fields such as hydraulic pressing machines, moulding technology, etc. where controlled forces or pressures with high accuracy and fast response are the most significant demands. Consequently, many hybrid actuator models have been developed for research on how to control forces or pressures with the best results. The current paper presents a new kind of hydraulic load simulator for conducting performance and stability tests for control forces of hydraulic hybrid systems. In the dynamic loading process, disturbance makes the control performance (such as stability, frequency response, loading sensitivity, etc.) decrease or turn bad. In order to improve the control performance of a loading system and to eliminate or reduce the disturbance, an online self-tuning fuzzy proportional—integral—derivative (PID) controller is designed. Experiments are carried out to evaluate the effectiveness of the proposed control method applied for hydraulic systems with varied external disturbance as in real working conditions.

[1]  Dave Misir,et al.  Determination of the control gains of a fuzzy PID controller using neural networks , 1996, Proceedings of IEEE 5th International Fuzzy Systems.

[2]  F. Conrad,et al.  Design of Hydraulic Force Control Systems with State Estimate Feedback , 1987 .

[3]  Xiaodiao Huang,et al.  Simulation on a Fuzzy-PID Position Controller of the CNC Servo System , 2006, Sixth International Conference on Intelligent Systems Design and Applications.

[4]  Jiangjiang Wang,et al.  Application of Fuzzy-PID Controller in Heating Ventilating and Air-Conditioning System , 2006, 2006 International Conference on Mechatronics and Automation.

[5]  Chin-Wen Chuang,et al.  Apply fuzzy PID rule to PDA based control of position control of slider crank mechanisms , 2004, IEEE Conference on Cybernetics and Intelligent Systems, 2004..

[6]  Yu Yongquan,et al.  The dynamic fuzzy method to tune the weight factors of neural fuzzy PID controller , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[7]  Jixin Qian,et al.  Self-learning fuzzy PID controller based on neural networks , 1998, Proceedings of the 1998 American Control Conference. ACC (IEEE Cat. No.98CH36207).

[8]  Jian Zhang,et al.  A Dynamic Fuzzy Neural Networks Controller for Dynamic Load Simulator , 2006, 2006 International Conference on Machine Learning and Cybernetics.

[9]  A. Rubaai,et al.  dSPACE DSP-Based Rapid Prototyping of Fuzzy PID Controls for High Performance Brushless Servo Drives , 2006, Conference Record of the 2006 IEEE Industry Applications Conference Forty-First IAS Annual Meeting.

[10]  Li Yunhua,et al.  Development of Hybrid Control of Electrohydraulic Torque Load Simulator , 2002 .

[11]  Andrew G. Alleyne,et al.  On the limitations of force tracking control for hydraulic active suspensions , 1998, Proceedings of the 1998 American Control Conference. ACC (IEEE Cat. No.98CH36207).

[12]  Kyoung Kwan Ahn,et al.  Position Control of Shape Memory Alloy Actuators by Using Self Tuning Fuzzy PID Controller , 2006, 2006 1ST IEEE Conference on Industrial Electronics and Applications.