Position Control of a Pulse Width Modulated Pneumatic Systems: an Experimental Comparison

Document Type: Original Article

Authors

1 K.N. Toosi University of Technology

2 Islamic Azad University Science and Research Branch

Abstract

In this study, a new adaptive controller is proposed for position control of pneumatic systems. Difficulties associated with the mathematical model of the system in addition to the instability caused by Pulse Width Modulation (PWM) in the learning-based controllers using gradient descent, motivate the development of a new approach for PWM pneumatics. In this study, two modified Feedback Error Learning (FEL) methods are suggested and the their effectiveness are validated by experimental tracking data. The first one is a combination of PD (Proportional–Derivative) and RBF (Radial Basis Function) and in the second one RBF is replaced by ANFIS (Adaptive Neuro-Fuzzy Inference System). The robustness to varying mass is also examined. The experimental results show that the proposed algorithms, especially with ANFIS, are able to give good performance regardless of any uncertainties.

Keywords


N. Nguyen, J. Leavitt, F. Jabbari, G.E. Bobrow, “Accurate sliding-mode control of pneumatic system using low cost solenoid valve”, IEEE/ASME Transactions on Mechatronic, 2007, Vol. 12, No. 2.

G. Gransoik, J. Bornstein, “Minimizing air consumption of pneumatic actuators in mobile robots”, IEEE international conference on robotics and automation, 2004, pp 3634-3639.

Y. Can-Jun, N. Bin, Y. Chen, “Adaptive NeuroFuzzy control based development of a wearable exoskeleton leg for a human walking power augmentation”, Proceeding of IEEE/ASME, international conference on advanced intelligence mechatronics, 2006.

Y. Tsai, A. Huang, “Multiple-surface sliding controller design for pneumatic servo systems”, J. Mechatronis 18 (2008) pp 506-512.

E. Richer, Y. Hurmuzlu, “A high performance pneumatic force actuator system part 1- nonlinear mathematical model”, J. ASME dynamic system and measurement control, 2000, pp 416-425.

A. Paul, J. Mishra, M. Radke, “Reduced order sliding mode control for pneumatic actuator”, IEEE Transaction of control systems technology, 1994, Vol. 2, No. 3.

R. Verseveld, G. Bone, “Accurate position of a pneumatic actuator using on/off solenoid valves”, IEEE/ASME Transactions of Mechatronics, 1997, Vol. 2, No. 3.

A. Messina, N. Giannoccaro, A. Gentil, “Experimental and modeling the dynamics of pneumatic actuator controlled by pulse width modulation (PWM) Technique”, J. Mechatronic 15 (2005), pp 856-881.

F. Najafi, M. Fathi, M. Saadat, “Dynamic modeling of servo pneumatic actuators with cushioning”, Int J Adv Manuf Technol (2009) 42 pp 757-765.

X. Shen, J. Zhang, E. Bart, M. “Goldfarb, Nonlinear averaging applied to the control of pulse width modulated (PWM) pneumatic systems”, proceeding of a 2004 American control conference.

E. Barth, J. Zhang, M. Goldfarb, “Sliding mode approach to PWM-controlled pneumatic system”, proceeding of the American control conference, 2002.

F. Najafi, M. Fathi, M. Saadat, “Performance improvement of a PWM-sliding mode position controller used in pneumatic actuations”, Intelligent Automaton and Soft Computing, Vol. 15, No. X, pp.1-12, 2009, printed in USA.

RR. Yager, AL. Zadeh, “Fuzzy sets neural networks and soft computing”, Thomson learning, 1994.

H.K. Lee, G.S. Choi, G.H. Choi, “A study on tracking position control of pneumatic actuators”, J. Mechatronics 12 (2002), PP 813-831.

J. Jang, “ANFIS: Adaptive-Network-Based Fuzzy Inference System”, IEEE transaction on systems, man and cybernetics, 1993, VOL. 23, No.3.

H. Schulte, H. Hahn, “Fuzzy state feedback gain scheduling control of servo-pneumatic”, J. Engineering practice 12 (2004) pp 639-650.

M. Parnichkun, C. Ngaecharoenkul, “Kinematics control of a pneumatic system by Hybrid Fuzzy PID”, J. Mechatronics, 2001, VOL. 11 , pp 1001-1023.

Z. Zhao, M. Tomizuka, S. Isaka, “Fuzzy Gain Scheduling of PID controllers”, IEEE Transaction on systems, manufacturing and cybernetics, 1993, Vol. 23, pp 1392-1398.

Z. Situm, D. Pavkovic, B. Novakovic, “Servo pneumatic position control using fuzzy PID gain scheduling”, Transaction of the ASME, 2004, Vol. 126, pp 376-387.

S. Mohagheghi, G. Venayagamoorthy, R. Harley, “Fully Evolvable optimal Neurofuzzy Controller Using Adaptive Critic Designs”, IEEE Transaction on Fuzzy Systems, 2008, Vol. 16, No. 6.

S. Kaitwanidvilai, M. Parnichkun, “Force control in a pneumatic system using hybrid adaptive neuro-fuzzy model refrence control”, J. Mechatronics 15 (2005) pp 23-41.

S. Haykin, “Neural Network, a comprehensive foundation”, Prentice Hall, 1999. S. Shibata, M. Jindai, A. Shimizu, “Neuro-fuzzy control for pneumatic servo system”, 0-8703-6456- 2/00/ 2000. IEEE.

Q. Song, F. Liu, “Improved Fuzzy neural network control for a pneumatic system based on Extended Kalman Filter”, International conference on computational intelligence for modeling, control and automation. International conference on intelligent agent, web technology and internet commerce, IEEE, 2006.

K. Sabahi, M. Teshnelab, M. Aliyari Shoorehdeli, “Recurent fuzzy neural network by using feedback error learning approaches for LFC in interconnected power system”, J. Energy conversion and management 50 (2009) pp 936-945.

J. Nakanishi, S. Schaal, “Feedback error learning and nonlinear adaptive control”, J. neural networks 17 (2004), pp 1453-1465.

R. Adlgostar,Y. Kouhi, M. Teshnelab, M. Aliyari, “Flow control using a combination of robust and neurofuzzy controllers in feedback error learning framework”, 1-4244-0726-5/06, 2006, IEEE.

K. Kurosawa, R. Futami, N. Hoshimiya, “Joint angle control by FES using a feedback error learning controller”, IEEE Transaction of neural systems and rehabilitation engineering, 2005, Vol. 13, No.3.

A. Miyamura, H. Kimura, Stability of feedback error learning scheme, Systems and control letters 45 (2002) pp 303-316.

O. Nelles, “Nonlinear system identification, From Classical approaches to Neural Networks and Fuzzy Models”, Springer, 2001.

J. Mar, F. Lin, “An ANFIS controller for the car-following collision prevention system”, IEEE Transactions on vehicular technology, 2001, Vol. 50, No. 4.

K. Ogata, “Modern Control Engineering”, 4th. Ed. Prentice Hall, 2001.