• K. Osman
  • A.N. Ahmad Sukri
  • S.F. Sulaiman
  • A.R. Azira
  • M.F. Faujan
Keywords: Pneumatic Actuator, Predictive Functional Controller


This paper discusses and analyzes the performances of an Intelligence Pneumatic Actuator (IPA) positioning system using Predictive Functional Controller (PFC) with Kalman filter Design. The uncertainties in the pneumatic system are undesirable. Kalman filter is useful in vast areas due to the prediction assets. The system models are designed based on previous research on the predictive controller and focused on position tracking.  The transfer function for the pneumatic actuator is obtained by using system identification (SI) techniques. The initial covariance values of the Kalman filter system are determined according to the plant system. The performances of the proposed system are performed in MATLAB, Simulink and validated with IPA plant. The validation process of the IPA plant is run through real-time experiments using National Instrument (NI) devices. The Result showed the system is stable with Kalman filter is new implementation on both simulations and experiment process.


Download data is not yet available.


[1] A.A.M. Faudzi, K. Suzumori, and S. Wakimoto, “Development of pneumatic actuated seating System to aid chair design,” in IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Montreal, Canada, 2010, pp. 1035–1040.

[2] K. Osman, M.F. Rahmat, and K. Suzumori, “Intelligent pneumatic assisted therapy on ankle rehabilitation,” in IEEE International Conference on Rehabilitation Robotics, Singapore, 2015, pp. 107–112.

[3] A.A.M. Faudzi, K. Suzumori, and S. Wakimoto, “Design and control of new intelligent pneumatic cylinder for intelligent chair tool application,” in IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Singapore, 2009, pp. 1909–1914.

[4] A.A.M. Faudzi, K. Osman, M.F. Rahmat, M.A. Azman, and K. Suzumori, “Controller design for simulation control of intelligent pneumatic actuators (IPA) system”, Procedia Engineering, vol. 41, pp. 593-599, 2012.

[5] M.F. Rahmat, N.H. Sunar, S.N.S. Salim, M.S.Z. Abidin, A.A.M. Faudzi, and Z.H. Ismail, “Review on modeling and controller design”, International Journal on Smart Sensing and Intelligent Systems, vol. 4, no. 4, pp. 630–661, 2011.

[6] L. Wang, Model Predictive Control System Design and Implementation Using MATLAB®. Melbourne: Springer Science & Business Media, 2009.

[7] R.E. Kalman, “A new approach to linear filtering and prediction problems”, Journal of Basic Engineering, vol. 82, no. 1, pp. 35-45, 1960.

[8] B. Feng, F. Mengyin, M. Hongbin, X. Yuangqing, and W. Bo, “Kalman filter with recursive covariance estimation-sequentially estimating process noise covariance”, IEEE Transactions on Industrial Electronics, vol. 61, no. 11, pp. 6253–6263, 2014.

[9] S. Gillijns, N. Haverbeke, B. De Moor, “Information, covariance and square-root filtering in the presence of unknown inputs”, in European Control Conference, Kos, Greece, 2007, pp. 2213-2217.

[10] T. Lacey. (1998). Tutorial: The Kalman Filter [Online]. Available:

[11] A.A.A. Emhemed, R. Mamat and A.A.M. Fauzi “A new predictive control technique for force control of pneumatic actuator plant”, in 10th Asian Control Conference, Kota Kinabalu, Malaysia, 2015, pp. 1-6.

[12] M.A.M. Yusoff and M. Sayahkarajy, “Experimental evaluation of a cylinder actuator control using mcKibben muscle”, International Journal of Integrated Engineering, vol. 11, no. 4, pp. 175-182, 2019.

[13] L.A. Zadeh, “From circuit theory to system theory”, Proceedings of the Institute of Radio Engineers, vol. 50, no. 5, pp. 856-865, 1962.

[14] M.S. Grewal, Kalman Filtering. Heidelberg, Germany: Springer Berlin Heidelberg, 2011.
How to Cite
Osman, K., Ahmad Sukri, A., Sulaiman, S., Azira, A., & Faujan, M. (2019). PREDICTIVE CONTROLLER WITH KALMAN FILTER FOR INTELLIGENCE PNEUMATIC ACTUATOR (IPA). Journal of Advanced Manufacturing Technology (JAMT), 13(2(2). Retrieved from