Siti Khadijah Badar Sharif, Musa Mailah


For precise application, it is imperative to provide accurate and stable performance. The feed flow rate of a syringe fluid dispensing system is regulated through a Proportional- Integral-Derivative (PID) and Active Force Control (AFC) control scheme that was actuated using a DC servo motor considering a real-time implementation. The focus of this study is to control the speed of a DC motor by implementing an AFC strategy in rejecting the disturbance in the system. The AFC is implemented by cascading its control loop with the outer PID controller loop to form a two degree-of-freedom (DOF) controller. The performance of the proposed PID with AFC control scheme was investigated considering both the theoretical simulation and experimental works. The simulation was performed in MATLAB/Simulink computing platform while the real-time experimentation was done by utilising the Arduino MEGA 2560 microcontroller with MATLAB/Simulink driver for the data acquisition, interface and control implementation. The results implies the robustness of the AFC-based system in controlling the feed flow rate of the fluid in the dispenser. The best performance is obtained for 100% AFC with the disturbance due to vibration almost completely compensated via the proposed scheme in comparison to the PID counterpart.


Proportional-integral-derivative control, active force control, real-time implementation, DC motor, ball screw mechanism, syringe fluid dispenser

Full Text:



Li, Z., S. Y. Mak, A. Sauret and C. . Ho. 2014. Syringe-Pump-Induced Fluctuation In All-Aqueoes Microfluidic System Implications For Flow Rate Accuracy. Lab Chip. 14(4): 744-749.

Zeng, W., I. Jacobi, D. J. Beck, S. Li and H. A. Stone. 2015. Characterization Of Syringe-Pump-Driven Induced Pressure Fluctuations In Elastic Microchannels. Lab Chip. 15(4): 1110-1115.

Pitowarno, E., M. Mailah and H. Jamaluddin. 2002. Knowledge-Based Trajectory Error Pattern Method Applied to An Active Force Control Scheme. International Journal of Engineering and Technology. 2(1): 1-15.

S. K. Sar, L. Dewan. 2014. MRAC Based PI Controller for Speed Control of D.C. Motor Using Lab View. WSEAS Transactions on Systems and Control. 9: 10-15.

Ali, A. T., E. Bashier, M. Tayeb and O. B. Mohd. Adaptive PID Controller for DC Motor Speed Control. 2012. International Journal of Engineering Inventions. 1(5): 26-30.

Asseni, A., A. Albagul and O. Jomah. 2009. Adaptive Controller Design for DC Drive System Using Gradient Technique. Procs. of 2nd Intl. Conf. on Maritime and Naval Science and Engineering. 125-128.

Akar, M. and I. Temiz. 2007. Motion Controller Design For The Speed Control Of DC Motor. International Journal of Applied Mathematics and Informatics. 4(1): 131-137.

Yang, Y., Y. Chen, H. Sun and J. Huang. 2014. The Rotation Speed Control of DC motor based on Fuzzy-PI Dual Mode. Applied Mechanics and Materials. 454: 34-38.

Kang, Y. H. and L. K. Kim. 2001. Design of Neuro-Fuzzy Controller For The Speed Control Of A DC Motor. Electrical Machines and Systems, 2001. ICEMS 2001. Proceedings of the IEEE Fifth International Conference on. 2: 731-734.

Rao, G.M. and B.V.S. Ram. 2009. A Neural Network Based Speed Control for DC Motor. International Journal of Recent Trends in Engineering. 2(6): 121-124.

Atri, A. and M. Ilyas. 2012. Speed Control of DC Motor using Neural Network Configuration. International Journal of Advanced Research in Computer Science and Software Engineering. 2(5): 209-212.

Peng, J. and R. Dubay. 2011. Identification And Adaptive Neural Network Control Of A DC Motor System With Dead-Zone Characteristics. ISA Transactions. 50(4): 588-598.

Wang, L. F. 2004. Controller Design For Dc Motor Drives Using Multi-Objective Optimization Evolutionary Algorithms. Procs. Of The 2004 Intl. Symposium on Micro-Nanomechatronics and Human Science. DOI: 10.1109/MHS.2004.1421273.

Anandaraju, M. B. and P. S Puttaswamy. 2012. Modified Interactive Evolutionary Computing for Speed Control of an Electric DC Motor. International Journal of Computer Applications. 39(15):19-24.

Suman, S. K. and V. K. Giri. 2016. Optimization of PID Controller Parameters for Speed Control of DC Motor. Procs. of Intl. Conf. on Consequences of Recent Researches in Engineering & Technology. 28-33.

Hewit, J. R. and J. S. Burdess. 1981. Fast Dynamic Decoupled Control for Robotics Using Active Force Control. Mechanism and Machine Theory. 16(5): 535-542.

Mailah, M. 1998. Intelligent Active Force Control of a Rigid Robot Arm Using Neural Network and Iterative Learning Algorithms. PhD Thesis, Univ. of Dundee, UK.

Mott, R. L. 2014. Machine Elements in Mechanical Design 5th ed. Pearson.

Ramli, H., M. S. Meon, T. L. T. Mohamed, A. A. M. Isa and Z. Mohamed. 2012. A Fuzzy-Active Force Control Architecture Based in Characterizing Nonlinear Systems’ Behavior. Procedia Engineering. 41: 1389-1397.

Jahanabadi, H., M. Mailah, M. Z. M. Zain and H. M. Hooi. 2011. Active Force With Fuzzy Logic Control Of A Two-Link Arm Driven By Pneumatic Artificial Muscles. Journal of Bionic Engineering. 8(4): 474-484.

Mohammad, A., M. Mailah, I. Z. M. Darus, A. F. Ismail, M. D. Raezaei, A. Mehdi and K. Shahab. 2015. Porosity and Pore Area Determination of Hollow Fiber Membrane Incorporating Digital Image Processing. Recent Advances in Mechanics and Mechanical Engineering. 118-123.



  • There are currently no refbacks.

Copyright © 2012 Penerbit UTM Press, Universiti Teknologi Malaysia.
Disclaimer : This website has been updated to the best of our knowledge to be accurate. However, Universiti Teknologi Malaysia shall not be liable for any loss or damage caused by the usage of any information obtained from this web site.
Best viewed: Mozilla Firefox 4.0 & Google Chrome at 1024 × 768 resolution.