A Comparative Study on the Speed Response of BLDC Motor Using Conventional PI Controller, Anti-windup PI Controller and Fuzzy Controller

A Comparative Study on the Speed Response of BLDC Motor Using Conventional PI Controller, Anti-windup PI Controller and Fuzzy Controller


Brushless dc motors (BLDC) are widely used for various applications because of high torque, high speed and smaller size. This type of motors are non linear in nature and are affected highly by the non-linearities like load disturbance. Speed control of this motor is traditionally handled by conventional PI and PID controllers. This paper presents the speed control of BLDC motor using anti wind up PI controller. Problems like rollover can arise in conventional PI controller due to saturation effect. In order to avoid such problems anti wind up schemes are introduced. As the fuzzy controller has the ability to control and as it is simple to calculate, a fuzzy controller is also designed for speed control of BLDC motor. The control and simulation of BLDC motor have been done using software MATLAB/SIMULINK. The simulation results using anti wind up PI controller and fuzzy controller are compared with PI controller.


  1. BLDC
  2. Speed response
  3. PI controller
  4. Fuzzy
  5. Anti windup






Fig.1. Simulation block diagram




Fig.2. Speed response under no load

Fig.3. Speed response for step increase in speed

Fig.4. Speed response for step increase in speed

Fig.5. Speed response under loaded condition

Fig.6.Speed response under load condition



 This paper presents the speed control of BLDC motor using anti wind up PI controller and fuzzy controller for three phase BLDC motor. The simulation results are compared with PI controller results. The conventional PI controller results are slower compared to fuzzy and anti wind up controllers. From the simulation results, it is clear that for the load variation anti wind up PI controller gave better response than conventional PI and fuzzy controller. Hence anti wind up PI controller is found to be more suitable for BLDC motor drive during load variation. It can also be observed from the simulation results that performance of fuzzy controller is better during the case of speed variation.



[1] R. Arulmozhiyal, R. Kandibanv, “Design of Fuzzy PID Controller for Brushless DC Motor”, in Proc. IEEE International Conference on Computer Communication and Informatics, Coimbatore, 2012.

[2] Anirban Ghoshal and Vinod John, “Anti-windup Schemes for Proportional Integral and Proportional Resonant Controller”, in Proc. National Power electronic conference, Roorkee, 2010.

[3] M. F. Z. Abidin, D. Ishak and A. Hasni Abu Hassan, “A Comparative Study of PI, Fuzzy and Hybrid PI Fuzzy Controller for Speed Control of Brushless DC Motor Drive”, in Proc. IEEE International conference on Computer applications and and Industrial electronics, Malysia, 2011.

[4] J. Choi, C. W Park, S. Rhyu and H. Sung, “Development and Control of BLDC Motor using Fuzzy Models”,in Proc. IEEE international Conference on Robotics, Automation and Mechatronics, Chengdu, 2004.

[5] C. Bohn and D.P. Atherton, “An analysis package comparing PID anti-windup strategies,” IEEE Trans. controls system, Vol.15, No. 2, pp.34-40, 1995.


Simulation and Control of Solar Wind Hybrid Renewable Power System


The sun and wind based generation are well thoroughly considered to be alternate source of green power generation which can mitigate the power demand issues. This paper introduces a standalone hybrid power generation system consisting of solar and permanent magnet synchronous generator (PMSG) wind power sources and a AC load. A supervisory control unit, designed to execute maximum power point tracking (MPPT), is introduced to maximize the simultaneous energy harvesting from overall power generation under different climatic conditions. Two contingencies are considered and categorized according to the power generation from each energy source, and the load requirement. In PV system Perturb & Observe (P&O) algorithm is used as control logic for the Maximum Power Point Tracking (MPPT) controller and Hill Climb Search (HCS) algorithm is used as MPPT control logic for the Wind power system in order to maximizing the power generated. The Fuzzy logic control scheme of the inverter is intended to keep the load voltage and frequency of the AC supply at constant level regardless of progress in natural conditions and burden. A Simulink model of the proposed Hybrid system with the MPPT controlled Boost converters and Voltage regulated Inverter for stand-alone application is developed in MATLAB.


  1. Renewable energy
  2. Solar
  3. PMSG Wind
  4. Fuzzy controller
  5. P&O



Figure 1. Block diagram of PV-Wind hybrid system


Figure 2. PV changing irradiation level

Figure 3. Output voltage for PV changing irradiation level

  Figure 4. Wind speed changing level

Figure 5. Output current wind

Figure 6. Output Voltage wind

Case 1 : PI voltage regulated inverter

Figure 7. Output voltage for inverter

Figure 8. Power generation of the hybrid system under varying wind speed and irradiation

Case 2 : fuzzy logic voltage regulated inverter

Figure 9. Output voltage for inverter

Figure 10. Power generation of the hybrid system under varying wind speed and irradiation


Nature has provided ample opportunities to mankind to make best use of its resources and still maintain its beauty. In this context, the proposed hybrid PV-wind system provides an elegant integration of the wind turbine and solar PV to extract optimum energy from the two sources. It yields a compact converter system, while incurring reduced cost.

The proposed scheme of wind–solar hybrid system considerably improves the performance of the WECS in terms of enhanced generation capability. The solar PV augmentation of appropriate capacity with minimum battery storage facility provides solution for power generation issues during low wind speed situations.

FLC voltage regulated inverter is more power efficiency and reliable compared to the PI voltage regulated inverter, in this context FLC improve the effect of the MPPT algorithm in the power generation system of which sources solar and wind power generation systems.


[1] Natsheh, E.M.; Albarbar, A.; Yazdani, J., “Modeling and control for smart grid integration of solar/wind energy conversion system,” 2nd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies (ISGT Europe),pp.1-8, 5-7 Dec. 2011.

[2] Bagen; Billinton, R., “Evaluation of Different Operating Strategies in Small Stand-Alone Power Systems,” IEEE Transactions on Energy Conversion, vol.20, no.3, pp. 654-660, Sept. 2005

[3] S. M. Shaahid and M. A. Elhadidy, “Opportunities for utilization of stand-alone hybrid (photovoltaic + diesel + battery) power systems in hot climates,” Renewable Energy, vol. 28, no. 11, pp. 1741–1753, 2003.

[4] Goel, P.K.; Singh, B.; Murthy, S.S.; Kishore, N., “Autonomous hybrid system using PMSGs for hydro and wind power generation,” 35th Annual Conference of IEEE Industrial Electronics, 2009. IECON ’09, pp.255,260, 3-5 Nov. 2009.

[5] Foster, R., M. Ghassemi, and A. Cota, Solar energy: renewable energy and the environment. 2010, Boca Raton: CRC Press.