Hybrid Fuzzy PID Control For BLDC Motor

Importance of motor performance in industrial applications

Question:

Discuss About The Hybrid Fuzzy Pid Control For Bldc Motor?

Motor stands as a very basic part from very small electronic device to a huge electronic device and for the larger and high powered electronic devices, high performance of the motor becomes vital, especially, in the industrial applications, like electric trains, steel rolling mills and robotics, where the basic functionality of the device would be majorly dependent on the performance and reliability of the motor. Eventually, a decent motor drive system with higher performance demand the vital features, such as load regulating response, command tracking with good dynamics speed, to achieve the reliable and expected performance, for longer period of time. DC drives act as the best and ideal solution for these industrial requirements, for their ease of application, favourable cost, high reliabilities and flexibilities and so are considered as the backbone of the home appliances, robot manipulators and industrial applications. The matching in all these applications by DC drives is because of the requirements of control of position and speed of the motor. The complexity of the DC drives is lesser than that of the AC drives, because the characteristic of speed torque of them, is much more than AC drives. In addition to that, DC motors have tremendous speed control for deceleration and acceleration and are available at less expensive cost, for even higher ratings of horsepower. All it needs is just a single system for conversion of power to DC from AC. DC drives has become a tradition in the industrial applications and home appliances, because of the machines that are adjustable with speed and various features. The logic for the match of the applications is that DC drives allows the designers and developers of the existing and new electronic applications, for the precise control of the speed of the motor, for the desired performance. The speed controllers have been the key to performance, of the motors, because of DC motor speed control can execute numerous varied tasks, from conventional controller types and numeric controller types. These speed controllers in application are usually of FLC (Fuzzy Logic Controller), PID (Proportional Integral Derivative) and PI (Proportional Integral) and combination of these controllers, such as Fuzzy Genetics Algorithm, Fuzzy=Neural Networks, Fuzzy Swarm[1]. The majority of the control system in the electronic industry in the world is operated by the PID (Proportional Integral Derivative), over 95% of the industrial process control applications. The dominated use of the PID in these applications is because of the unmatchable simplicity, applicability and clear functionality and ease of use, compared to the other controllers[2]. Hence, the PID controllers provide reliable performance and robust performance, for the electronic systems, when apt usage of the parameters and tuning of the PID is set.

Standard control techniques for controlling the performance of DC motors

However, there are certain major issues associated with the control algorithm, used conventionally, such as PID, PD, PI, for the DC motors, is non-linearity effects. The conventional controllers’ performance can be degraded by the DC motor’s non-linear characteristics, like fictions and saturation, etc.[3]. It is generally, difficult find the actual DC motor’s non-linear model and only approximated values can be obtained for the parameters, from systems identification.

Fuzzy Logic Control Fuzzy Logic Control has been introduced and developed in 1973, by L.A. Zadeh and started its application, in 1974, by Mamdani, as an attempt to control system, which have difficulty in modelling structurally. And FLC has become one of the significant fuzzy set theory applications and has been spreading in recent years, with rapid progress. The progress of the FLC has become fruitful and extremely active area of research, having numerous industrial applications. Hence, the FLC has been evolved as a complementary and best alternative to the control strategies used conventionally, in different areas of engineering. The best part is thwat the theory of fuzzy control provides non-linear controllers, having the capability to perform various non-linear complex control action, also for nonlinear systems that are uncertain. FLC design demands no precise system model knowledge, like system’s zero and poles of transfer functions, unlike the conventional control. Fuzzy control system have the two critical inputs, in its design, that are  rate change of the error and tracking error, in the way of human learning imitation[4].

FLC provide the advantages, as the following.

1. Faster system response
2. Simplicity in the design
3. Provides hint of intelligence of human to the controller
4. Needs no mathematical modelling, related to the system
5. Cost effective
6. Increased reliability of the system
7. Increased precision degree
8. Easy handling of the non-linearity of the system
9. Makes use of linguistic variables, in place of numerical ones

Having all the above advantages, fuzzy controllers are allowed in the systems, where the process parameters identification with parameters and process description are highly difficult. Thus, control mechanism can be obtained by the fuzzy characteristics [13].

Fuzzy logic has been experiencing continued success in wider range of applications and so gaining acceptance, in the community of the control engineering, but, there are certain inherent difficulties of the fuzzy techniques, in terms of approaching difficulties, that have been restricting them to grow. The difficulties of the fuzzy techniques are as the following that face the difficultness in the development of the applications.

1. Difficulties to select appropriate function shapes of membership
2. Difficulties to fuzzy rules development, by hand, when larger systems are considered
3. Difficulties, in terms of fuzzy solutions fine tuning, when certain degree and levels of accuracy are specified, and when the robustness or reliability has to be guaranteed, of the solutions. The method of trial and error, still stands to be the basic method, towards expert knowledge improvement, for stable and tuned fuzzy controller development.

1. Subway train

Enhances the accuracy of stop and increases the stable drive with evaluation of conditions of passenger traffic. Gives a better and smoother stop and smoother start

2. Video camcorder

Determination of the best lighting and focusing, during the picture movement

3. Television

Higher precision of adjustment of colour, brightness and contrast, related to the picture, to the please viewers

4. Motor control

Issues with the conventional control algorithm for DC motors

Enhances and improves the motion control range and accuracy, when unexpected conditions are occurred

5. Washing machine

Making adjustment of the washing cycle, through fabric type, load size and dirt judgement

The project has the following objectives.

1. To model the existed BLDC DC motor, separately
2. To control of the speed of BLDC DC motor through typical conventional methods of controlling
3. To control the speed of the BLDC DC motor, with the controller of FUZZY-PID
4. To analyze the MF’s sensitivity, evaluation and comparison, considering difference kinds of them, in the speed control of the fuzzy PID BLDC DC motor
5. To compare the various techniques of the speed controlling

When the typical control techniques are considered, the usage of the PID controller was to use as a structure of standard control. Because of this external disturbances and variation of parameters, in this process, the industrial machinery performance would be distorted greatly and it results in the reduction of the efficiency. When the new control technique is considered, with the fuzzy and PID controllers, using as a conventional technique extension, as it preserves the PID controller linear structure. The fuzzy and PID controllers have been designed, with the fuzzy logic control basic principle, towards gaining a new controller, similar to the controllers of the digital PID, possessing analytical formulas. The fuzzy PID controllers possess control gains varied, in linear structure of them. Varied error signal rages and errors nonlinear functions are some of the variable gains and eventually helps in overall performance improvement, because of characteristics features of them, such as, mechanism of self-tuning that can be adaptable towards rapid error changes and error rate of change, by effects of time delays, uncertainties and nonlinearities, related to the process[5].

A remarkable disadvantage possessed by these fuzzy logic based methods is the appropriate tools lacking, for the analysis of performance of the controllers, like robustness, optimality, stability, etc. One of the significant things is to have the right choice of the rule based and membership functions parameters, due to the fact that the control of the fuzzy logic, stands similar to the control algorithm, stands on the strategy of linguistic control, deriving from the knowledge of expert, into the strategy of automatic control. The FLC operation is according to the qualitative knowledge, regarding the controlling system. It needs the application of the adequate experience and knowledge, to ensure good response, obtained by the system.

The application of PID controller cannot be with the system, having faster parameters change, as it demands PID contrast change, in terms of time. Hence, further study of the Fuzzy controllers and PID controller possible combinations. It indicates that the system has to be controlled well, by PID that is in turn supervised by the system of fuzzy[6].

There are various kinds of MF (Membership Functions) proposed for the system of fuzzy control. The provision of MFs custom design is possible, in certain software of the fuzzy control. The fuzzy control literature gives indication of various kinds of MFs applications[7].

Introduction to Fuzzy Logic Control

References

Zhoa Jin and K. Bose, Bimal Evaluation Of Membership Function For Fuzzy Logic Controlled Induction Motor.

Varun Varuneet, Bhargavi G. and Suneet Nayak, “Speed Control Of Induction Motor UsingFuzzy Logic Approach.

Malhotra Rahul and Kaur Tejbeer, ‘Dc Motor Control Using Fuzzy Logic Controller, (Ijaest)’ International Journal Of Advanced Engineering Sciences And Technologies Vol No. 8, Issue No. 2, 291 – 296.

Alloua Bomediene and Abderrahamani ABdessalam, ‘Neuro-Fuzzy DC Motor speed Control Using Particle Swarm Optimization, Leonaro Electronic Journal of Practices and Technologies ISSN,1583-1078.

Pattaradej Thana, Chen Guanrong and Sooraksa Pitikhate, Design and Implementation of Fuzzy PID Control of a bicycle robot, (Integrated computer-aided engineering, (2002) Vol.9, No.4,

Zhang, N. Wang and S. Wang, “A developed method of tuning PID controllers with fuzzy rules for integrating process, ” ( 2004) Proceedings of the American Control Conference, Boston, pp. 1109-1114.

J. Chalmers, “Influence of saturation in brushless permanent magnet drives”, (IEE proc. B, 1992), Electr.Power Appl, vol.139, no.1.

H. Ang, G. Chong and Y. Li, “PID control system analysis, design and technology,” (IEEE transaction on Control System Technology, 2005), Vol.13, No.4, pp. 559-576.

Tushir Meena, Srivastava Smriti, “Design And Simulation Of A Novel Clustering Based Fuzzy Controller For DC Motor Speed Control”,( Innovative Systems Design And Engineering, 2011) ISSN 2222-1727 (Paper) ISSN 2222-2871 (Online) Vol 2, No 7,

Chow and A. Menozzi, “On The Comparison Of Emerging And Conventional Techniques For DC Motor Control” (proc.IECON, 1992) PP.1008- 1013,.

Essam Natsheh, Khalid A. Buragga, “Comparison between Conventional and Fuzzy Logic PID Controllers for Controlling DC Motors”, (2010) IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 5.

Haytham M. Fayek, I. Elamvazuthi, “Type-2 Fuzzy Logic PI (T2FLPI) Based DC Servomotor Control”, (2012) Journal of Applied Sciences Research, 8(5):2564- 2574. Dr. R. Arulmozhiyal,R. Kandiban, “An Intelligent Speed Controller for Brushless DC Motor”, 978-1-4577-2119-9/12,2012,IEEE.

Malkeet Saini,Neeraj Sharma, “Speed Control of Separately excited D.C Motor Using Computional Method”, (2012) Vol 1 Issue 7.

[1] BomedieneAlloua and ABdessalamAbderrahamani, “Neuro-Fuzzy DC Motor

speed Control Using Particle Swarm Optimization,” Leonaro Electronic

Journal of Practices and Technologies ISSN,1583-1078.

[2] J. Zhang, N. Wang and S. Wang, “A developed method of tuning PID

controllers with fuzzy rules for integrating process, ( 2004) ” Proceedings of the

American Control Conference, Boston, , pp. 1109-1114

[3] B.J. Chalmers, “Influence of saturation in brushless permanent magnet

drives”, ( 1992) IEE proc. B, Electr.Power Appl, vol.139, no.1.

[4] Thana Pattaradej, Guanrong Chen and PitikhateSooraksa, "Design and

Implementation of Fuzzy PID Control of a bicycle robot",  (2002) Integrated

computer-aided engineering, Vol.9, No.4.

[5] Rahul Malhotra,Tejbeer Kaur, “Dc Motor Control Using Fuzzy Logic

Controller”, (Ijaest) International Journal Of Advanced Engineering Sciences

And Technologies Vol No. 8, Issue No. 2, 291 – 296.

[6] Varuneet Varun, G. Bhargavi, Suneet Nayak, “Speed Control Of Induction

Motor UsingFuzzy Logic Approach”.

[7] Tushir Meena, Srivastava Smriti, “Design And Simulation Of A Novel

Clustering Based Fuzzy Controller For DC Motor Speed Control”, (2011)

Innovative Systems Design And Engineering, ISSN 2222-1727 (Paper) ISSN

2222-2871 (Online) Vol 2, No 7.