A Review of Implemention of Evolutionary Computational Techniques for Speed Control of Brushless DC Motor Based on PID Controller

Transcription

1 Advance in Electronic and Electric Engineering. ISSN , Volume 4, Number 2 (2014), pp Research India Publications A Review of Implemention of Evolutionary Computational Techniques for Speed Control of Brushless DC Motor Based on PID Controller M.D. Bhutto 1 and Prof. Ashis Patra 2 1 Electrical Engineering Dept., Madhav Institute of Technology and Science, Gwalior, India. 2 Electrical Engineering Dept., Madhav Institute of Technology and Science, Gwalior, India. 1 mdbhutto786@gmail.com, 2 prof_apatra@rediffmail.com Abstract This paper present comparative study on speed control of Brushless DC motor (BLDC) using PID controller. High efficiency, high torque density, small size increase the popularity of BLDC. BLDC, because of their construction, has high linearity. In this paper the parameters(kp, Ki & Kd) of the PID controller are controlled using evolutionary computational techniques to control the speed of the BLDC motor, various evolutionary technique to control the parameters of PID controllers are known. The principle advantage of a BLDC motor is that its speed can be changed over a wide range very easily such speed control is generally not possible with the AC motor. The mathematical model of the BLDC motor is also developed and is used to examine the performance of the controllers. The simulation result demonstrate that the designed self-tuned PID controller realize a good dynamic behavior of the BLDC,a perfect speed tracking with less rise and settling time,minimum overshoot,minimum steady states and give better performance. The proposed approach has superior features, including easy execution, stable convergence peculiarity, and good computational capacity. Fast tuning of optimal PID controller parameters yields high-quality solution. Keywords-component; BLDC motor, PID Controller, Evolutionary Computational Techniques, MATLAB/ Simulink

2 114 M.D. Bhutto & Prof. Ashis Patra 1. Introduction The development of high performance motor drives is very important in industrial as well as other purpose applications. The high performance of BLDC motor drive system must have good dynamic speed command tracking and load regulating response. The BLDC motors are used in many applications such as defense, industries, Robotics etc. BLDC drives, because of their simplicity, reliability, ease of application and favorable cost have long been a backbone of industrial applications. BLDC drives are less complex with a single power conversion from AC to DC. BLDC drives are normally less expensive for most horsepower ratings. DC motors has its long history. It has been used in the industries for many years now. They provide simple means and precise way of control. In addition, they have high efficiency and have a high starting torque versus falling speed characteristics which helps high starting torque and help to prevent sudden load rise. The dc motors have some deficiencies that needed to be attended to which gave rise to design of some other alternative types of dc motors for example, the lack of periodic maintenance mechanical wear outs, acoustic noise, sparking, brushes effects are some of the problems that were needed to overcome the defects in dc motors. BLDC motors have a long tradition of use as adjustable speed machines and a wide range of options have evolved for this purpose. In these applications, the BLDC motor should be precisely controlled to give the desired performance. Various method of control schemes such as P, proportional integral (PI), PID, adaptive, and FLCs, have been developed for speed control of dc motors. These are introduced controller systems consist of PID controller and DC drive for the speed control. The permanent magnet brushless motor can be classified upon to the back-emf waveform. Where it can be conducted in either brushless AC (BLAC) or brushless DC (BLDC) mode. Usually the BLAC motor has a sinusoidal back-emf waveform and BLDC motor has a trapezoidal back-emf. In recently electrical machines industry productions the brushless direct current (BLDC) motors are rapidly gaining popularity. As a result emphatic studies have been made on synchronous dc motors with brushless commutators. So, current researches have been motivated towards developing brushless direct current motors, which are fast becoming alternatives to the conventional dc motor types. Types of speed control of BLDC motor drive using Evolutionary computational techniques such as particle swarm optimization (PDPSO), MATLAB/SIMULINK, Adaptive Neuro-Fuzzy Controller, Fuzzy Logic PI Controller etc. BLDC motors, also called Permanent Magnet DC, Synchronous motors, are one of the motor type that have more sharply gained popularity, mainly because of their good characteristics an performance. The speed control of BLDC motor can be done in sensor or sensor less mode, but to Low-cost Hall-effect sensors are used. Accelerometers have been extensively applied to measure motor position and speed. Reduce overall cost of actuating devices, sensor less control techniques are normally used. As a result, many researchers have been reported for sensor less drives that can speed control position and/or torque without shaft-mounted position sensors

3 A Review of Implemention of Evolutionary Computational Techniques for Speed Evolutionary Computation Techniques Dr. H.K. Verma et.al [2011] This paper proposed a new approach to control speed of linear brushless DC motor. This paper provides an overview of performance dependant particle swarm optimization (PDPSO) and presenting it as an alternative to evolutionary algorithm. Performance Dependent Particle swarm optimization is used to determine optimal gains of proportional-derivative-integral controller (PID). To obtain optimal solutions, PDPSO introduced the relationship between particle selection and particles performance. The proposed method shows its robustness under critical conditions when conventional optimization methods fail. After finding the two best values, the particles update its velocity and positions using following equation: V k i = wv k i + C 1 rand Pbest i x k i + C 2 rand Gbest k x k i Where, w is inertia weight factor, c1 and c2 are self confidence and swarm confidence respectively. Combinations of these values usually lead to much slower convergence or sometimes non-convergence at all after calculating the velocity, the new position of every particle can be worked out: X i k 1 = X i k + V i k 1 Uma maheswararao et.al [2011] The main objective of this paper is to control the speed of separately excited DC Motor using a Sliding mode controller based on VSS perspective. This controller is rooted on variable structure systems which aim at reducing the settling Time, peak overshoot and steady state error of a DC Motor. In initially, PI controller is used to control the speed of DC Motor. A model is developed and simulated using MATLAB/SIMULINK.Afterward on the common is done with sliding mode controller. The speed control of DC Motor using both PI and Sliding mode controllers is studied and the results are collated. The simulation results display for speed control of DC motor that Sliding mode controller is superior to PI. Since the SMC is robust in presence of disorder, the desired speed is exactly tracked. Fig. 1: Control model of Sliding Mode Control. Dr. R. Arulmozhiyal et.al [2012] Brushless DC (BLDC) motors are widely used for many industrial applications because of their low volume, high efficiency and high torque. This paper introduced a improved Fuzzy PID controller to control speed of

4 116 M.D. Bhutto & Prof. Ashis Patra Brushless DC motor. The proposed controller is called proportional integral derivative (PID) controller and Fuzzy proportional integral derivative controller. This paper endows a supervision of execution conventional PID controller and Fuzzy PID controller. They are complex to tune the parameters and get satisfied control characteristics by using normal conventional PID controller. As the Fuzzy has the efficiency to satisfied control characteristics and it is easy for computing, In order to speed of control the BLDC motor, a Fuzzy PID controller is designed as the controller of the BLDC motor. The experimental results verify that a Fuzzy PID controller has better control performance than the conventional PID controller. The control, modeling and simulation of the BLDC motor have been done using the software package MATLAB/SIMULINK. Fig. 2: Simulation of Fuzzy PID Controller. Fig. 3: Reference speed of 1500 rpm with load (a) Speed and (b) Torque. Vishal verma et. al [2012] This paper present Hybrid Proportional Integral (PI) controllers (with fuzzy controller) as Series Hybrid or Parallel Hybrid configuration. For speed control of Permanent Magnet Brushless DC Motor drive for application to intermitted duty loads. The introduced configurations have superior performance compared to conventional PI controller amidst parametric variations and nonlinearities; it is used to a continuous controller for the drive. Parallel Hybrid utilizes Fuzzy logic

5 A Review of Implemention of Evolutionary Computational Techniques for Speed 117 based on-line parameters tuning, increasing the solidity under severe unknown plant nonlinearities., whereas, In Series Hybrid configuration fuzzy controller provides pre compensated input to the PI controller to reduce oscillation and fast operation The dynamic characteristics of BLDCM such as Back EMF, Current Torque, and speed are observed and analyzed through simulation under MATLAB Simulink environment Fig. 4: Block diagram of Series hybrid controller. Fig. 5: Block diagram of Parallel hybrid controller. It may be observed that series hybrid is slightly better than parallel hybrid in the term of overshoot/undershoot, whereas, parallel hybrid exhibit better performance in term of settling time. Overall both series hybrid and parallel hybrid exhibit better performance than PI when both overshoot/undershoot and settling time are the considered criterion for the comparison P. M. Meshram et.al [2012] In this paper, a weighted tuning methods of a PID speed controller for separately excited Direct current motor is introduced, based on modified Ziegler-Nichol PID tuning formula and Empirical Ziegler-Nichols tuning formula. Both these methods are compared on the basis of output response, minimum overshoot, and minimum settling time for speed requirement application of DC motor. Simulation shows that the performance of PID controller using Modified Ziegler- Nichols technique is better than that of traditional Ziegler-Nichols technique. The transfer function of speed control of DC motor with respect to the input voltage can be written as G(S) = ( ) ( ) = ( ( ) )( )( ) La: Armature inductance, Ra : Armature resistance, B : Friction constant, J: Rotating inertial measurement of motor bearing, Kb : EMF constant, KT : Torque constant, w: An angular velocity of rotor,

6 118 M.D. Bhutto & Prof. Ashis Patra K.Premkumar et. al [2013] A novel method for speed control of brushless dc motor using adaptive fuzzy logic and PI control algorithms has been presented in this paper. Fuzzy logic and PI controllers are formulated and designed using MATLAB toolbox. The parameters such as rise time, peak overshoot, recovery time, settling time and steady state error of a brushless DC motor are taken for analyzing the performance of the proposed controller. The simulation result demonstrated that the response of brush less dc motor with adaptive fuzzy logic shows satisfactory and well damped performance compared to classical PI controller. Keywords-PI controller, Fuzzy logic controller, Brushless DC motor, MATLAB/Simulink The BLDC motor mathematical model can be represented by the following equation in matrix form, L M M i V R 0 0 i M L M i = V 0 R 0 i M M L i V 0 0 R i e e e Fig. 6: Proposed adaptive fuzzy logic controlled BLDC motor. Sujit Kr. Gupta et. al [2013] In this paper, a new technique for controlling the speed of DC motor is developed. They are using the conventional fuzzy control for controlling the Kp, Ki &Kd of an integer-order PID controller along with fractionalorder integral and derivative parts. The parameters of the PID controller are first tuned using Ziegler Nichols. Then, using Genetic Algorithm, optimized solutions for Kp, Ki &Kd and _ & μ (which are the fractional components of integral &derivative parts) are obtained. A Fuzzy Logic Controller (FLC) whose inputs are error & change in error is designed to obtain the crisp values of Kp, Ki &Kd. These values along with the results of GA are combined to yield better results. Simulation results a review in MATLAB. These results are compared with the fractional-order PID &conventional PID simulation results and it is observed that the proposed technique gives better performance Fractional Order Controller In integer-order PID controller, both integrator and differentiator are defined only for integer-orders. The modified fractional order PID is given by

7 A Review of Implemention of Evolutionary Computational Techniques for Speed 119 G (S) = ( ) ( ) = K + K s + K S (α, μ > 0) GC(s) represents the transfer function of generalized controller, E(s) and U(s) are the error and output respectively. Kp, Ki and Kd and α & μ are the proportional, integral, differential constants and fractional components of integral &derivative parts respectively The time domain representation of the PI D controller is U(t) = K e + K D e(t) + K D e(t) 3. Conclusions This paper presents a review based application of differential Evolutionary computational techniques using PID controllers for the speed control of BLDC motors. Also the reduced cost and easily realized for industrial application for simple algorithm is achieved. In this paper, a detailed literature review on speed control of BLDC motors problem has been carried out. The limitations of conventional optimization methods have been outlined. In genetic base control the load torque disturbance applied to the BLDC motor operation, the rotor speed can be regulated to operate within ±5% speed error band. 4. Acknowledgements The authors would like to thanks Dr. Sanjeev Jain, Director MITS, Gwalior, and M.P. to promoting this work. References [1] Dr. H.K. Verma and Miss Cheshta Jain.(2011) A Performance-Dependent PSO based Optimization of PID Controller for DC Motor, /11$26.00_c 2011 IEEE, st International Conference on Electrical Energy Systems, pp [2] X.J Cai, Z. H. Cui, J.C Zeng and Y.Ten (2007) performance-dependent adaptive particle swram optimization, International Journal of Innovative Computing, Information and Control, Vol.3, No.6B,pp , [3] Prof.Dr.A.Sivasubramanian and Sreekala.P.(2011), SPEED CONTROL OF BRUSHLESS DC MOTOR WITH PI AND FUZZY LOGIC [4] CONTROLLER USING RESONANTPOLE INVERTER, /11/$ IEEE, 2011 IEEE PES Innovative Smart Grid Technologies India. [5] Mohammed Golam Sarwer, Md. Abdur Rafiq and B.C. Ghosh," Sliding Mode Speed Controller of a D.C Motor Drive", Journal of Electrical Engineering, The Institution of Engineers, Bangladesh, Vol. EE 31, No. I & II, December 2004.

8 120 M.D. Bhutto & Prof. Ashis Patra [6] Dr. R. Arulmozhiyal and R.Kandiban.(2012) An Intelligent Speed Controller for Brushless DC Motor /12/$26.00_c 2011 IEEE, th IEEE Conference on Industrial Electronics and Applications (ICIEA), pp [7] N. Leena and R. Shanmugasundaram,(2012) Adaptive Controller for Improved Performance of Brushless DC Motor, /12/$ IEEE, 2012 International Conference on Data Science & Engineering (ICDSE), pp [8] Vishal Verma, V Harish and Renu Bhardwaj.(2012) Hybrid PI Speed Controllers for Permanent Magnet Brushless DC Motor, /12/$ IEEE. [9] Neethu U. and Jisha V. R. (2012) Speed Control of Brushless DC Motor: A Comparative Study, /12/$ IEEE, 2012 IEEE International Conference on Power Electronics, Drives and Energy Systems December16-19, 2012, Bengaluru, India. [10] Ahmed Rubaai, Abdul R. Ofoli and Marcel 1. Castro-Sitiriche.(2008) "Design and Implementation of Parallel Fuzzy PID Controller for High-performance Brushless Motor Drives: An integrated Environment for Rapid Control Prototyping "IEEE Trans. On Ind. App. Vol. 44, no.4, July/Aug. [11] P. M. Meshram and Rohit G. Kanojiya.(2012) Tuning of PID Controller using Ziegler-Nichols Method for Speed Control of DC Motor, ISBN: IEEE, IEEE- International Conference On Advances In Engineering, Science And Management (ICAESM -2012) March 30, 31,2012, pp [12] Dr.B.V.Manikanda and K.Premkumar,(2013) Adaptive fuzzy logic speed controller for Brushless DC motor, /13/$ IEEE, 2013 International Conference on Power, Energy and Control (ICPEC), pp [13] Sujit Kr. Gupta and Pragya Varshney.(2013) Fractional Fuzzy PID Controller for Speed Control of DC Motor, /13 $ IEEE DOI /ICACC , 2013 Third International Conference on Advances in Computing and Communications, pp.1-4. [14] S. U. Madankar and M. A.Gai dhane (2011) Study and Simulation of Fuzzy Logic Based Speed Control of Multi Level Inverter Fed PMBLDC Drives, UACEE International Journal of Advances in Electronics Engineering., vol. 1, pp [15] M. F. Z. Abidin, D. Ishak, and A. H. A. Hassan, A Comparative Study of PI, Fuzzy and Hybrid PI Fuzzy Controller for Speed Control of Brushless DC Motor Drive, in Proc. ICCAIE'11, 2011, p. 189.