Speed control of dc motor using artificial bee colony optimization technique

The aim of this work is to design a speed controller of a DC motor by selection of PID parameters using bio-inspired optimization technique of Artificial Bee Colony Optimization (ABC). Here, model of a DC motor is considered as a second order system for speed control. In this work bio-inspired optimization technique in controllers and their advantages over conventional methods is discussed using MATLAB/Simulink. This proposed optimization methods could be applied for higher order system also to provide better system performance with minimum errors. The main aim is to apply ABC technique to design and tune parameters of PID controller to get an output with better dynamic and static performance. The application of ABC to the PID controller imparts it the ability of tuning itself automatically in an on-line process while the application of optimization algorithm to the PID controller makes it to give an optimum output by searching for the best set of solutions for the PID parameters.


INTRODUCTION
DC motor drives are widely used in applications requiring adjustable speed, good speed regulations and frequent starting, braking and reversing. Some important applications are rolling mills, paper mills, mine winders, hoists, machine tools, traction, printing presses, textile mills, excavators and cranes. Fractional horsepower DC motors are widely used as servo motors for positioning and tracking. Although, it is being predicted that AC drives will replace DC drives, however, even today the variable speed applications are dominated by DC drives because of lower cost, reliability and simple control. As per the control of DC motor, there are lot of methods to control the speed and position of the motor. The purpose of a motor speed controller is to take a signal representing the demanded speed and to drive a motor at that speed.
PID (proportional-integral-derivative) control is one of the earlier control strategies. It has a simple control structure which was understood by plant operators and which they found relatively easy to tune. Since many control systems using. PID control have proved satisfactory, it still has a wide range of applications in industrial control. PID control is a control strategy that has been successfully used over many years. Simplicity, robustness, a wide range of applicability and near-optimal performance are some of the reasons that have made PID controller so popular in the academic and industry sectors. Recently, it has been noticed that PID controllers are often poorly tuned and some efforts have been made to systematically resolve this matter. PID control has been an active research topic for many years; since many process plants controlled by PID controllers have similar dynamics it has been found possible to set satisfactory controller parameters from less plant information than a complete mathematical model. These techniques came about because of the desire to adjust controller parameters with a minimum of effort, and also because of the possible difficulty and poor cost benefit of obtaining mathematical models.
The PID controller calculation (algorithm) involves three separate parameters, and is accordingly sometimes called three-term control: the proportional, the integral and derivative values, denoted P, I, and D. The proportional value determines the reaction to the current error, the integral value determines the reaction based on the sum of recent errors, and the derivative value determines the reaction based on the rate at which the error has been changing. The weighted sum of these three actions is used to adjust the process via a control element. By tuning the three constants in the PID controller algorithm, the controller can provide control action designed for specific process requirements. The response of the controller can be described in terms of the responsiveness of the controller to an error, the degree to which the controller overshoots the set point and the degree of system oscillation.
In Bees Algorithm, the colony of artificial bees consists of three groups of bees: employed bees, onlookers and scouts. First half of the colony consists of the employed artificial bees and the second half includes the onlookers. For every food source, there is only one employed bee. In other words, the number of employed bees is equal to the number of food sources around the hive. The employed bee whose the food source has been abandoned becomes a scout. The position of a food sourc possible solution to the optimization problem amount of a food source corresponds to the q of the associated solution. The number of the e or the onlooker bees is equal to the number the population. In proposed ABC-PID co algorithm is used to optimize the gains and applied into the controller of the plant. The ob algorithm is to optimize the gains of the PID the given plant. The proportional gain makes respond to the error while the integral derivati eliminate steady state error and preve respectively.

II. MATHEMATICAL ANALYSIS O MOTOR
In armature control of separately excited D voltage applied to the armature of the mot without changing the voltage applied to the shows a DC motor equivalent model.
where V a = armature voltage (V), R a = arma (Ω), L a = armature inductance (H), I a = arm (A), E b = Back emf (V), ω = angular speed ( motor torque (Nm), T L = load torque (Nm) position of rotor shaft (rad), J m = rotor inertia viscous friction coefficient (Nms/rad), K t = t (Nm/A), K b = Back emf constant (Vs/rad). Figure 2 showing the basic block diagram model including their transfer functions. V supply, T L is load torque and ω is angular spee d by the bees ce represents a and the nectar quality (fitness) employed bees of solutions in ntroller, ABC the values are bjective of this D controller for s the controller ve gain help to ent overshoot

III. SPEED CONTROL USING CLAS TUNING METHODS
The PID controller is the most common g controller in the today's industries. It can be u unit or it can be a part of a distributed com system.
After implementing the PID controller, no tune the controller; and there are different tune the PID parameters like P, I and D. Th (P) part is responsible for following the de while the Integral (I) and Derivative (D) part a accumulation of past errors and the rate of cha the process or plant, respectively. PID controller consists of three types o Proportional, Integral and Derivative control Where p K is the proportional Coefficient and K d is the derivativ T i is known as the integral actio is the derivative action time or rat There are various tuning stra loop step response. While they idea, they differ in slightly in h parameters from the recorded slightly as to relate appropriate model parameters. There are dif Ziegler-Nichols test, and Cohenresponse is not sigmoid or overshoot, or an integrator, then applicable.
This method implicitly ass adequately approximated by a fi with time delay.

G p =
Where K is gain, θ is the dead ti the open loop process time consta the open loop input/output data, a the times T and θ, the PID t obtained directly from the given t methods. Tuning rules based on a measur called process reaction curve me well-known) tuning rule of this 1942 [3]; in this method, the FOPDT process the model parameters estimated method, as indicated in Figure  used to

A: Ziegler-Nichols Tuning Method
The PID tuning parameters as a functio loop model parameters K, T and θ from the Pr curve derived by Ziegler-Nichols [2][3][4][5].
They often form the basis for tuning proce controller manufacturers and process industry are based on determination of some featur dynamics. The controller parameters are then terms of the features by simple formulas. presented by Ziegler and Nichols is based on of the open-loop step response of the syst characterized by two parameters. First determ tangent at this point is drawn. The intersection tangent and the coordinate axes give the param A model of the process to be controlled was these parameters. This corresponds to modelin an integrator and a time delay. Ziegler and given PID parameters directly as functions o behavior of the controller is as can be expect ratio for the step response is close to one smaller for the load disturbance. The oversh point response is too large.

B: Cohen-Coon Tuning Method
Cohen and Coon based the three parametersθ, T and K of th The main design criterion is reje The method attempts to position a quarter decay ration is achieved The PID tuning parameters loop model parameters K, T an derived by Cohen-Coon:

IV. ARTIFICIAL BEE CO (ABC) [6-10]
Dervish Karaboga [10] presen on the performance of the Arti algorithm for constrained optimiz algorithm has been firstly pro optimization problems and sho performance on these kinds of p ABC algorithm has been extend optimization problems and appli problems [8].
In Bees Algorithm, the colon of three groups of bees: emplo scouts. First half of the colony artificial bees and the second ha For every food source, there is o other words, the number of emp number of food sources around th whose the food source has bee becomes a scout. The position of possible solution to the optimizat amount of a food source correspo of the associated solution. The nu or the onlooker bees is equal to the population.

A: Implementation of Algorithm
Steps (pseudo-coding) to initializ 1. Initialize the population i = 1,2,3…S and j = 1,2 nted the comparison results ificial Bee Colony (ABC) zation problems. The ABC oposed for unconstrained owed that it has superior problems. In this paper, the ded for solving constrained ied to a set of constrained ny of artificial bees consists oyed bees, onlookers and consists of the employed alf includes the onlookers. only one employed bee. In ployed bees is equal to the he hive. The employed bee en abandoned by the bees f a food source represents a tion problem and the nectar

V. SIMULINK MODEL OF D
The Simulink model of DC motor

VI. RESULT
The Simulink model in Fig. 8 plots for various tuning method w Fig. 11 show the Speed versus Tim and bio inspired optimization met DC MOTOR r using is shown in Fig 8. f DC motor us tuning method for speed ontroller is shown in Fig 9. various tuning methods d to describe the electrical re given below.   It can be seen from the above comparison t while using the bio-inspired technique (Artific Colony Optimization ) the overshoots obtained compared to the case when the PID Controller via conventional methods. The settling time is case of the Artificial Bee Colony Optimization time is reduced. The Artificial Bee Colony Op PID controller tends to approach the reference and has, comparatively, a zero overshoot. It ca from Fig 11 and 12 that the Conventional PID have overshoot from the reference speed and steady state with larger settling time. Performance comparison of di been reviewed and it is found that Optimization is best among the al for tuning the parameter of PID c time and rise is found to be less. T controllers however are not recom and complex systems as they can become unstable. Hence, a heuris for choice of the controller param provided with the help of Bio insp Artificial Bee Colony Optimizatio variables in a subjective way.