Motor DC PID System Regulator for Mini Conveyor Drive Based-on Matlab

— The goal of the research was to develop a Proportional Integral Derivative (PID) control DC motor system as a Matlab-based driver mini conveyor to discover how to regulate speed on an actual mini conveyor where certain factors that impact the research are not considered 0. The hardware configuration of the mini conveyor used hollow steel as a frame and two copies of the roller belt for the stretch belt conveyor. The PID control system used an empirical approach to get the DC motor's response system to determine the best fit of proportional gain, integral gain and derivative gain, and then implement those PID control systems using Matlab and Arduino as the tools for data acquisition. The speed sensor (Rotary Encoder) was mounted on the roller belt to accurately gain read speed. This sensor will submit data on every increasing in PWM to accurately measure the speed and control speed at the same time, based on the set points. The consequence of this work was the proportional gain values = 0.94624747, the Integral gain = 51.4023958 and the derivative gain = 0.01941504. The PID control, designed to monitor the response of motor DC speed on this research, had successfully reached set point value and decreased steady state error from 47.16 percent to 1.015188 percent (unloaded) and 2.2020751 percent (loaded) on the real response device.


INTRODUCTION
A conveyor is a mechanical system capable of moving goods from one place to another. It has been widely used in the industry for large and sustainable goods transportation. In the production process, the products have a very diverse weight. If overloaded, it can impact the speed of the motor / conveyor on a conveyor not fitted with speed control.
When the conveyor is loaded, the anchor current will increase as the load increases. The increase in the current will cause the interference of the anchor field in the main field to enlarge so that the neutral point is increasingly shifted. Shifting the main field will increase the losses on the brush if the position of the brush is not adjusted. The average efficiency value on a motor without speed control in a study conducted by Stephanus Antonius Ananda is relatively low, around 70%. The research has conducted a motor speed control system using a PID controller. In the study, they use a line follower robot as a plant and produces a maximum overshoot that is quite large, reaching 10%. It will affect the speed of production. It will affect the speed of production. The advances in science and technology generate many ways to control motor speed, one of which uses a PID controller.
Some previous studies have conducted speed control research using DC motors. These researchers, Mohammad and Billah, conducted a research analysis of speed control of DC series motors using a diverter and observing saturation points of speed. The speed of the dc series motor can be controlled using a diverter in the motor field circuit [1]. The linear quadratic regulator and the predictive control model used for optimal control of the DC motor speed were studied by Sahoo. In the DC motor model, different control techniques are used to control the speed angle [2].
The dynamic SMC control scheme with a PID controller that is adapted to the adaptive speed control of a DC motor is examined by Dil Kumar. Adaptive PID with dynamic shear mode control techniques is proposed to control the speed of the excited DC motor separately [3]. Das conducted research on the optimal tuning of the PID controller using the GWO algorithm for speed control on the DC motor. Bio-inspired meta-heuristic soft computation methods to optimize the performance of the PID controller used in DC motor speed control [4]. The speed control of DC motors that are driven separately is supported by photovoltaic energy researched by Jeddi. Speed control of the direct current motor (SEDM) separately using the Maximum Power Point Tracking (MPPT) strategy applied to the DC-DC Boost converter [5].
The linearization approach for anti-windup synthesis and implementation for DC motor speed control was investigated by Saqib. Anti-windup gain is designed for non-linear systems that have input saturation [6]. Nath conducted research on energy-efficient DC drive speed control based on Neural Networks. DC motors have excellent speed control characteristics and are widely used in various industrial applications [7].
The design and simulation of a DC PWM motor speed regulator based on Proteus was investigated by Sun. The Proteus software is used to design and simulate PWM speed regulators of DC motors and get the main control of the motor [8]. Microprocessor-based closed loop speed control on DC motors using PWM was investigated by Ravi Kumar Electric drive systems must have these benchmark properties such as linear control, reliability and stable operation [9]. Remote DC motor control using Embedded Linux and FPGA was investigated by Jaziri. Codeign architecture in which the embedded Linux operating system running on Beaglebone communicates with FPGA via the SPI protocol with a master slave scheme [10]. The speed control of ANFIS based DC motors was investigated by Chaudhary. Comparative analysis of different controller configurations for DC motor speed control [11]. The comparison of P-I and I-P controllers using the Ziegler-Nichols tuning method for speed control of DC motors was investigated by Bhatti. Ziegler-Nichols tuning method for tuning of a Proportional-Integral (P-I) and Integral-Proportional (I-P) controller for a DC motor to control its speed [12]. The control of the speed of a DC motor using voice commands was investigated by Sai. There are many techniques for speed control such as using sound as an input control will reduce manual operation [13].
Simulink simulation of a brushless DC motor speed control system was examined by According to the brushless mathematical model researched by Ling Xu. DC motors, using MATLAB / Simulink to build a variety of independent functional modules, and are integrated into a simulation model of a DC motor control system [14]. The control speed of a four-quadrant closed DC motor is examined by Patil. Low Cost, high performance control Chopper Based Four closed loop of DC Motor [15].
PWM speed control of DC motors based on singular perturbation techniques was investigated by Yurkevich. The proportional-integral (PI) controller is designed to control the motor current and speed based on a single perturbation technique so that the scale-time movement is artificially induced in a closed loop system [16]. Improvement of the performance of the uncensored DC motor control using fuzzy logic was examined by Nguyen Thanh. Despite the development of various types of electric motors, DC motors are still used in many industrial applications because this type of motor can be easily controlled and has a wide range to adjust [17].
The design and performance evaluation of a strong SMC scheme for DC motor speed control was investigated by Dil Kumar T R. The design and performance evaluation of a strong SMC scheme for DC motor speed control [18]. The stabilization of the rotation speed of a DC motor with PWM in the control channel based on the time scale separation method was investigated by Stepanov. The proportionalintegral (PI) controller is designed to control the motor current and speed based on a single perturbation technique so that the scale-time movement is artificially induced in a closed loop system [19]. Speed control for DC motors with pulse width modulation (PWM) method using an infrared remote control based on the ATmega16 microcontroller was investigated by Agung. Direct current (DC) motor speed control for various applications is very important [20].
Al-Maliki conducted research on tuning an FLC-based PID controller for uncensored speed control of a DC motor. Sensor less speed control of permanent magnet DC motors using voltage and current sensors is not ideal [21]. The design and comparative study of the maximum photovoltaic power point tracker with DC motor speed control was investigated by Sultan. Finding the biggest power point by the solar panel direct current motor separately controls the excited speed [22]. The design of a real-time PID tracking controller using the Arduino Mega 2560 for permanent magnet DC motors under real interference was investigated by Adel. The popularity of PID controllers can be attributed in part because of their strong performance in various operating conditions and partly because of its functional simplicity [23]. The GWO / PID approach for Optimal Control of DC Motors was investigated by Ahmed. This work is related to the application of Gray Wolf Optimization (GWO) for optimal control of DC motor speed [24]. Controlling the AVR Voltage and Speed of a DC Motor using a Modified PI-PD Controller was investigated by Singh. Methods of adjusting the PI-PD controller, which are validated using simulation results [25].
The application of DC Motor Speed Control Logic to FPGA was investigated by Bhate. A system that can automate the speed control process and enables the speed control of DC motors that are designed more accurately [26]. Sliding Mode Controller (SMC) Adjusts the Speed Control of a DC Motor researched by Rakhonde. The sliding mode controller is one of the best tools for analyzing second-order dynamic systems [27]. Agarwal conducted research on the comparative analysis of PID controllers for DC motor speed control with the Intelligent Optimization algorithm. Comparative analysis between optimization gray wolf (GWO) and Sine Cosine (SCA) algorithm has been done to adjust the PID controller for DC motor speed control [28]. Wahyudi conducted research on the design of the self-tuning regulator for speed control of brushless DC motors. Brushless DC motor (BLDC) is one type of dc electric motor that has various advantages such as higher efficiency, torque under the low speed range, and power density, and lower maintenance because this motor does not use brushes in the turn process [29]. The PV is fed to a Separate Relaxing DC Motor with Closed Loop Speed Control researched by Narendra. The closed loop control of a single quadrant based chopper is driven separately by an excited DC motor [30].
The study discusses the DC Motor PID Regulatory System as a Mini Conveyor-Based Matlab Drive to find out how to regulate the motor speed at the actual mini conveyor plant, where certain factors are not considered 0. The PID regulation system is applied as a speed control module in the system that uses Matlab and Arduino as data acquisition tools to read speed sensors and control motor speed to suit the desired set point.

II. METHOD
This work aimed to use and establish mathematical models, theories, or physical hypotheses. It had employed a quantitative approach. The measurement phase in this approach is central to quantitative study, since it offers a fundamental relationship between empirical observation and quantitative mathematical language.
In this research, Arduino E and Matlab software worked for simulating the running system, used along with the plant design that produced a speed response to the load on the mini conveyor plant. The research referred to previous journals included in the literature review. The system design was formulated based on the existing industrial phenomena, and the PID configuration used analytical methods.  Figure 1 illustrates the overall hardware circuit scheme for the motor speed regulation system in the PID-based mini conveyor design. The Figure 1 shows that the computer, as the data processing unit, receiver and software sender, displays a Matlab GUI (Graphical User Interface) for reading data in graphical form through a USB cable to Arduino UNO R3. The Matlab software used was the Matlab 2009a model which was combined with Arduino to construct a motor speed control system on the mini conveyor based on PID. The monitoring display was derived from the Matlab in the form of graphs / numerical values obtained from the sensor reading.  Figure 2 is a block diagram of a motor speed regulation system in the design of a mini conveyor. It shows that the input reference value (set point) of the system is the desired speed. The result of the PID control output is the movement of the motor that gets pulses from PWM to regulate the incoming power to the motor. The speed sensor (Rotary Encoder) will be used to measure the speed of the mini conveyor belt in m/s (meters per second), and the result of the reading value is as the feedback value.

B. Firmware design
The software design applied in the research was a program incorporated into the Arduino microcontroller. The programming was made using a special programming language by Arduino in Arduino IDE v1.6.12.
The program started by setting up the desired set-point value, and then the motor would run. The rotary encoder attached to the motor provided RPM to the PID controller, calculated using an analytical method to provide feedback so that the motor speed could be adjusted. When the set-point did not match, the process would be repeated until reaching the desired set-point. Figure 3 displays the system workflow. The research testing and analysis included analysis and testing of the plant control system design for PID, application of the PID control configuration at the plant, and results of the PID control test.

A. System circuit and interface
The circuit design tests included power supply, rotary encoder sensor, microcontroller, and motor driver. Figure 4 displays the circuit test blocks.
The power supply has an important role as a source of voltage in the system. The designed system used two power supplies. The first power supply had a 5V DC output to provide voltage to the microcontroller block and sensor, and the second had a 12V DC output to provide voltage to the motor driver.
The rotary encoder sensor calibration process was carried out to get the RPM value on the DC motor used. The calibration aid used was the DT-2234C digital tachometer. Table 1 presents the calibration. A microcontroller testing was done by connecting the sensor to the microcontroller to calculate motor speed. The results were then displayed on a PC through Arduino software. The microcontroller used was Arduino Uno R3. The data from the microcontroller block was the same as those presented in table 1 because the microcontroller contained the processing data inputted by the rotary encoder sensor.
The motor driver block testing was performed by giving 0 percent, 25 percent, 50 percent, and 75 percent variations in the PWM duty cycle to the motor driver and measuring the DC motor speed without being attached to the conveyor. Figure 4 shows the driver test for the DC motor.

B. PID control configuration
The method used to find system modeling was the method approach to order-1 system characteristics with the required parameters K and τ. It was done by obtaining a system response that would be used to obtain a model or transfer function of a system. The transfer function would be employed as a comparison between real and simulated responses. In this research, the modeling was performed with a 90 rpm set-point at 12V voltage as shown in Figure 5.   Figure 6 illustrates the comparison graph between simulation and real responses. Based on the response graph, the real response parameters could be calculated openly in the forms of (Settling Time), (Rising Time), (Delay Time), and (Steady State Error) with values of τ = 0.0184086258 seconds. Table 2 presents the calculation of PID controller parameters with the settling time performed two times longer. Derivative gain 0.01941504 By entering the values of , , and on the mini conveyor plant, the real response graph of the transfer function set-point was 90 at a voltage of 12 V, as shown in Figure 7.  Figure 7 shows the system response to the 12V switching function test with PID Calculation of system response parameters with τ = 0.02058345 seconds, as presented in tabular form in Table 3.

C. PID control results
The next stage was implementing these parameters into the PID controller. The test was carried out three times with a load of 1 kg, 2 kg, and 3 kg. The result is shown in Table 4.   Table 4 of the PID controller, designed to control the DC motor speed response, it shows that it succeeds in reaching the setpoint value and reducing the ESS (Error Steady State) on the real system response from 47.16% to 1.02% (no load) and 2.2020751% (with 2 kg load).