Position control for fan and plate system using pid controller

The PID control algorithm is then implemented, where the proportional, integral, and derivative terms are appropriately tuned to achieve optimal performance.. Proportion-Integral-Derivat

INTRODUCTION

Overview

1.2.1 Description about the fan and plate system

The Fan-Plate system, featuring a PID controller, is an advanced mechanical structure that ensures accurate position control It utilizes a thin plate subjected to vibrations from a fan mechanism The PID controller employs proportional, integral, and derivative actions to reduce position error and stabilize the plate By continuously adjusting control inputs based on the error signal and its derivatives, the PID controller effectively reduces vibrations and maintains stability.

The integration of a PID controller in the Fan-Plate system significantly improves positioning accuracy and control quality, making it ideal for applications in robotics, aerospace, manufacturing, and structural engineering Despite its straightforward modeling structure, the Flat and Plate System is highly sensitive to noise, which is affected by factors such as the mass of the flat plate and the surrounding environment These challenges complicate the design of an effective controller for the Fan-Plate System.

Figure 1.1 Operational description diagram 1.2.2 Models and studies have been completed all over the world a In international

Numerous researchers have explored angle control for first-person shooters (FPS) using various algorithms, including sliding mode control (SMC) and fuzzy sliding mode control (FSM) FPS models that utilize encoders to measure feedback angles have demonstrated success in both simulations and real-world applications, serving as valuable resources for teaching and implementing additional algorithms on practical models.

The evolution of fan and plate systems globally has been propelled by significant advancements in technology and engineering Key factors influencing this development include enhanced efficiency, improved design capabilities, and the integration of innovative materials, all contributing to the systems' effectiveness in various applications.

Figure 1.2 FPS from National Yunlin University of Science & Technology

Figure 1.3 FPS from KentRidge Instrument

The evolution of fan and plate systems in Vietnam has been shaped by technological advancements, industrial growth, and market demands; however, their application remains underwhelming.

Objectives of the topic

 Investigation and verification of PID law

 Set the preferred inclination angle

 Design of the system monitoring and control interface

Approach method to the topic

To develop an effective FPS, start by calculating its mathematical model using the torque equation and Kirchhoff’s voltage law Once the model is established, design a PID controller and validate it using MATLAB/Simulink Finally, construct a realistic model and optimize the PID parameters to achieve the best performance.

Research method of the topic

 Determine the mathematical model of FPS by referring to previous studies

 Conduct simulation on MATLAB to verify the feasibility of applying the PID controller on the real model

 Applying PID controller and adjust Kp, Ki, Kd manually

 Record controller results and improve controller better

Research subjects of the topic

This research focuses on the Fan and Plate system, which operates as a single-input and single-output (SISO) control mechanism It regulates the angle of the plate in response to airflow generated by the fan.

THEORETICAL BASIC AND ALGORITHM

Fan and plate system

This chapter focuses on the operational principles of the FPS, detailing its mathematical model and incorporating the PID algorithm for effective control Additionally, it discusses the implementation of the Kalman filter to minimize noise during system operation.

2.1.1 Detailed description of FPS system

The Fan and Plate system is a widely utilized model in automatic control experiments, featuring a flat plate mounted on a fixed bracket and a DC motor fan operating at 12V The flat plate, made of PVC, is designed to pivot from its original position when subjected to airflow from the fan, which is strategically positioned to create a significant wind effect across a broad area To enhance performance, the mounting bar is lubricated to minimize friction, allowing for greater deflection angles and diverse measurement amplitudes The system's primary function is to control the deflection angle, α, by adjusting the fan's voltage, which alters the wind speed and subsequently the angle of the flat plate This voltage variation is managed automatically through pulse width modulation (PWM) by a microcontroller, ensuring precise control over the deflection angle.

In this graduation project, knowledge about torque equation and Kirchhoff voltage law is applied to analyze mathematical model of Fan and Plate system

2.1.2 Analyze system and build mathematical model

Figure 2.1 The schematic diagram of FPS

The table provided below describes the components of the system and symbols having:

Te Torque of the motor N.m

J m Moment inertia Kg.m 2 ω r Rotor angular velocity of the motor rad/s

B m The viscous friction coefficient Kg/(m.s) k t The constant of torque N.m/A i a The armature current A vb The anti-electromotive force voltage V k b The constant of anti-electromotive none unit

The torque equation of the fan motor is described:

Since the FPS fan motor is a DC motor, the torque T t e ( ) is as follow9:

The anti-electromotive force voltage v b is linearly related to the motor’s speed as follows:

Thus, the Kirchhoff’s voltage law for the armature bay is represented by

Substituting (2.2)Error! Reference source not found.Error! Reference source not found into (2.1), we have

The Laplace transform is established based on equations (2.4) and (2.5), with the initial condition set at zero This foundational concept allows for the analysis and solution of differential equations in engineering and physics.

Because L a is less than the armature resistance R a , it is frequently adjusted to zero in Assuming that the armature inductance is zero, L a  0 Then

The inverse Laplace transform of eq.(2.9) Error! Reference source not found is

Determining the mathematical relationship between velocity \( \omega_r(t) \) and angle \( \alpha(t) \) is complex; therefore, we utilized curve fitting to establish this connection For simplification, we assume a direct proportional relationship between the angle and fan rotation.

Substituting (2.11)Error! Reference source not found into (2.10) Error! Reference source not found., we have

Algorithm

2.2.1 Proportion-Integral-Derivative (PID) controller

PID control is a widely used process control technique that combines three types of controllers: proportional, integral, and differential This method effectively minimizes error signals by leveraging the proportional effect to reduce error, the integral effect to address accumulated past errors, and the differential effect to predict future errors By utilizing closed-loop feedback, PID controllers enhance response speed, limit overshoot, and reduce oscillation, making them essential in various applications across electrical systems, automation, and electronics.

Figure 2.2 Block diagram of PID controller

In this project, a discrete (z-domain) PID controller is utilized instead of a continuous (s-domain) PID controller, due to the processing time required by microcontrollers to feedback to the controller The discrete PID controller is favored in numerous applications, such as robotics, industrial automation, and process control, for its capability to deliver precise and stable control over dynamic systems.

The PID controller comprises three components: the Proportional Term (P), the Integral Term (I), and the Derivative Term (D) Each component has a distinct influence on the control process

The proportional term in PID controllers is a crucial parameter that dictates the range around the setpoint where the controller output is maximized A smaller proportional term results in a narrower active range, enabling precise control but potentially increasing oscillations around the setpoint Conversely, a larger proportional term broadens the range, allowing for a more gradual response, which may compromise both responsiveness and accuracy.

The choice of the proportional term is crucial for the controlled system's characteristics and the desired performance Achieving the optimal balance between stability and responsiveness typically necessitates careful tuning and adjustment Various control applications demand distinct proportional term settings to meet specific control objectives effectively.

Figure 2.4 Proportional term block diagram Equational description:

With: u(t) : Control signal from of PID controller (Output value) e(t) : Error feedback from control signal (Input value)

Figure 2.5 Effect of changing Kp 2.2.1.2 Integral term

The integral term is a crucial element of the PID controller, essential for adjusting the control signal based on the cumulative error over time It effectively eliminates steady-state errors and enhances control performance, particularly when faced with disturbances or long-term deviations in the system By continuously summing the instantaneous error values, the integral action accumulates the error, causing the integral term to increase as long as the error persists This accumulated error is then multiplied by the integral gain (Ki) and added to the controller's output, aiming to gradually reduce the steady-state error to zero.

The integral action in a PID controller is crucial for eliminating steady-state errors, such as offset or bias in controlled variables For instance, in temperature control systems, the integral term effectively compensates for consistent temperature differences between the setpoint and the actual temperature, ensuring the system reaches and maintains the desired value Ultimately, the integral operation allows the system to progressively minimize steady-state errors by accumulating the error signal over time.

Implementing a corrective action that addresses sustained errors enhances control performance and eliminates ongoing deviations from the setpoint Proper tuning of the integral gain is essential to avoid instability and excessive overshoot.

Figure 2.6 Integral term block diagram Equation description:

Figure 2.7 Effect of changing Ki

The derivative term, or "D term," is a vital aspect of a PID controller, as it accounts for the rate of change of the error between the desired setpoint and the actual process value By anticipating and responding to sudden changes in error, the D term provides a damping effect that reduces overshoot, enhances system stability, and improves overall response time It is calculated by assessing the error's rate of change over time, introducing a control input proportional to this rate Proper tuning of the derivative term is essential for optimal control performance; if set too high, it can amplify noise and destabilize the system, while a setting that is too low may result in sluggish control responses and slow error correction.

The derivative term is essential in PID controllers, providing a damping effect that enhances the system's response to abrupt changes This functionality is crucial for applications demanding rapid and precise control, including robotics, motion control, and dynamic processes.

Figure 2.8 Derivative term block diagram Equation description:

Figure 2.9 Effect of changing Kd 2.2.2 Variations of PID controller

In addition to the standard PID controller, there are several variations that utilize subsets of the proportional (P), integral (I), and derivative (D) terms Here are three common types:

A PI controller effectively merges proportional and integral control actions to optimize system performance By utilizing the error signal and its integral, it generates a control output that responds immediately to current errors while eliminating steady-state errors through the accumulation of past errors The lack of a derivative term simplifies the implementation of the controller, making it particularly beneficial for systems with slower dynamics or where derivative action is unnecessary.

The PD controller combines proportional and derivative terms to effectively manage control output by using the current error and its rate of change The proportional term ensures a quick response to errors, while the derivative term predicts and mitigates changes in the error's rate By omitting the integral term, the PD controller minimizes steady-state errors, making it ideal for systems where integral action could cause instability or other unwanted effects.

 PID Controller: The PID controller encompasses all three components: proportional, integral, and derivative terms It combines the immediate response of the

The PID controller effectively combines proportional, integral, and derivative terms to eliminate steady-state errors, anticipate changes, and dampen the rate of error change This balanced approach ensures stability, responsiveness, and precise control across diverse control scenarios.

Choosing between PI, PD, or PID controllers is crucial and should be based on the specific needs and characteristics of the system being controlled Key factors that influence this decision include system dynamics, desired response time, sensitivity to noise, and stability requirements.

According to the researched and taught knowledge, there is a discrete transfer function as follows:

Percent of Overshoot (POT): is a measure of the overshoot of the system:

Figure 2.10 Overshoot description 2.2.3.2 Steady-state error

Steady-state error is the difference between the set signal and the feedback signal:

Figure 2.11 Steady-state error description 2.2.4 Advantages of PID Controller:

 Simplicity: PID controllers are relatively simple to understand and implement, making them widely used in various applications

 Stability: When properly tuned, PID controllers can provide stable and reliable

19 control over a wide range of systems

 Fast Response: The combination of proportional, integral, and derivative actions allows PID controllers to respond quickly to changes in the error signal, leading to fast system response times

 Flexibility: PID controllers can be used in both continuous and discrete systems, making them versatile for different control scenarios

 Robustness: PID controllers can handle disturbances and variations in system parameters to some extent, making them robust in certain applications

 Tuning Complexity: Tuning PID controllers to achieve optimal performance can be a complex and time-consuming task, requiring expertise and practical experience

 Sensitivity to Parameter Changes: PID controllers can be sensitive to changes in system parameters, and retuning may be necessary when there are significant variations

 Steady-State Error: In certain systems, PID controllers may struggle to eliminate steady-state error completely, requiring additional control techniques or modifications

 Lack of Adaptability: Traditional PID controllers do not adapt to changes in system dynamics or operating conditions unless manually retuned

 Limited Control Authority: PID controllers may have limitations in controlling systems with nonlinear dynamics or large control authority requirements

Recent advancements, including adaptive PID controllers and alternative control strategies, have been designed to overcome existing limitations and enhance overall control performance in complex systems.

HARDWARE DESIGN AND SOFTWARE USED

Hardware selection

The choice of the Arduino Mega 2560 microcontroller is driven by its cost-effectiveness and robust user community, as highlighted in Chapter 1 Our decision is further supported by our familiarity and previous experience with this microcontroller in academic projects, allowing us to leverage our existing knowledge effectively.

Figure 3.1 Component diagram of Arduino Mega 2560 The Arduino Mega 2560 microcontroller boasts the following specifications:

 Digital I/O Pins: 54 (of which 15 provide PWM output)

 DC Current per I/O Pin: 20 mA

 DC Current for 3.3V Pin: 50 mA

 Flash Memory: 256 KB of which 8 KB is used by the bootloader

These specifications make the Arduino Mega 2560 a versatile microcontroller suitable for various projects that require a large number of digital and analog I/O pins, extensive program storage, and robust communication capabilities

In our project, we utilized the GY-521 6DOF IMU MPU6050, which combines accelerometer and gyro sensor functionalities Initially, we chose an encoder for its accuracy and compatibility with our microprocessor However, we encountered issues with frictional resistance when the encoder was mounted on the rotational axis, which reduced the deflection angle and limited our investigation To address this challenge, we opted to switch to the MPU-6050 sensor, which is more cost-effective despite its noise issues We successfully mitigated these noise concerns by implementing a Kalman filter, ensuring reliable performance for our measurements.

GY-521 6DOF IMU MPU6050 sensor is used to measure 6 parameters: 3-axis Angle (Gyro), 3-axis accelerometer (Accelerometer), is the most popular type of accelerometer on the market today

Figure 3.2 GY-521 6DOF IMU MPU6050 pins diagram Specifications of MPU-6050:

 Communication Interface: I2C (Inter-Integrated Circuit)

 Sensitivity: Accelerometer: 16384 LSB/g, Gyroscope: 131 LSB/°/s

 Digital Motion Processor (DMP): Integrated motion processing unit for offloading computation from the main microcontroller

 Motion Detection: Supports motion detection interrupt, zero-motion detection, and FIFO buffer for sensor data

 Programmable Features: Configurable digital low-pass filters, sample rate divider, and full-scale range for both accelerometer and gyroscope

 Dimensions: 4mm x 4mm x 0.9mm (package size)

The MPU-6050 is widely utilized in various applications, including motion tracking, gesture recognition, robotics, and gaming, making it essential for projects that demand precise motion sensing capabilities.

Robotics: The MPU-6050 can be used in robotics to measure the orientation and motion of a robot It can provide crucial data for stabilization, balance control, and navigation

The MPU-6050 plays a crucial role in Virtual Reality (VR) and Augmented Reality (AR) systems by accurately tracking head movements, enabling users to control virtual environments and interact seamlessly with augmented content.

Gaming: The MPU-6050 can be used in gaming applications to enable motion-controlled input It can capture the player's movements and translate them into in-game actions

Drones and UAVs: Drones and unmanned aerial vehicles (UAVs) can utilize the MPU-

6050 to measure the aircraft's attitude and stabilize flight It helps in maintaining a level flight, compensating for external disturbances, and providing accurate position data

Motion-controlled devices: The MPU-6050 can be used in various motion-controlled devices such as motion-sensitive remote controls, gesture-based interfaces, and motion- activated switches

The MPU-6050 sensor is essential for electronic stabilization in cameras and image stabilization systems, effectively minimizing the impact of hand movements and vibrations This technology ensures smoother and clearer images and videos, enhancing overall visual quality.

The MPU-6050 sensor plays a crucial role in vehicle tracking systems by monitoring acceleration, detecting sudden movements or collisions, and aiding in navigation Its integration into such systems enhances safety and provides valuable data for efficient route management.

The H-bridge circuit is a fundamental power circuit widely used for controlling DC motors and 2-pole stepper motors Various types of H-bridges exist, each designed for specific applications, with differences primarily in their controllability Factors such as control voltage levels, speed control capabilities, and PWM pulse frequency significantly influence the selection of components for an H-bridge.

The direction of a DC motor's rotation—either forward or backward—depends on the connection of its positive and negative terminals For instance, connecting terminal A to the positive (+) terminal and terminal B to the negative (-) terminal results in clockwise rotation Conversely, reversing the connections, with A linked to (-) and B to (+), causes the motor to rotate counterclockwise When switches S2 and S3 are closed, current flows from the power source through the motor to the ground, enabling forward rotation This process illustrates how proper terminal connections influence the operational direction of a DC motor.

Figure 3.4 H bridge circuit schematic in the forward direction

In contrast, the motor rotates in the opposite direction when S1 and S4 are closed

Figure 3.5 H bridge circuit schematic in the reverse direction

We chose the L298 DC motor driver for its cost-effectiveness and compatibility with the motor's low power needs in our project Given that the motor does not require significant power, the L298 motor driver provides an excellent balance of functionality and affordability, as illustrated below.

Figure 3.6 H-Bridge L298 Pinout of L298 H-bridge:

(1) Pin powers the IC’s internal H-Bridge, which drives the motors

Input voltages ranging from 5 to 12V

(3) Used to power the logic circuitry within the L298N IC, and can range between 5V and 7V

(4) Turn on/off and control the speed of motor A

(5) Control the spinning direction of motor A

(6) Control the spinning direction of motor B

(7) Turn on/off and control the speed of motor B

According to the original plan, the student planned to use Centrifugal exhaust fan model

After testing various fans to generate sufficient force on the plate, it became clear that the initial choice was inadequate Despite experimenting with multiple motors and cooling fans, the desired effect was not achieved Ultimately, the student decided to use the Box Fan model B300 for optimal performance.

Figure 3.8 Centrifugal exhaust fan model 7530

Figure 3.9 Box Fan model B300 Table 3 2 Compare the specifications of 2 types of fans Centrifugal exhaust fan model 7530 Box Fan model B300

Amperage current 0.23A (no-load) 1.8A(load)

 Why student choose Box Fan model B300

 Calculate gravitation of the Plate:

 The force of Centrifugal exhaust fan model 7530 with r=2.5cm, NE00RPM

 Wind pressure in SI system (Newton/square meter):

 The drag coefficient of the object:

The standard drag coefficient for short flat panels is 1.4 so we have Cd 1.4

 The force of Centrifugal exhaust fan model 7530:

 The force of Box Fan model B300 with rcm, R (propeller radius)|m,

 Wind pressure in SI system (Newton/square meter):

 The drag coefficient of the object:

The standard drag coefficient for short flat panels is 1.4 so we have Cd 1.4

The force of Box Fan model B300:

Summarize, from the calculation above the suitable fan for our project is B300 box fan 3.1.5 The plate

In this project, we chose foam plates for their cost-effectiveness and easy availability in stores, streamlining the procurement process The foam plate's compatibility with fan-generated airflow allows for significant deflection angles, providing a broader range for model surveys than materials like iron, wood, or aluminum.

Students use dimension (length-height-width): 30x20x0.4 (Unit: cm):

Students use C-480-12 converter power block to power the motor

Figure 3 12 Power supply pins diagram 3.1.7 Modeling off-axis Fan-Plate system and mechanical processing

A summary and the many parts of the actual FPS model are shown in the following figure:

32 Figure 3.13 View from the front of the system

33 Figure 3.14 Side view of the system

Figure 3.15 Top-down view of the system

Software

In this project, the software Arduino IDE will be instrumental in integrating the fan and plate system:

Programming Control Logic: Arduino IDE allows you to write the necessary code to control the fan and plate system You can define the fan speed, set temperature thresholds,

35 implement feedback loops, and customize the cooling strategy according to your project's requirements

Arduino IDE enables seamless sensor integration, supporting a range of devices like temperature, humidity, and pressure sensors By connecting these sensors to your Arduino board, you can easily read and process sensor data This valuable information allows for automated adjustments to fan speed or activation of the cooling system based on real-time environmental conditions.

PWM Control for Fan Speed: Arduino boards often have built-in Pulse Width Modulation

PWM pins enable precise control of fan speed, allowing for dynamic regulation based on temperature readings or other input parameters By using the Arduino IDE, users can program the PWM output to adjust fan speed effectively.

The Arduino IDE facilitates seamless communication between your project and external devices, enabling serial connections with computers, data display on LCD screens, and wireless communication through Bluetooth or Wi-Fi modules This functionality allows for effective monitoring of system status, real-time temperature readings, and the ability to receive commands for adjusting fan and plate systems.

The Arduino IDE features a Serial Monitor that facilitates data exchange between your project and computer, proving invaluable for debugging and troubleshooting By printing debugging information, sensor readings, and error messages to the Serial Monitor, you can effectively diagnose issues within your fan and plate system.

Visual Studio is a robust integrated development environment (IDE) that, although not specifically tailored for Arduino projects like the Arduino IDE, provides valuable advantages for developing a fan and plate system project.

Visual Studio enhances productivity in code editing with its advanced features, including syntax highlighting, code completion, and code navigation These tools offer valuable suggestions, identify errors, and provide contextual information, making it easier to write code for your fan and plate system efficiently.

Visual Studio provides powerful debugging tools that enable you to effectively identify and resolve coding issues With features like setting breakpoints, stepping through code line by line, inspecting variable values, and analyzing execution flow, you can efficiently diagnose problems and ensure the optimal performance of your fan and plate system.

Version Control Integration: Visual Studio supports integration with popular version control systems like Git, allowing you to manage your project's source code effectively

You can track changes, collaborate with others, and maintain different versions of your codebase, ensuring the project's code is well-documented and accessible

Visual Studio offers a well-structured environment for efficiently managing project files, dependencies, and configurations By creating solution files, developers can organize multiple projects, oversee libraries, and manage dependencies effectively, while also configuring build settings This structured approach enhances the development workflow and ensures that your fan and plate system project remains organized.

Visual Studio's compatibility with various programming languages and platforms enables the integration of third-party libraries and extensions, offering enhanced functionality and customization for your fan and plate system project.

Visual Studio enhances collaboration and team development by allowing multiple developers to work on the same project simultaneously Its integrated features enable code sharing, change tracking, and task management within the IDE, promoting effective teamwork and streamlined project development.

In this project, the software Visual Studio will be used to design the control interface for the system

VERIFY THE CONTROLLER ON SIMULATION

Controller

The PID algorithm consists of three components: Kp, Ki, and Kd A single PID controller is sufficient to manage a fan and plate system, as it only needs to control one variable: the angle of the plate.

Figure 4.1 FPS block diagram on simulation

Figure 4.2 Simulate the PID controller of FPS description

Table 4.1 Blocks in simulation program

(2) Take angle plate error compare to setpoint

(4) Keep the control signal continue

Figure 4.3 PID controller block diagram

Figure 4.4 PID controller block description

Table 4.2 Blocks in simulation program

(5) Block parameters: Discrete Transfer Fcn

4.1.2 PID rule results and comments when setpoint

Run simulation with test change Kp parameters to get simulation results

Table 4.3 Changing Kp when setpoint at 15

Figure 4.5 Result of simulation case 1 Steady-State time and error, respectively:

Figure 4.10 System response when changing Kp

In Figure 4.10, the step response exhibited minimal fluctuation, achieving a steady state following a brief overshoot Notably, the steady-state value was elevated compared to the initial conditions.

Kp since it’s proportionally scale with the error (setpoint – process value) After increasing the value of Kp over 4 there was a really big overshot (27.8%)

Run simulation with test change Ki parameters to get simulation results

Table 4.4 Changing Ki when set point at 15

Figure 4.16 System response when changing Ki

The implementation of the Ki parameter significantly improved the system's ability to reach the setpoint value As illustrated in Figure 4.16, increasing the Ki value accelerated the step response time to steady state; for instance, with Ki set at 0.5, the system took over two seconds to stabilize, whereas a Ki value of 2 achieved steady state in approximately 1.2 seconds However, excessively high Ki values led to overshooting, prompting us to halt the experiment to avoid undesirable outcomes.

Run simulation with test change Kd parameters to get simulation results

Table 4.5 Changing Kd when setpoint at 15

Figure 4.20 System response when changing Kd

According to Figure 4.20, Kd seemed have minimum effect on the step response if we use value

Kp = 0.5 and Ki = 2.0 It’s also expected the with too much Kd could much the system fluctuate since it affects the rate of changes in the system

4.1.3 PID rule results and comments when decrease setpoint (Angle degree)

Table 4.6 Changing Kp when setpoint at 10

Figure 4.21 Result of simulation case 14

Steady-State time and error, respectively:

Figure 4.26 System response when changing Kp and set point decrease

Table 4.7 Changing Ki when setpoint at 10

Figure 4.32 System response when changing Ki and set point decrease

Table 4 8 Table 4 7 Changing Kd when set point at 10

Figure 4.36 System response when changing Kd and set point decrease

4.1.4 PID rule results and comments when set point increase (Angle degree)

Table 4.9 Changing Kp when set point at 20

Figure 4.42 System response when changing Kp and set point increase

Table 4.10 Changing Ki when set point at 20

Figure 4.48 System response when changing Ki and setpoint increase

Table 4.11 Changing Kd when setpoint at 20

68 Figure 4.52 System response when changing Kd and setpoint increase

BUILDING EXPERIMENTAL MODEL

Experimental model with PID controller

Figure 5.1 The result of stable control of FPS with desired setting angle is 10 degree Steady-State time and error, respectively:

Figure 5.2 Survey results to increase Kp of FPS Steady-State time and error, respectively:

Figure 5.3 Survey results to reduce Kp of FPS Steady-State time and error, respectively:

Figure 5.4 Survey results to increase Ki of FPS

Figure 5.5 Survey results to reduce Ki of FPS

Figure 5.6 Survey results to increase Kd of FPS

Figure 5.7 Survey results to reduce Kd of FPS

Figure 5.8 Comparison of scaling factor correction Kp When the scaling factor Kp is increased, the system has the following responses:

+ The setting time is little changed

When reducing the scaling factor Kp, the system has the following responses:

+ The setting time is little changed

Figure 5.9 Comparison of scaling factor correction Ki When increasing the integral Ki, the system has the following responses:

When the integral coefficient Ki is reduced, the system has the following responses: + Longer boot time

+ Time to set up many times longer

Figure 5.10 Comparison of scaling factor correction Kd When the differential coefficient Kd is increased, the system has the following responses: + The boot time is little changed

+ The error of setting is little changed

When the differential coefficient Kd is reduced, the system has the following responses: + Faster boot time

Interface

Figure 5.11 Graphic user interface of system

Figure 5.12 Graphic user interface of system

(2) Connect/Disconnect: Connect and disconnect the COM port from the

(3) Setpoint: Select the desired setting angle

(4) 5 deg/10 deg/15deg/20dg: Desired reach angle is available

(5) Up/Down: Increase or decrease the desired setting angle by 1 degree

(6) Real value: Angle set in real time

(7) Setpoint: The desired setting angle

Flowchart of the program

CONCLUSION

Conclusion

The PID algorithm effectively designs a controller for the Fan Positioning System (FPS), ensuring that the actual angle closely matches the desired set angle with minimal deviation The time required to reach the target angle is efficient, contributing to the system's overall stability Despite being susceptible to noise, the PID controller maintains control over the actual deviation angle, achieving the desired outcome promptly while preserving system stability.

The discrepancies between the actual model and simulation results arise from challenges in parameter identification and the impact of environmental factors like friction and noise Despite these differences, the design of the controller and the construction of the FPS model remain unaffected, emphasizing the importance of simulating the PID controller's practical application in FPS systems While the simulation and real-world outcomes differ, the underlying principles of the PID controller apply consistently across both contexts; for instance, increasing Kp leads to greater overshoot, while decreasing Kd results in longer settling times Additionally, the project faces limitations, such as the small desired angle of the plate, which is influenced by the fan's performance, particularly as its power diminishes due to voltage drops when controlled by the H-bridge.

Improvement

Upon completing this project, we recognized its novelty and the potential for further exploration There are opportunities to enhance both the hardware and the controller for improved outcomes Upgrading the plate and fan can facilitate larger angles, increasing survey diversity Additionally, we can develop new controllers, such as SMC or LQR, or combine existing ones to achieve superior performance Furthermore, implementing neural networks could optimize the overall system.

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Tiêu đề	Position Control For Fan And Plate System Using PID Controller
Tác giả	Nguyen Ngoc Phu, Hoang Nam Kon
Người hướng dẫn	M.S Le Thi Thanh Hoang
Trường học	Ho Chi Minh City University of Technology and Education
Chuyên ngành	Automatic and Control Engineering Technology
Thể loại	Graduation Project
Năm xuất bản	2023
Thành phố	Ho Chi Minh City

Định dạng
Số trang	96
Dung lượng	8,14 MB