Low-Cost Microcontroller-based Hover Control Design of a Quadcopter

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Procedia Engineering 41 ( 2012 ) 458 – 464

doi: 10.1016/j.proeng.2012.07.198

International Symposium on Robotics and Intelligent Sensors 2012 (IRIS 2012)

Low-Cost Microcontroller-based Hover Control Design of a Quadcopter

Bernard Tat Meng Leong, Sew Ming Low, Melanie Po-Leen Ooi*

School of Engineering, Monash University, Sunway Campus, Bandar Sunway, 46150 Selangor Darul Ehsan, Malaysia

Abstract

In this paper, a low-cost hover control mechanism is developed and implemented on low-cost microcontroller for a type of aircraft configuration known as the quadcopter. Flight control becomes simpler as the quadcopter hovers at a constant level from ground by itself, at the same time allowing anyone to easily maneuver it at that height and perform tasks such as imaging. When successfully implemented, the proposed hover control design will simplify the flight control of a quadcopter, especially for beginners and unskilled individuals.

© 2012 The Authors. Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the Centre of Humanoid Robots and Bio-Sensor (HuRoBs), Faculty of Mechanical Engineering, Universiti Teknologi MARA.

Keywords: hover control; quadcopter; microcontroller.

1. Introduction

The hover stability of a quadcopter is important for many of its applications such as security surveillance, crop monitoring and on-board imaging to allow clear still images to be taken in surveillance operations [1]. It also prevents the quadcopter from crashing in the event of strong wind or due to its weight. Fig. 1 shows the six degrees of freedom of the quadcopter. In Fig. 1(a), x and y represents the translational motion along the x- and y-axes respectively and represents yaw, the rotational motion about the z-axis, while in Fig. 1(b), represents roll, the rotational motion about the x-axis, represents pitch, the rotational motion about the y-axis and z represents the translational motion in the direction perpendicular to ground. The label ‘1’ signifies the front propeller.

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Fig. 1 The six degrees of freedom of a quadcopter at (a) Bird’s eye view, (b) Frontal view

With a hover control unit, the quadcopter will hover at a constant height z (see Fig. 1(b)), with its roll and pitch angles stabilised by the gyroscope. The person at the command base will only need to control the quadcopter’s motion along the x- and y-axes and also its rotation about the z-axis (to turn corners), reducing the degree of complexity from six to only three.

This defines the purpose and aim of this paper, which is to obtain a stable quadcopter hover that will last for at least 5 minutes with an acceptable error of within of the hover altitude.

* Corresponding author. Tel.: +6-035-514-6238; fax: +6-035-514-6207.

E-mail address: [email protected]

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A significant number of research works have been performed to obtain a mathematical model which describes the quadcopter flight dynamics [2][3][4][5]. As a result, controller boards have been developed and mass produced to be sold in the market. A few notable off-the-shelf controller board developers are KKmulticopter, Hoverfly and DJI Naza, which usually have good performance but can be costly (up to USD 260 [6]).

This paper proposes a Hover Control System which will operate on a low cost microcontroller board, Arduino Duemilanove, which costs USD 24[7]. As opposed to the controller boards available in the market, the implementation of the Arduino board in this research does not require the original quadcopter gyroscope to be removed. This makes the proposed prototype portable as it can be installed in other quadcopter units without affecting their existing operation.

Successful implementation of the proposed prototype hover controller will enable the quadcopter hover control to be achieved at approximately 10% of the current controller market price in addition to being customisable to suit user’s needs.

1. Control Theories 1.1. PID Control

The PID (Proportional-Integral-Derivative) control algorithm has been considered and implemented in literature to control the hover altitude of the quadcopter [8][9]. PID control is a type of linear control that is widely used in the robotics and automation industry [10]. The PID algorithm is popularly used mainly because [11]:

• It has a simple structure

• It provides good performance

• It can be tuned even if the specific model of the controlled plant or system is not available

The last factor is important as it was not easy to obtain the exact mathematical model of the quadcopter [11]. A PID controller functions by calculating the error, or difference between a measured output and a desired setpoint and adjusts the system control inputs such that the calculated error is minimised. The PID algorithm consists mainly of three control parameters, P – Proportional, I – Integral and D – Derivative. The mathematical expression of the discrete-time PID algorithm is given in (1). P determines the reaction to the current error, I determines the reaction based on a sum of recent errors while D responds to the rate at which the error has been changing. Table 1 summarizes the effects of increasing these parameters to the system.

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=

−

− +

+

= ^k

i

D I

Pek K ei K ek ek

K k u

0

) 1 ( ) ( ) ( )

( )

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Table 1. Effects of Independent P, I and D Tuning [11]

Closed Loop Response Rise Time Overshoot Settling Time Steady-State Error Stability Increasing Kp Decrease Increase Small

Increase Decrease Degrade

Increasing Ki Small

Decrease Increase Increase Large Decrease Degrade

Increasing Kd

Small

Decrease Decrease Decrease Minor Change Improve

1.2. Linear Quadratic Regulator

Calculation of the control input by control algorithms such as PID control may return a control input gain which may be too high for the quadcopter system. This results in a large control input magnitude which may be out of the limits recognisable by the system. To solve this problem, the linear quadratic regulation (LQR) method was employed. LQR is a form of linear optimal control regulation which aims to reduce the magnitude of the control input without affecting the performance of the control algorithm [12]. The LQR algorithm is used to obtain the parameter settings that will minimise the undesired deviations (in this research, altitude) while at the same time limiting the energy expended by the control action by using a mathematical algorithm that minimises a cost function or performance index with weighting factors. The cost function or performance index refers to the sum of deviations of measured values from its desired values [12]. For a discrete-time LQR, the performance index is defined as (2). By adjusting the weight parameters Q and R, the optimal control sequence that minimises the performance index is given by (3).

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= ^N

k

k T k k T

kQx u Ru

x J

0

(2)

(3)

−1

−

= k k

k F x

u (3)

2. Hover Control System Design

The hover control system consists of three major subsystems: quadcopter, sensor and microcontroller. The quadcopter subsystem forms the physical system that requires control while the sensor subsystem provides altitude information to the control system. The microcontroller subsystem combines the electronic signals with the hardware in the hover control routine, thus achieving automatic hover control.

2.1. Quadcopter subsystem

The quadcopter model used in this research is the GAUI 330-X Quadflyer. The quadcopter subsystem consists of a receiver module, a gyroscope unit, electronic speed controller (ESC) circuits, propeller DC motors and a Lithium-Polymer battery. Fig.2 shows the components of the quadcopter subsystem. The command signal is a pulse-width modulated (PWM) signal that is received by the receiver module. The gyroscope unit will modify this signal by incorporating the attitude (roll, pitch and yaw angles) information before transmitting the signal to the ESC circuits. This modification is performed to keep the quadcopter’s chassis level with respect to ground. The ESC modules functions to smoothen the speed variation of the DC motors.

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Fig. 2 Components in the Quadcopter Subsystem

2.2. Microcontroller (MCU) Subsystem

The Arduino Duemilanove MCU is placed in between the receiver and the gyroscope module (see Fig.2) to manipulate the throttle command signal for hover control, based on the height information provided by the sensor subsystem. The main function of the MCU is to obtain altitude readings from the sensor module and determine if hover control mode is asserted.

If hover control mode is on, the PID control algorithm would be executed by the MCU.

2.3. Sensor Subsystem

The sensor subsystem functions to provide the altitude information to the microcontroller subsystem. In this research, the PING))) Ultrasound Sensor, a low-cost ultrasound sensor unit developed by Parallax Inc. was used. The height information is transmitted by the sensor module to the MCU in terms of a PWM signal pulse width, and the actual altitude can be computed using the formula given in [13].

2.4. Integration of Subsystems

To integrate the subsystems into a complete system, the microcontroller subsystem is used as the common interfacing point for all hardware and subsystems. Fig. 3 illustrates the integration of the hardware and subsystems to form the hover control system.

When hover control is activated, the ultrasound sensor captures the current altitude as the reference point. The flight altitude is then continuously monitored and communicated to the controller board. The PID control algorithm programmed into the MCU determines the throttle control signal that is transmitted to the gyroscope module based on the error computed in (1). Depending on the throttle control signal, the propeller speed is either increased or decreased to bring the quadcopter altitude to the reference level.

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Fig. 3 Component and hardware design of the hover control unit 3. Results and Discussion

3.1. Software Simulation

A model of the quadcopter’s dynamics along the z-axis is empirically obtained by performing system identification on the input-output data. It was found to be closely modeled by (4). The step responses of the model using different PID tuning parameters were simulated using MATLAB Simulink. Three types of control were tested and evaluated: P (Proportional), PI (Proportional-Integral) and PID (Proportional-Integral-Derivative). The simulation results, with the corresponding tuning parameters are displayed in Fig. 4(a), (b) and (c). Table 2 summarizes the results obtained from software simulation.

0025 . 1 0228 . ) 0

( = +

z z

G (4)

0 5 10 15 20 25 30 35 40 45

0 0.5 1 1.5 2

Plot of System's Step Response

Time (s)

Altitude (m)

0 5 10 15 20 25 30 35 40 45

-2 0 2 4 6

Time (s)

Control Input Magnitude

Reference System Response

0 5 10 15 20 25 30 35 40 45

0 0.5 1 1.5 2

Time (s)

Altitude (m)

0 5 10 15 20 25 30 35 40 45

-2 0 2 4 6

Time (s)

Reference System Response

(a) (b)

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0 5 10 15 20 25 30 35 40 45 0

0.5 1 1.5 2

Time (s)

Altitude (m) _Reference

System Response

0 5 10 15 20 25 30 35 40 45

-10 0 10 20 30

Time (s)

(c)

Fig. 4 The system response for (a) Proportional Control, P = 3.1557,; (b) Proportional-Integral Control, P = 3.1557, I = 1.0; (c) Proportional-Integral- Derivative Control, P = 7.0, I = 4.0, D = 1.0

Table 2. Software Simulation Results

Control Type Settling Time (s) Overshoot (%) Steady State Error (cm)

Proportional (P) 5.7 0 5.6

Proportional-Integral (PI) 12.4 22.81 0

Proportional-Integral-Derivative (PID) 7.3 17.9 0

Simulation results show that the PID control returns zero steady state error with lower settling time and overshoot compared to P and PI control. In this research, it is desirable to have a low overshoot percentage and settling time with zero steady state error, thus the PID control is selected.

3.2. Hardware Implementation

The software simulation provided a good platform to understand the effects of the control parameters on the system.

However, a actual hardware implementation using the software-obtained parameters showed unstable hovering of the quadcopter. This could be caused by inaccurate mathematical modelling of the actual quadcopter system. Thus an empirical method was developed to empirically obtain the control parameters. The tuning of the control system was performed based on the theoretical control effects summarised in Table 3. The steps involved in the empirical approach are shown in Fig.5.

Set an initial Kp

(Ki and Kd = 0)

Large Overshoot?

Stable?

Steady State Error?

Optimum Control Achieved

Increase Ki

Decrease Kp or Increase Kd

Increase Kd

Yes

Yes No

No

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Fig. 5 Empirical Methodology to Obtain Control Parameters

Using the empirical approach in Fig 5, the optimal P, I and D values were found to be 0.005, 0.01 and 0.003 respectively.

Using a 3-axis accelerometer, the stability of the quadcopter can be evaluated. Fig. 6 displays the acceleration profile obtained in the hardware implementation test. The new acceleration profile shows that the quadcopter’s hover is stable at the reference altitude, thus achieving stable hover control.

0 200 400 600 800 1000 1200

-2 0 2

Acceleration Profile (x-dimension)

Sample Number

Acceleration, m/(s2)

0 200 400 600 800 1000 1200

-1 0 1

Acceleration Profile (y-dimension)

Sample Number

0 200 400 600 800 1000 1200

9 10 11

Acceleration Profile (z-dimension)

Sample Number

Fig. 6 Acceleration Profiles in the x-, y- and z- dimensions

4. Conclusion

The quadcopter configuration generally has a greater stability as compared to the other configurations. In addition to that, the quadcopter is able to hover close to its target, unlike its other counterparts. However, the hovering of the quadcopter can be unstable, making it difficult to navigate and be used in precise monitoring operations. The purpose of this research was to address this hover stability issue and develop a hover control system that is not only low cost, but fully customisable to suit its users’ needs. The results of the hardware implementation show that the quadcopter has a stable hover with an error of ±2 cm for a total flight time of 5 minutes with a total implementation cost of USD 24.

Acknowledgements

The authors would like to thank Monash University for their support and resources to conduct this research, and Mr.

Khoo Boon How for provision of the use of his data acquisition module.

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