Hardware in the Loop Simulation for Unmanned Aerial Vehicles
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1 NATIONAL 1 AEROSPACE LABORATORIES BANGALORE INDIA CSIR-NAL Hardware in the Loop Simulation for Unmanned Aerial Vehicles Shikha Jain Kamali C Scientist, Flight Mechanics and Control Division National Aerospace Laboratories Bangalore, India 4/21/2016 MATLAB EXPO 2016,
2 Introduction 2 Hardware-In-The Loop Simulation (HILS) is a real-time simulation setup, in which the UAV platform is tested in the same way as it is in the real experiment. The motivation is to develop a simulation and testing framework that can be exhaustively used to examine the performance of designed automatic flight control algorithms.
3 Unmanned Aerial Vehicle 3 A mini UAV developed by NAL with surveillance as the main application. Table 1: A mini UAV geometric parameters Parameter A (Military Version) B (Police Version) Length 1.2 m 1.2 m WingSpan 1.6 m 1.9 m Weight 2 kg 2.5 kg Payload <1kg <1kg UAV designed by NAL
4 Six DOF model for UAV 4 In order to develop a 6DOF model of the system, following data is required Aerodynamic data Propulsion data Mass, Centre of gravity, inertia and moment reference point data Geometry data such as wing-span, mean aerodynamic chord and wing surface area. The 1:1 UAV model is subjected to the wind tunnel tests to yield the aerodynamic coefficients. The coefficients are in the look table form and it captures all nonlinearities. Rigid body Equations of Motion are implemented in MATLAB/Simulink.
5 Trimming & Linearization 5 Trimming A nonlinear Least Squares (LS) minimization algorithm is implemented to perform wings level trim. Trim solution is used to start the simulation. Linearization Linearization is performed using central difference method. The linear models are generated at trim point. The linear models are used for control design.
6 HILS framework 6 A HILS framework contains four modules Host PC Real Time Target Machine and Interfaces Autopilot Hardware Ground Control Station
7 HOST PC 7 The UAV aerodynamic model and engine model along with equations of motions, sensor models, hardware interface blocks and control algorithms are developed in Simulink platform. Open loop, model in the loop, software in the loop and processor in the loop simulation models are tested on Host PC. For HILS compiles the 6 DOF model and communicates it to the target machine which is a Matlab real time kernel. Runs the model in target machine. Communicates with target machine through Ethernet.
8 6DOF Simulation model of UAV NATIONAL AEROSPACE LABORATORIES BANGALORE INDIA CSIR-NAL 8
9 Control Algorithm Using embedded real-time target, autocode is generated for the control strategy and is burnt in the micro controller. 9
10 Real Time Target Machine and Interfaces 10 Real time target machine is a real time operating system which meets the timing constraints required for real time applications. I7 Single Board Computer Boots MATLAB real time kernel from CD. Acts as a root complex and Communicates with Spartan 6 FPGA through PCIe bus. Spartan 6 FPGA SPI, I2C, UART, USB, CAN, GPIO, PWMIO, ADC and DAC IP run in Spartan 6 FPGA The IP s are developed in VHDL programming language All hardware signals are isolated from Autopilot using Digital Isolators
11 Autopilot Hardware 11 Focus on low weight with reconfiguration capability. Hardware is realized using programmable systems on chip (PSoC). On board 3 axis accelerometers, 3 axis gyroscope, 3 axis magnetometer, and, a static pressure and temperature sensor. NAL Autopilot Version 3 (APV3)
12 Ground Control station 12 Open source mission planner software is used Used for direct observation and monitoring purposes.
13 HILS Architecture 13 Classified based on the level of fidelity Low fidelity Medium fidelity High fidelity Presentation covers the development of low and medium fidelity HILS architecture
14 Low fidelity HILS 14 Communication between target machine and autopilot hardware is via serial communication. Sensor data (IMU and GPS)are generated at regular interval. PWM signal are normalized and mapped to the flight control parameters. Real Time Target Machine Serial Autopilot board Low fidelity HILS architecture
15 Detail connection diagram 15
16 Communication protocol 16 Real Time Target Machine Autopilot board Check Sum Range Protocol Data Length Payload Check Sum 1 byte 1 byte Data Length 1 byte 1 byte Total Count= Data length(byte)+ 4 bytes
17 17 Flow of code in the Target Machine Receive Data Packet Scaling of Data Decode the Packet Normalization of Data 6 DOF Simulation Model Formation of packet Mapping of Data to Flight Model Send packet through UART/USB
18 18 Flow of code in an autopilot board Mapping of sensor data to the control subroutine Decode & scaling of Data Receive Data Packet via UART Execute Control Subroutine Generate Control Deflections (PWM) Formation of Packet Send packet to Ground Control Station for Visualization Send Packet to Target Machine through UART
19 Snapshot 19 Target Machine Host PC Visualization PC NAL Autopliot
20 20 MATLAB EXPO 2016, 4/21/2016
21 Medium fidelity 21 Sensors data is sent over the interface supported by the autopilot hardware such as I2C, UART. Real Time Target Machine Sensor data Autopilot board Feedback to the flight model in target machine is obtained from the actuator (servos). Actuator (Servos) Medium fidelity HILS architecture
22 22 Block Diagram
23 23 Flow of code in the Target Machine Receiving Analog Voltages Mapping Voltage values to the Flight control parameters Aircraft 6DOF Simulation Model Scaling of sensor data Framing of packet Sending data: IMU through I2C interface GPS through UART interface
24 24 Flow of code in an autopilot board Receive Data Packet via (I2C & UART) Packet Decode & scaling of Data Mapping of sensor data to the control subroutine Encode packet for visualization in Ground Control Station Generate Control Deflections (PWM) Execute Control Subroutine Drive Servo Motors
25 Snapshot 25 Target Machine Host PC Visualization PC Servos NAL Autopliot
26 26 MATLAB EXPO 2016, 4/21/2016
27 Results 27 UAV performing way point navigation
28 Conclusions 28 Design and development of a low and medium fidelity HILS for a mini UAV is presented. The development is based on Model Based Design. These architectures are used to verify the working of control code and sensor fusion algorithms on the autopilot board.
29 Acknowledgements 29 Our sincere thanks to Director, NAL Dr. G. K Singh and his team Dr. C M Ananda and his team Dr Jatinder Singh, Head FMCD Dr Abhay A Pashilkar, Group head, Flight Simulation
30 References C Kamali, Alexander Kale, Development of Six DOF model for Class-I MAV, September, Preliminary test results on 1:1 scale UAV models Report No. HAL/ARDC/UAV/WNT/001, July Alexander Kale, Shikha Jain, C Kamali, Simulation, Modal Analysis and Parameter Estimation of Class-I MAV, October Shikha Jain, C Kamali, Experimental Validation of NAL s Class 1 MAV Simulation Model Using Flight Data, December Software In the Loop Simulation (SILS) for NAL s Class 1/Similar class MAV. 6. Arya, Hemendra,(2010), Hardware-In-Loop Simulator for Mini Aerial Vehicle, Centre for Aerospace Systems Design and Engineering, Department of Aerospace Engineering, IIT Bombay, India. 7. Dongwon Jung and Panagiotis Tsiotras (2007), Modelling and Hardware-in-the-Loop Simulation for a Small Unmanned Aerial Vehicle, AIAA Infotech at Aerospace, Rohnert Park, CA. 8. A. Gholkar, A. Isaacs, and H. Arya, (2004),.Hardware-In-Loop Simulator for Mini Aerial Vehicle, Sixth RealTime Linux Workshop, Nanyang Technological University (NTU), Singapore. 9. Viswanathan S, Guruganesh R, Design of Autopilot for Class I MAV using Classical Control, November 2013.
31 31 Thank You
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