1 / 15

Modeling and Controls

Sam Dubin , Charlotte Gill, Ryan Hammerschmitt, Joe Miller. Modeling and Controls. Introduction. Past Objectives Determine vehicle properties for implementation in the simulator Determine motor mappings from voltage to thrust Finish coding the control laws into the Arduino controller

gary
Download Presentation

Modeling and Controls

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Sam Dubin, Charlotte Gill, Ryan Hammerschmitt, Joe Miller Modeling and Controls

  2. Introduction • Past Objectives • Determine vehicle properties for implementation in the simulator • Determine motor mappings from voltage to thrust • Finish coding the control laws into the Arduino controller • Currently • Obtained initial vehicle stability derivatives by linearizing the vehicle around hover • Implemented non linear vehicle dynamics into the simulation • Found initial PID gains to test in Simulink model and code into controller • Added more robustness and safety to motor controller

  3. AUV Dynamics • Changed from linear model to nonlinear model in the translational direction • Modeled • Force includes thruster control forces/moments and drag forces • Allows for a more accurate representation in both the simulation and controller • Rotational direction still modeled by transfer function • More accurate estimation of vehicle properties

  4. PID Controller • PID controller implemented • Takes u, v, depth, and psi commands • KP, KI, and KD gains determined from transfer functions and root locus methods • Feedback • Currently from AUV dynamics • Will eventually come from onboard sensors • Commands will eventually come from autonomy • Possible feed forward control later

  5. PID Controller • Gains Use sisotool to determine gains KP, KI, and KDapproximations Enter these values in PID controller Then use Simulink to further tune response

  6. Simulation Current Expected Commands from Autonomy: Forward velocity, Lateral velocity, Heading, Depth PID Controllers Command Thrusters to Forces/Moments Mapping + - Dynamics States Sensors & Estimation Environment

  7. Simulation PID Control Unit Impulse Input Unity Feedback

  8. Testing • Verifying the vehicle moves as commanded from Matlab • Obtaining trim values for thrusts at hover • Verified robustness of communication at a high data rate Learned • New safety requirements • High sensitivity to commands in water • Necessity for Matlab to give more than single commands

  9. Testing Matlab/ Joystick AUV Thrust Mapping Trim Trust Thrust Commands XBee XBee

  10. Motor Controller • Adding IMU integration • Possibly have automatic vehicle trimming • 5 packet types: • Thruster commands • Heartbeat • Ping/pong • IMU telemetry • IMU calibration

  11. Coding PID in Arduino • Takes in measured states and commands (u, v, psi, z) and the proportional, integral, and derivative gains • Returns control thrust values • The PID controller is coded as KP*error + KI * ∑error + KD* ∆ error • Control thrust values then sent to motor controller as PWM signals

  12. Coding PID in Arduino

  13. Going Forward • Automate pitch & roll trimming from IMU • Implement controller on the Arduino • Experiment with feed forward systems • Compare simulation dynamics with pool testing • Integrate system with Sensing and Autonomy Teams

  14. Competition Readiness…

  15. Simulation

More Related