huangvictorauto / carnd-pid-control-project

CarND-Controls-PID

Self-Driving Car Engineer Nanodegree Program

Overview

This project was to using a PID controller to control a car to run in a track in the Udacity Simulator.The Simulator give the CTE(cross track error),speed,and driving angle to the controller.The steer value was calculated using the PID control and was fed into the simulator.The Kp,Ki,Kd parameter was tuned manualy and the car run inside the track sucessfully.

Rubric

Compilation

Your code should compile.

The Code compile without errors with cmake and make.

Implementation

The PID procedure follows what was taught in the lessons.

The PID controller was used like what was taught in the lessons in pid.cpp. The update_error function calculate the proportional, integral and derivative errors using cross track error and total_error function calculates the total error using the appropriate coefficients.

Reflection

Describe the effect each of the P, I, D components had in your implementation.

The proportional term produces an output value that is proportional to the current error value. The proportional response can be adjusted by multiplying the error by a constant Kp, called the proportional gain constant.In the implementation, the P can adjust the car fast with propotional to the CTE. But with the kP increase,it will cause oscillation.

The contribution from the integral term is proportional to both the magnitude of the error and the duration of the error. The integral in a PID controller is the sum of the instantaneous error over time and gives the accumulated offset that should have been corrected previously. The accumulated error is then multiplied by the integral gain (Ki) and added to the controller output.In the implementation, the I was not used as the system has no significant bias error accumulated through time.

The derivative of the process error is calculated by determining the slope of the error over time and multiplying this rate of change by the derivative gain Kd. The magnitude of the contribution of the derivative term to the overall control action is termed the derivative gain, Kd. Derivative action predicts system behavior and thus improves settling time and stability of the system.In the implemantaion, the D was used to smooth the oscillation cause by P and reached the goal more quickly.

Describe how the final hyperparameters were chosen.

The PID value was tuned manually by me. The intial value for Kp, Ki, Kd selected using trial and error method. First I set Ki and Kd values to zero and increase proportional term (Kp) until system reaches to oscillating behavior.I reache the Kp as 0.15. Then Kd was tuned to reduced oscillation.The Kd was reached to 3. With the Kp=0.15,Ki=0.0,Kd=3.0, the car can already drive through the track in the simulator without problem.So the parameter was finaly chosen.

Simulation

The vehicle must successfully drive a lap around the track.

No tire leave the drivable portion of the track surface. The car did not pop up onto ledges or rolled over any surfaces that would otherwise be considered unsafe (if humans were in the vehicle).

Dependencies

• cmake >= 3.5
• All OSes: click here for installation instructions
• make >= 4.1(mac, linux), 3.81(Windows)
  • Linux: make is installed by default on most Linux distros
  • Mac: install Xcode command line tools to get make
  • Windows: Click here for installation instructions
• gcc/g++ >= 5.4
  • Linux: gcc / g++ is installed by default on most Linux distros
  • Mac: same deal as make - [install Xcode command line tools]((https://developer.apple.com/xcode/features/)
  • Windows: recommend using MinGW
• uWebSockets
  • Run either ./install-mac.sh or ./install-ubuntu.sh.
  • If you install from source, checkout to commit e94b6e1, i.e.

    git clone https://github.com/uWebSockets/uWebSockets 
    cd uWebSockets
    git checkout e94b6e1
    

    Some function signatures have changed in v0.14.x. See this PR for more details.
• Simulator. You can download these from the project intro page in the classroom.

Fellow students have put together a guide to Windows set-up for the project here if the environment you have set up for the Sensor Fusion projects does not work for this project. There's also an experimental patch for windows in this PR.

Basic Build Instructions

1. Clone this repo.
2. Make a build directory: mkdir build && cd build
3. Compile: cmake .. && make
4. Run it: ./pid.

Tips for setting up your environment can be found here

Editor Settings

We've purposefully kept editor configuration files out of this repo in order to keep it as simple and environment agnostic as possible. However, we recommend using the following settings:

• indent using spaces
• set tab width to 2 spaces (keeps the matrices in source code aligned)

Code Style

Please (do your best to) stick to Google's C++ style guide.

Project Instructions and Rubric

Note: regardless of the changes you make, your project must be buildable using cmake and make!

More information is only accessible by people who are already enrolled in Term 2 of CarND. If you are enrolled, see the project page for instructions and the project rubric.

Hints!

• You don't have to follow this directory structure, but if you do, your work will span all of the .cpp files here. Keep an eye out for TODOs.

Call for IDE Profiles Pull Requests

Help your fellow students!

We decided to create Makefiles with cmake to keep this project as platform agnostic as possible. Similarly, we omitted IDE profiles in order to we ensure that students don't feel pressured to use one IDE or another.

However! I'd love to help people get up and running with their IDEs of choice. If you've created a profile for an IDE that you think other students would appreciate, we'd love to have you add the requisite profile files and instructions to ide_profiles/. For example if you wanted to add a VS Code profile, you'd add:

• /ide_profiles/vscode/.vscode
• /ide_profiles/vscode/README.md

The README should explain what the profile does, how to take advantage of it, and how to install it.

Frankly, I've never been involved in a project with multiple IDE profiles before. I believe the best way to handle this would be to keep them out of the repo root to avoid clutter. My expectation is that most profiles will include instructions to copy files to a new location to get picked up by the IDE, but that's just a guess.

One last note here: regardless of the IDE used, every submitted project must still be compilable with cmake and make./

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