Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/nikolasent/pid-controller

Udacity Self-Driving Car Engineer Nanodegree. Project: PID controller
https://github.com/nikolasent/pid-controller

carnd pid pid-control pid-controller self-driving-car

Last synced: 7 days ago
JSON representation

Udacity Self-Driving Car Engineer Nanodegree. Project: PID controller

Awesome Lists containing this project

README 

        
# PID controller

This Project is the ninth task (Project 4 of Term 2) of the Udacity Self-Driving Car Nanodegree program. The main goal of the project is to implement PID controller in C++ to steer the self driving car around the track in a [Simulator](https://github.com/udacity/CarND-PID-Control-Project/releases) v. 0.1.1.



The project was created with the Udacity [Starter Code](https://github.com/udacity/CarND-PID-Control-Project).



## About PID controller



A PID (Proportional, Integral, Derivative) controller is a control loop feedback controller which is widely used in different control systems.



Error is an input variable for the controller:



_cte = desired_state - measured_state_



With the proportional band (_P_) only, the PID controller output is proportional to the cte. It takes into account only the present value of cte. Thanks to this part of controller, it is able to steer in the correct direction.



Integral term (_I_) takes into account the integral of cte over the past time. It is used to reduce systematic bias. From my experianse of PID tuning on real-world systems and some experiments in the simulator, it was found out that we do not need to compute the integral for all time, but calculate it over only last n frames. In other case, we can accumulate errors because of left turns prevalens which can result in different behaviour of the controller during multi lap drive and even going off the track. A buffer was implemented for the purpose using the code from [stackoverflow](https://stackoverflow.com/questions/25024012/running-sum-of-the-last-n-integers-in-an-array).



With derivative (_D_) part, the controller output is proportional to the rate of change of cte (its derivative). The parameter is used to reduce overshooting and dump oscillations caused by the _P_.



While the PID controller is easy to implement, but it is not so easy to tune.



## How it was tuned



Initially, the controller was tuned with so-called [Ziegler–Nichols method](http://staff.guilan.ac.ir/staff/users/chaibakhsh/fckeditor_repo/file/documents/Optimum%20Settings%20for%20Automatic%20Controllers%20(Ziegler%20and%20Nichols,%201942).pdf). Generally speaking, it requires to set _Kd_ and _Ki_ to 0 and gradually increase _Kp_ before the car runs with stable and consistent oscillations. This value of _Kp_  and the oscillation period can be used to calculate optimal pid controller parameters by the [method](http://www.mstarlabs.com/control/znrule.html). Unfortunatly, the controller with resulted parameters was able to drive car around the track but with a lot of wobbling. That is why, parameters were further tuned manually by try-and-error process.



The same process was applied for differnt speed, so different PID parameters were found for different speed. The results were linearized The same process was applied for different speed, so different PID parameters were found for different speed. The results were linearized in order to make the parameters automatically tune with the car speed variation.



The implementation also takes into account different time intervals between data frames.



## Dependencies



* cmake >= 3.5

 * All OSes: [click here for installation instructions](https://cmake.org/install/)

* make >= 4.1

  * Linux: make is installed by default on most Linux distros

  * Mac: [install Xcode command line tools to get make](https://developer.apple.com/xcode/features/)

  * Windows: [Click here for installation instructions](http://gnuwin32.sourceforge.net/packages/make.htm)

* 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](http://www.mingw.org/)

* [uWebSockets](https://github.com/uWebSockets/uWebSockets) == 0.13, but the master branch will probably work just fine

  * Follow the instructions in the [uWebSockets README](https://github.com/uWebSockets/uWebSockets/blob/master/README.md) to get setup for your platform. You can download the zip of the appropriate version from the [releases page](https://github.com/uWebSockets/uWebSockets/releases). Here's a link to the [v0.13 zip](https://github.com/uWebSockets/uWebSockets/archive/v0.13.0.zip).

  * If you run OSX and have homebrew installed you can just run the ./install-mac.sh script to install this

* Simulator. You can download these from the [project intro page](https://github.com/udacity/CarND-PID-Control-Project/releases) in the classroom.



## Basic Build Instructions



1. Clone this repo.

2. Make a build directory: `mkdir build && cd build`

3. Compile: `cmake .. && make`

4. Run it: `./pid`. 



## 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](https://google.github.io/styleguide/cppguide.html).



## 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.