This page (revision-25) was last changed on 2021-11-08 09:09 by Murray Altheim

This page was created on 2019-12-22 08:01 by Murray Altheim

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PID Controller
PIDController

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It's surprisingly difficult to drive a wheeled robot in a straight line. No two motors have exactly the same performance, and there may also be differences in the floor surface. If we want a robot to follow a more complicated trajectory or to have some indication of where it has traveled, we need to be able to measure how far each wheel has traveled, that is, how many times the left and right wheels have rotated. This is called __[odometry]__.
It's surprisingly difficult to drive a wheeled robot in a straight line. No two motors have exactly the same performance, and there may also be differences in the floor surface. If we want a robot to follow a more complicated trajectory or to have some indication of where it has traveled, we need to be able to measure the distance each motor has traveled, that is, how many times the left and right wheels have rotated. This is called __[odometry]__.
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Besides the distance traveled, we also want to control the motors in such as way that the robot's movements are both accurate and smoothly executed. Regardless of whether the robot is traveling in a straight line, a gradual curve, or turning around, we want to set the left and right motors to operate at a specified speed and be able to accurately maintain that speed.
Besides the distance traveled, we also want to control the motors in such as way that the robot's movements are both accurate and smoothly executed. This means that regardless of whether the robot is traveling in a straight line, a gradual curve, or turning around, we want to set the left and right motors to operate at a specified speed and be able to accurately maintain that speed.
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On a typical robot with a motor on the left and a motor on the right side, to go forward one motor will be going clockwise, the other motor counter-clockwise. It's entirely typical of DC brushed motors to have a difference in performance in each direction, so it's entirely typical that such a robot will not run in a straight line. So having motor encoders and a PID controller is __exactly__ how one fixes this problem.
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See: [PID Rug Bump Challenge|PIDRugBumpChallenge], [Robot Chassis Dynamometer|RobotChassisDynamometer]
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A __Proportional–Integral–Derivative__ or __PID Controller__ is a general-purpose mechanism for regulating the output of any kind of system through the use of a ''feedback loop''. The feedback signal provides an indication of the current output of the system and is used to alter the system so that its output matches the desired value. PID Controllers are used to control oven temperatures and in automobile cruise controls. One of its first industrial uses was as an automated steering system for ships.
A __Proportional–Integral–Derivative__ or __PID Controller__ is a general-purpose mechanism for regulating the output of any kind of system through the use of a ''feedback loop''. The feedback signal provides an indication of the current output of the system and is used to alter the system so that its output matches the desired value. PID Controllers are used in temperature controllers and automobile cruise controls. One of its first industrial uses was as an automated steering system for ships.
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What sets a PID Controller apart from simpler controllers is that it combines three types of __control terms__ to quickly reach and stay at the desired system output value, minimising overcorrection and maximising stability. These three control terms are what forms its acronym: ''Proportional'', ''Integral'', and ''Derivative''. Because of this a PID Controller is also sometimes called a ''three term controller''.
!! How does it work?
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|| Term || What It Does || Considerations
|| Proportional Term | multiplies the error value by a fixed gain value to determine the corrective value influencing the system output (this value is described as a ''proportional'' to the output). | because a "P-only" controller is using the value of the error to generate its corrective value, it will overshoot its target, then undershoot, with smaller and smaller oscillations until the error approaches zero. Unfortunately, as the output of the system oscillates closer and closer to its target the amount of correction also approaches zero, so the controller never gets there.
|| Integral Term | integrates the error value into the correction over time so that it minimises this overshoot and pushes the output towards the target value. | the amount of integral applied to the formula needs to be carefully tuned, otherwise this can create its own form of oscillation. Given this tuning is quite tricky to get right, there is generally some overshoot and therefore oscillation in a PI controller. If the PID controller is used for position control the I term is used; for a motor controller the value is set to 0 since it actually represents the position of the motor if we're sampling at a fixed rate.
|| Derivative Term | projects the rate of change of the corrective value, and reduces its amount as the system nears the target. This allows the controller to move towards its Set Point as fast as possible, with as little oscillation as possible. | the Derivative Term is intended to smooth the operation of a PI Controller, but the value of the Derivative must also be tuned. If either the P or D are too high it can cause the system to oscillate.
What sets a PID controller apart from simpler controllers is that it combines three types of __control terms__ to quickly reach the desired system output, minimising overcorrection and maximising stability. These three control terms are what forms its acronym: ''Proportional'', ''Integral'', and ''Derivative''.
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The more I read about PID controllers the more I found that tuning the control terms is considered something of a black art. There are a number of constants in the formula, and tweaking them is done by trial and error, in other words: guessing. I did
learn one rule of thumb for a PD controller: start at three zeros, then turn up the P until it oscillates, then back off to half that value. Then do the same thing with D.
A __Proportional term __ multiplies the error value by a fixed proportional gain value to determine the corrective value influencing the system output. Because a controller using only a proportional term is using the value of the error to generate its corrective value, it will overshoot its target, then undershoot, with smaller and smaller oscillations until the error approaches zero. Unfortunately, as the oscillation of the system gets closer and closer to its target the amount of correction also approaches zero, so the controller never gets there.
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The __ Integral Term__ integrates the error value into the correction over time so that it minimises this overshoot and pushes the output towards the target value. This requires that the amount of integral applied to the formula be carefully tuned, otherwise this can create its own form of oscillation. Given this tuning is quite tricky to get right, there is generally some overshoot and therefore oscillation in a PI controller.
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!! Slew Rate
A __Derivative Term__ projects the rate of change of the corrective value, and reduces its amount as the system nears the target. This allows the controller to move towards its Set Point as fast as possible, with as little oscillation as possible.
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To add another twist to a robot motor controller, as mentioned above we don't want to tell the motors to go from zero to a projected speed immediately, or from full speed to stopping suddenly, as that would both overly stress the motors and drive system, and cause the robot's movements to be very jerky, perhaps even causing it to crash. We want to gradually ''slew'' the value so that it changes at a maximum rate, called a __slew rate__. This is measured in some measure of motor speed per unit of time.
The more I read about PID controllers the more I found that tuning the Integral and Derivative control terms is considered something of a black art. There are a couple of constants in the formula, and tweaking them is done by trial and error, in other words: guessing.
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For example, when experimenting with a motor controller written in [Python], the motor speed is set as a value between 0.0 and 1.0, where half-speed is 0.5. We might set a slew rate of 0.2 per second, meaning that it would take 5 seconds to go from a stop (0.0) to full speed (1.0).
To add another twist to a robot motor controller, we don't want to tell the motors to go from zero to a projected speed immediately, as that would both overly stress the motors and drive system, and cause the robot's movements to be very jerky. I want to ''slew'' the value so that it changes at a maximum speed, called a __slew rate__.
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All in all, I've now got requirements for a robot motor control that include a proportional term, integral term, derivative term, and a slew rate.
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!! Conclusions
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All in all, we've now got requirements for a robot motor control that includes a Proportional Term, Integral Term, Derivative Term, and a Slew Rate.
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* __[PID Controller|https://en.wikipedia.org/wiki/PID_controller]__ from Wikipedia (note the animation of adjusting P,I,D values in the ''Manual tuning'' section)
* __[Improving the Beginner’s PID – Introduction|http://brettbeauregard.com/blog/2011/04/improving-the-beginners-pid-introduction/]__ by Brett Beauregard
* __[PID for Dummies|https://www.csimn.com/CSI_pages/PIDforDummies.html]__
* __[Going Straight with PID|https://projects.raspberrypi.org/en/projects/robotPID/3]__, from RaspberryPi.org
* [PID Autotune|https://platformio.org/lib/show/3/PID-AutoTune] by Brett Beauregard (C++)
* [Build a PID Controller with Python 2019|https://onion.io/2bt-pid-control-python/] from Onion.io
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* [simple-pid|https://pypi.org/project/simple-pid/] in Python
* [ivPID|https://github.com/ivmech/ivPID] in Python
* [PID controller|https://solderspot.wordpress.com/2014/01/29/pid-controllers-101/], blog post
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This page (revision-25) was last changed on 2021-11-08 09:09 by Murray AltheimTop