[DPRG] April 2009 Meeting Minutes & PID and Beyond Presentation
Summary
Subject: [DPRG] April 2009 Meeting Minutes & PID and Beyond Presentation
Summary
From: DeltaGraph at aol.com
DeltaGraph at aol.com
Date: Mon Apr 13 04:30:28 CDT 2009
DPRG Monthly Meeting Minutes 4/11/09
Present: Ron Grant, Paul Bouchier, John Swindle, Will Kuhnle, Brian Pike,
Drew Dolan, Ed Paradis, Glenn Pipe
There was no March meeting  the club attended AllCon instead.
Notes
 Ron showed his audiooutput from motor ramp generator, and his robot
which lets you play with PID parameters
 Ron described how Doug brought the club $1500 (from Verizon donations),
and he asked people to think about whether they would support the idea of
approving Doug's desire to guide it's application. Doug is excited about
YARB, and would like the club to buy one.
 Ron described YARB (Yet Another Robotic Blimp)  $350 for blimp OR $700
for blimp & blackfin robot + camera (discount from $900)
 There's a Blimpduino  around $100, but doesn't carry much payload
 Paul suggested maybe the club should perhaps auction off the robomower
to offset blimp cost. Discussion of need to find out whether the donor is
amenable to that kind of use of the donation. We need to get in touch with
the donor. Question: what laws or rules apply to who can participate in such
an auction. Paul got action to find out from Steve Rainwater who donated
it, and what rules apply to any such disposal (e.g. 501c rules etc).
 Ed mentioned Akon  2 hour presentation
 Ron said Tanner Electronics wants DPRG to do a robot day in October or
November  dual purpose  people can shop & support the demo. Tanner has
offered to let us show DPRG info on the wall. People liked both of those
ideas.
 Ron won't be here next month's meeting, Glenn (VicePresident) is
responsible for organizing next month's meeting.
 Will presented a talk on PID (proportional, integral, derivative)
PID AND BEYOND
==============
A presentation on control systems, by Will Kuhnle
[Note: We Expect to Have Will's Slides Published to DPRG.ORG]
 Will handed out notes for the talk, & pointed out: if K & G are large,
the output essentially becomes equal to the output, because KG / KG
approaches 1
 As an example, the F16 is unstable without feedback control
 Feedback permits greater tolerance on control components G
 The dotted line in the speed/torque graph shows that as load increases
speed decreases
 Servos may not compensate for noise in the system  e.g. bumps on the
floor
 Will showed his propellerdriven pendulum.The axes of the feedback time
delay graph are: X: ms, Y: voltage applied to propeller. He found that
thrust is almost linear with current, but speed is not.
 The voltage constant & torque constant are related. The time constant of
the motor/propeller is around 50  60ms
 Will emphasized the importance of thinking about what you're trying to
actually control, when designing a control system. Also, you can't control
what you can't measure  be sure you can measure what you're trying to
control, and ask is it the right variable to control, to meet your goals?
 You may want to switch what you're controlling over time. E.g. when
sending a robot to a target, you may want to control speed for much of the
travel, so as not to spin the wheels due to saturated control system, but when
the distance becomes less than some value, you may want to switch to
controlling distance to the target. Will's list of variables that can be
controlled on a 2wheel driven robot shows the wide range of variables that can be
controlled, perhaps at different points in time. It really is important to
think about what you want to control.
 Philosophical question from Ed: is there some physical reality to the
integral of distance with respect to time?  Unanswered.
 Will showed that a motor that's part of a position servo can be modeled
as a mass on a spring, which will oscillate in the absence of friction. The
derivative term acts like friction on the mass + spring assembly.
 Will explained why you need the integral term, by using an analogy with
2 tanks of water connected by a pump that pumps water from tank 1 to tank
2. If you're trying to control the level of tank 2, the pump could simply
run until the water is at the right level. But now if you start draining
water out of tank 2 at a steady rate, there will be an error in the desired
level, because the pump won't run until there is an error, and the error will
remain, as long as you're draining water out of tank 2. Anytime you have to
supply power to hold the desired value, you need integral, otherwise
you'll have a constant error.
 On a position servo, you need derivative control, to stop it from
oscillating. But on a speed controlled servo, you don't any derivative, because
when you get to the desired speed, you can just stop applying power. This is
because there's only a single integral in the controlled system  it
doesn't have "momentum" that will cause it to tend to overshoot in speed. So
it's really important in robotics to know what you're trying to control  if
you're controlling speed, you don't need any derivative, but if you're
trying to control position, you do.
 Discussion of a bode plot  it's the output of the process (e.g.
position of a motor) with the controlloop feedback removed, when the input is
driven by a sinusoid. The point where the motor starts to fall behind the input
sinusoid is the time constant of the motor  typically ~30  60ms for
small motors. You need to have a gain of less than 1 at the point where the
frequency response on the bode plot drops from flat to the 20dB/octave falloff.
 Servo motors are designed to have a high torque to inertia ration  this
is why they tend to be long skinny motors to keep inertia low (because
inertia varies as the square of diameter of the rotor).
 When you're changing the amount of integral or derivative you're
changing the frequency on the Bode plot at which the response changes.
 New techniques: There's an alternate approach to control systems from
PID  the State Space approach. PID is a technician's approach  tune it
until it works. State space is a different approach to how you design a servo
system. You feed in position, velocity, acceleration, jerk in different
amounts. PID is a mere combination of the derivatives. In state space, you
have a matrix of variables, but we didn't get a real good explanation of it.
 There's a difference between state space & Kalman filter  in a kalman
filter you can't measure all the states, so you come up with a math model of
the system. If you have a perfect model, you can get your states out of
the model. But your model isn't going to be perfect. So you measure system
output & feed it back into the math model as a correction. A Kalman filter is
a state space estimator.
 Discussion of types of system. System type is the number of pure
integrators in the system: a type 1 system has 1 integrator.
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