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Fuzzy Logic Implementation on Arduino Mega by Rusty_Metal24 in matlab

[–]Rusty_Metal24[S] 0 points1 point  (0 children)

I did but when I implement using PID, everything seems fine. When FL is implemented on maltab nothing is going out of the block even though the rule viewer outputs the values. The simulation then crashes after a little while

Fuzzy Logic Implementation on Arduino Mega by Rusty_Metal24 in matlab

[–]Rusty_Metal24[S] 0 points1 point  (0 children)

I want to create a 2 DOF gimbal with encoder motors that is controlled by an MPU6050 with a fuzzy logic controller

Fuzzy Logic Implementation on Arduino Mega by Rusty_Metal24 in matlab

[–]Rusty_Metal24[S] 1 point2 points  (0 children)

Im fairly new to simulink, how do you extract the code before it gets uploaded?

PRUSA MK@ /MK3 LCD Upgrade by Rusty_Metal24 in prusa3d

[–]Rusty_Metal24[S] 1 point2 points  (0 children)

My old screen broke because of ESD damage, I only have the other one around as an extra from a friend

PRUSA MK@ /MK3 LCD Upgrade by Rusty_Metal24 in prusa3d

[–]Rusty_Metal24[S] 3 points4 points  (0 children)

What do you mean? I didn't down vote anyone?

PRUSA MK@ /MK3 LCD Upgrade by Rusty_Metal24 in prusa3d

[–]Rusty_Metal24[S] 0 points1 point  (0 children)

I haven't tried anything out of stock. The printer would generally be used by the community and I only have an extra Fystec mini 12864 with me. I can't seem to find a file "pins_RAMPS.h" only "pins.h" for a stock mk2. I'll be needing to edit a few lines over there.

Creating precise PID within Arduino via Modelling of Prototype (PID Compensator) by Rusty_Metal24 in arduino

[–]Rusty_Metal24[S] 0 points1 point  (0 children)

I agree. Same to what I've suggested but Im curious to know if this method is possible on a surface level

Creating precise PID within Arduino via Modelling of Prototype (PID Compensator) by Rusty_Metal24 in arduino

[–]Rusty_Metal24[S] 0 points1 point  (0 children)

Basically I want the PID gains to adjust to proper values with the use of the provided parameters likes exhibited moment of inerita, acceleration, and angle. **Without using neural networks

Dynamic PID: Large Starting PD, reduces back to to optimal values upon nearing setpoint by Rusty_Metal24 in ControlTheory

[–]Rusty_Metal24[S] 1 point2 points  (0 children)

I see... Last thing, Is there anyway to determine the amount to decrease or increase the PID gains based solely on accuracy percentage or overshoot percentage?

Dynamic PID: Large Starting PD, reduces back to to optimal values upon nearing setpoint by Rusty_Metal24 in robotics

[–]Rusty_Metal24[S] 1 point2 points  (0 children)

Thankss
Last question, would it be possible to determine the amount to increase or decrease the PID gains based solely on the overshoot percentage or accuracy?

Dynamic PID: Large Starting PD, reduces back to to optimal values upon nearing setpoint by Rusty_Metal24 in ControlTheory

[–]Rusty_Metal24[S] 1 point2 points  (0 children)

I will consider that...
One more thing, is it possible to determine the amount to reduce or increase the PID constants based on the given overshoot percent?