Autotune feature usage.

[bigbread59]

Hi all,

I am trying to use HASmartThermostat, unfortunately with unsatisfactory results so far.

I can't find the right kp, ki and kd parameters manually.

I have made a few attempts, sometimes the temperature does not reach the set point, other times it goes far beyond (diverges).

I have tried to use the auto_tuning function but I have the following problems.

1)
After setting up the following configuration

climate:

• platform: smart_thermostat
  #entity_id: smart_thermostat_PP
  name: PID_Thermostat
  heater: switch.contact_clean_thermostat
  target_sensor: sensor.xiaomi_wsdcgq11lm_002_temperature
  precision: 0.1
  min_temp: 15
  max_temp: 21
  ac_mode: False
  target_temp: 17
  keep_alive:
  seconds: 60
  away_temp: 14
  kp: 1
  ki: 5
  kd: 20
  pwm: 00:15:00
  sampling_period: 00:10:00
  autotune: "pessen-integral"

I restart HA, (the ambient temperature is about 19 ° C)
I set the temperature setpoint at 21 ° C

At this point (it seems) nothing happens: the heater control switch remains off so the heating does not start.
Am I wrong something?

2)
I'm not very familiar with HA, how do I check the autotune_status attribute for success ?

Many thanks

[ScratMan]

Hello, in the latest 2021.11.8-beta4, the autotune doesn't need a step, it will directly use the set point that is set a few temperature samples after the reboot. You may use a set point aligned with your ambient temperature at time of restarting HA, for example, if your thermostat is set to 18°C but the ambient temperature is 19.7°C before restarting, you could set 19°C, then restart and let the autotuner start with this 19°C as target. It will start with a step-down, so waiting for the temperature to drop below set-point minus the noise band before switching on the heater.

I would recommend you to set the noiseband setting according to your sensor's measurements rate and accuracy :

• if the sampling rate of the sensor is fast (few seconds) and temperature moves quickly up and down (fast heating system and/or low insulation in the room), the noiseband should be higher (keep 0.5)
• if the sampling rate of the sensor is low (refreshing temperature after several minutes) or temperature moves slowly (high inertia, like floor heating, with good insulation), you should set a lower noiseband (for example 0.2), otherwise the temperature oscillation period will be very long.

The lookback parameter is important too, I used lookback set between 6 and 10 hours while simulating autotuning with some data exported by a user of the thermostst, and it worked well for the autotuner, the temperature oscillation period was around 2 hours on his heating system, and the gains computed by the autotuner were quite good after the first testing he did.

You can check the status of the autotuner by using the "Development tools" in HA, in the "States" section. You can select your climate entity and its state and attributes will be shown, you will see the autotune_status attribute here and new Kp, Ki and Kd if the autotune succeeded (it will also be shown in the log, with different gains computed with all the tuning rules).
Or you can add sensor templates in your configuration file to directly create some sensors monitoring the attributes you want, like this example that monitors the PID outputs :

sensor:
  - platform: template
    sensors:
      smart_thermostat_output:
        friendly_name: PID Output
        unit_of_measurement: "%"
        value_template: "{{ state_attr('climate.your_entity_id', 'control_output') | float }}"
      smart_thermostat_p:
        friendly_name: PID P
        unit_of_measurement: "%"
        value_template: "{{ state_attr('climate.your_entity_id', 'pid_p') | float }}"
      smart_thermostat_i:
        friendly_name: PID I
        unit_of_measurement: "%"
        value_template: "{{ state_attr('climate.your_entity_id', 'pid_i') | float }}"
      smart_thermostat_d:
        friendly_name: PID D
        unit_of_measurement: "%"
        value_template: "{{ state_attr('climate.your_entity_id', 'pid_d') | float }}"

Don't hesitate to share you results

[bigbread59]

Thanks for the quick reply!

I have installed version 2021.11.7 via HACS.

How can I upgrade to version 2021.11.8-beta4?

I'm sorry but I'm not very familiar with HA.

[ScratMan]

You need to reinstall and activate the "show beta releases" toggle switch.

[bigbread59]

I started the autotune function last night at 6PM (Italian time) with the following settings:

---

climate:

• platform: smart_thermostat
  name: PID_Thermostat
  heater: switch.contatto_pulito_termostato
  target_sensor: sensor.xiaomi_wsdcgq11lm_002_temperature
  precision: 0.1
  min_temp: 15
  max_temp: 21
  ac_mode: False
  target_temp: 17
  keep_alive:
  seconds: 60
  away_temp: 14
  kp : 1
  ki : 5
  kd : 10
  pwm : 00:15:00
  sampling_period : 00:10:00
  autotune: "pessen-integral"
  lookback: 10:00:00
  noiseband: 0.2

---

I set the lookback parameter (Optional): to 10:00:00
but in 10 hours I won't have 5 cycles.
What should I do?

I have this situation at the moment:

hvac_modes:

• heat
• 'off'
  min_temp: 15
  max_temp: 21
  preset_modes:
• none
• away
  current_temperature: 19.1
  temperature: 19
  hvac_action: idle
  preset_mode: none
  control_output: 0
  pid_p: 0
  pid_i: 0
  pid_d: 0
  autotune_status: relay step down
  autotune_sample_time: 210.86558151245117
  autotune_tuning_rule: pessen-integral
  autotune_set_point: 19
  autotune_peak_count: 0
  autotune_buffer_full: 0.21
  autotune_buffer_length: 171
  Kp: 1
  Ki: 5
  Kd: 10
  friendly_name: PID_Thermostat
  supported_features: 17

---

I can see that that
autotune_peak_count: 0
Is this a normal value?

[ScratMan]

Hi, everything looks normal at the moment. The buffer is 21% filled, once completely filled it will analyze the data. The documentation is wrong regarding cycles, the autotune needs 1.5 periods of the "sine" wave, and there is near 1 period visible. I think you'll be fine in a few hours. In the log of HA you will see all the gains computed with all tuning rules if you want to try different options afterward.

[bigbread59]

While I'm waiting for the autotuning to finish (autotune_buffer_full: 0.85), I'm playing around with the https://pidtuner.com/ tool.

I loaded the recorded data during the autotuning time.
If you are curious you can take a look at the simulation:
https://pidtuner.com/#/fxJLv8GUQZ

[bigbread59]

The autotune procedure is still running since
11/22/2021 at 6pm.

On 11/25/2021 around 6 pm it occurred
autotune_buffer_full: 1.
However, the procedure is still running by cycling from
autotune_status: relay step down
to
autotune_status: relay step up

The recorded cycles are attached.

Questions:

1. Is this behavior normal?
2. Do I still have to wait finish the autotune procedure?
3. Is there a way to safely abort the procedure without restarting the HA and without losing the acquired data?
4. Do you have some suggestions?

---

CURRENT STATUS
`hvac_modes:

• heat
• 'off'
  min_temp: 15
  max_temp: 21
  preset_modes:
• none
• away
  current_temperature: 19.4
  temperature: 19
  hvac_action: idle
  preset_mode: none
  control_output: 0
  pid_p: 0
  pid_i: 0
  pid_d: 0
  autotune_status: relay step down
  autotune_sample_time: 210.86558151245117
  autotune_tuning_rule: pessen-integral
  autotune_set_point: 19
  autotune_peak_count: 0
  autotune_buffer_full: 1
  autotune_buffer_length: 171
  Kp: 1
  Ki: 5
  Kd: 10
  friendly_name: PID_Thermostat
  supported_features: 17

`

[ScratMan]

I think that the temperature oscillation is not regular enough for the Autotune to detect the peaks correctly as the min and max are always changing in the buffer.

If you use an external SQL database to store data, like InfluxDB or MariaDB you can export the data to CSV using its web interface.
Otherwise it seems it's possible to open the HA database with a web browser to make some requests, but I don't know how to.

It would also possible to compute the approximate gains by hand.

[bigbread59]

Thank you very much!

What data do you need?
Are the following data enough?

• timestamp
• temperature
• output (0.0 ... 100.0)

Once I have the data, how can I analyze them?
Is there an offline tool?

It would also possible to compute the approximate gains by hand.

Very interesting!
How you do it?

[ScratMan]

Hello,

If autotune fails (it really depends on the regularity of the oscillation), you can extract the required information from the temperature history manually (and you can do it as soon as you have a full period of oscillation, while the autotune often needs to wait several cycles) :
Measure the amplitude of the oscillation Yosc (in degrees) and the oscillation period tosc (in seconds).

This will allow computing the ultimate gain Ku:
Ku = 8.0 * difference / (Yosc * pi)
(difference = 100 by default)

Next, depending on the autotune rule you prefer (it provides different results) you can compute the gains Kp, Ki and Kd using the divisors of the selected rule :

ruler	divisor1	divisor2	divisor3
"ziegler-nichols"	34	40	160
"tyreus-luyben"	44	9	126
"ciancone-marlin"	66	88	162
"pessen-integral"	28	50	133
"some-overshoot"	60	40	60
"no-overshoot"	100	40	60
"brewing"	2.5	6	380

using the formulas below :

Kp = Ku / divisor1
Ki = Kp / (tosc / divisor2)
Kd = Kp * (tosc / divisor3)

Example with some measures made on the chart above :
Oscillation amplitude Yosc = 2.1°C, oscillation period tosc = 10200 seconds.
According to Ziegler-Nichols the gains will be :

Ku = 8 * 100 / (2.1 * pi) = 121.261
Kp = 121.261 / 34 = 3.567
Ki = 3.567 / (10200 / 40) = 0.01499
Kd = 3.567 * (10200 / 160) = 227.396

The resulting gains are a good starting point, you should then tune them to match your requirements, if you need more reactivity or stability, or to decrease an overshoot.

[bigbread59]

Thank you very much,
I calculated the parameters using the method you suggested.
Now I am doing the fine tuning.

[MarkoPaasila]

Hello,

If autotune fails (it really depends on the regularity of the oscillation), you can extract the required information from the temperature history manually (and you can do it as soon as you have a full period of oscillation, while the autotune often needs to wait several cycles) : Measure the amplitude of the oscillation Yosc (in degrees) and the oscillation period tosc (in seconds).

This will allow computing the ultimate gain Ku: Ku = 8.0 * difference / (Yosc * pi) (difference = 100 by default)

Next, depending on the autotune rule you prefer (it provides different results) you can compute the gains Kp, Ki and Kd using the divisors of the selected rule :
ruler divisor1 divisor2 divisor3
"ziegler-nichols" 34 40 160
"tyreus-luyben" 44 9 126
"ciancone-marlin" 66 88 162
"pessen-integral" 28 50 133
"some-overshoot" 60 40 60
"no-overshoot" 100 40 60
"brewing" 2.5 6 380

using the formulas below :

Kp = Ku / divisor1
Ki = Kp / (tosc / divisor2)
Kd = Kp * (tosc / divisor3)

Example with some measures made on the chart above : Oscillation amplitude Yosc = 2.1°C, oscillation period tosc = 10200 seconds. According to Ziegler-Nichols the gains will be :

Ku = 8 * 100 / (2.1 * pi) = 121.261
Kp = 121.261 / 34 = 3.567
Ki = 3.567 / (10200 / 40) = 0.01499
Kd = 3.567 * (10200 / 160) = 227.396

The resulting gains are a good starting point, you should then tune them to match your requirements, if you need more reactivity or stability, or to decrease an overshoot.

Could you add these manual tuning instructions to the readme?

[nagyrobi]

This will allow computing the ultimate gain Ku: Ku = 8.0 * difference / (Yosc * pi) (difference = 100 by default)

I think that this formula is incorrect. According to the theory the correct formula is:

where d corresponds to difference from your formula, and a corresponds to your Yosc. difference is actually the modulation of the PWM, which indeed in our case should be between 0 and 100. However, the formula is derived initially from -d and +d - that means it should be from -50 to +50:

That means that for a PWM requirement of 0 to 100, d is actually only 50 corresponding to the original formula.
I think the mistake is that difference was multiplied by 2 instead of being divided by 2.
4 * (2 * difference) / (Yosc * pi) versus 4 * (difference / 2) / (Yosc * pi)

So the ultimate gain Ku formula, if you wanna stick to difference as the total amplitude of the PWM = 100 (as used by HA), should be:
Ku = 2.0 * difference / (Yosc * pi).

ESPHome implementation also divides difference by 2.

[zagi988]

I've calculated the values using both yours and original formula and both had heating starting at 22.9 even tho setpoint is 22.5 and I've used no overshoot to calculate.
Can someone explain a bit how to get the values properly? I was using HA history to determine Tosc and Yosc.

[Masterz69]

In my case autotune just not starting.
Attribute autotune_status remains off.

State is heat, hvac_action: idle, pid_mode: off, autotune_status: off... not heating, not tuning.

[Masterz69]

Update about autotune start - looks like it starts after 10 sensor reading.

HA log entry: Autotune will run on XXX (yyy_zzz) with the target temperature set after 10 temperature samples from sensor. Changes submitted after doesn't have any effect until autotuning is finished

Integration version 2022.2.1.