3DPrinting/Prusa-Firmware
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TM: Handle dynamic parameter changing

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Instead of assuming the model state is always valid, allow NAN values to
pass-through the various check/estimation stages.

This allows running the model checker with incomplete parameters (for
example, missing entries in the R vector) and resume automatically.
This commit is contained in:
Yuri D'Elia 2022-08-30 12:26:58 +02:00 committed by DRracer
parent 54e5702f30
commit ff459a6427
1 changed files with 21 additions and 10 deletions
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31
Firmware/temperature.cpp
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@ -2314,7 +2314,8 @@ static void check_temp_raw()
#ifdef TEMP_MODEL
namespace temp_model {
void model_data::reset(uint8_t heater_pwm, uint8_t fan_pwm, float heater_temp, float ambient_temp)
void model_data::reset(uint8_t heater_pwm _UNUSED, uint8_t fan_pwm _UNUSED,
float heater_temp _UNUSED, float ambient_temp _UNUSED)
{
// pre-compute invariant values
C_i = (TEMP_MGR_INTV / C);
@ -2322,18 +2323,22 @@ void model_data::reset(uint8_t heater_pwm, uint8_t fan_pwm, float heater_temp, f
err_s = err * TEMP_MGR_INTV;
// initial values
memset(dT_lag_buf, 0, sizeof(dT_lag_buf));
for(uint8_t i = 0; i != TEMP_MODEL_LAG_SIZE; ++i)
dT_lag_buf[i] = NAN;
dT_lag_idx = 0;
dT_err_prev = 0;
T_prev = heater_temp;
// perform one step to initialize the first delta
step(heater_pwm, fan_pwm, heater_temp, ambient_temp);
T_prev = NAN;
// clear the initialization flag
flag_bits.uninitialized = false;
}
static constexpr float iir_mul(const float a, const float b, const float f, const float nanv)
{
const float a_ = !isnan(a) ? a : nanv;
return (a_ * (1.f - f)) + (b * f);
}
void model_data::step(uint8_t heater_pwm, uint8_t fan_pwm, float heater_temp, float ambient_temp)
{
constexpr float soft_pwm_inv = 1. / ((1 << 7) - 1);
@ -2352,13 +2357,13 @@ void model_data::step(uint8_t heater_pwm, uint8_t fan_pwm, float heater_temp, fl
uint8_t dT_next_idx = (dT_lag_idx == (TEMP_MODEL_LAG_SIZE - 1) ? 0: dT_lag_idx + 1);
float dT_lag = dT_lag_buf[dT_next_idx];
float dT_lag_prev = dT_lag_buf[dT_lag_idx];
float dT_f = (dT_lag_prev * (1.f - TEMP_MODEL_fS)) + (dT * TEMP_MODEL_fS);
float dT_f = iir_mul(dT_lag_prev, dT, TEMP_MODEL_fS, dT);
dT_lag_buf[dT_next_idx] = dT_f;
dT_lag_idx = dT_next_idx;
// calculate and filter dT_err
float dT_err = (cur_heater_temp - T_prev) - dT_lag;
float dT_err_f = (dT_err_prev * (1.f - TEMP_MODEL_fE)) + (dT_err * TEMP_MODEL_fE);
float dT_err_f = iir_mul(dT_err_prev, dT_err, TEMP_MODEL_fE, 0.);
T_prev = cur_heater_temp;
dT_err_prev = dT_err_f;
@ -2697,11 +2702,17 @@ static float cost_fn(uint16_t samples, float* const var, float v, uint8_t fan_pw
*var = v;
temp_model::data.reset(rec_buffer[0].pwm, fan_pwm, rec_buffer[0].temp, ambient);
float err = 0;
uint16_t cnt = 0;
for(uint16_t i = 1; i < samples; ++i) {
temp_model::data.step(rec_buffer[i].pwm, fan_pwm, rec_buffer[i].temp, ambient);
err += fabsf(temp_model::data.dT_err_prev);
float err_v = temp_model::data.dT_err_prev;
if(!isnan(err_v)) {
err_v = fabsf(err_v);
err += err_v * err_v;
++cnt;
}
}
return (err / (samples - 1));
return cnt ? (err / cnt) : NAN;
}
constexpr float GOLDEN_RATIO = 0.6180339887498949;