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| import streamlit as st | |
| import numpy as np | |
| import pandas as pd | |
| class ProcessModel: | |
| def __init__(self, Kp, tau, theta, dt): | |
| self.Kp = Kp | |
| self.tau = tau | |
| self.theta = theta | |
| self.pv = 0.0 | |
| self.buffer = [0.0] * (int(theta / dt) if dt > 0 else 0) | |
| def update(self, cv, dt): | |
| if self.buffer: | |
| delayed_cv = self.buffer.pop(0) | |
| self.buffer.append(cv) | |
| else: | |
| delayed_cv = cv | |
| # Simple process simulation | |
| d_pv = (self.Kp * delayed_cv - self.pv) / self.tau | |
| self.pv += d_pv * dt | |
| return self.pv | |
| class PIDController: | |
| def __init__(self, Kp, Ki, Kd): | |
| self.Kp = Kp | |
| self.Ki = Ki | |
| self.Kd = Kd | |
| self.integral = 0.0 | |
| self.previous_error = 0.0 | |
| def calculate(self, sp, pv, dt): | |
| error = sp - pv | |
| self.integral += error * dt | |
| derivative = (error - self.previous_error) / dt | |
| self.previous_error = error | |
| return self.Kp * error + self.Ki * self.integral + self.Kd * derivative | |
| def run_simulation(process, controller, setpoints, duration, dt): | |
| time_points = np.arange(0, duration, dt) | |
| pv_history, cv_history, sp_history = [], [], [] | |
| iae = coi = oscillation_index = 0.0 | |
| error_last = cv_last = 0.0 | |
| current_sp = setpoints[0][1] | |
| for t in time_points: | |
| for sp_time, sp_value in reversed(setpoints): | |
| if t >= sp_time: | |
| current_sp = sp_value | |
| break | |
| sp_history.append(current_sp) | |
| cv = controller.calculate(current_sp, process.pv, dt) | |
| pv = process.update(cv, dt) | |
| pv_history.append(pv) | |
| cv_history.append(cv) | |
| error = current_sp - pv | |
| iae += abs(error) * dt | |
| if t > 0: | |
| coi += abs(cv - cv_last) * dt | |
| if error * error_last < 0: | |
| oscillation_index += 1 | |
| cv_last = cv | |
| error_last = error | |
| metrics = {"IAE": iae, "COI": coi, "Oscillation Index": oscillation_index} | |
| df = pd.DataFrame( | |
| {"Time": time_points, "SP": sp_history, "PV": pv_history, "MV": cv_history} | |
| ) | |
| return df, metrics | |