BF: PID Controller (MIMO) #1049

drafts_Amerik/basic_control.py

@@ -0,0 +1,113 @@
+import gymnasium as gym
+import numpy as np
+import matplotlib.pyplot as plt
+from stable_baselines3 import DDPG
+from stable_baselines3.common.noise import NormalActionNoise, OrnsteinUhlenbeckActionNoise
+import random
+env = gym.make("Pendulum-v1",render_mode="human")
+
+def Random_games():
+
+    action_size = env.action_space.shape[0]
+
+    for episode in range(10):
+        env.reset()
+
+        while True:
+            env.render()
+
+            action = np.random.uniform(-1.0,1.0,size=action_size)
+
+            next_state,reward, done, info,_ = env.step(action)
+            print(f'Next state{len(next_state)}\n',f'reward:{reward}')
+
+            if done:
+                break
+def dpg_algorithm():
+        # The noise objects for DDPG
+    n_actions = env.action_space.shape[-1]
+    action_noise = NormalActionNoise(mean=np.zeros(n_actions), sigma=0.1 * np.ones(n_actions))
+
+    model = DDPG("MlpPolicy", env, action_noise=action_noise, verbose=1)
+    model.learn(total_timesteps=10000, log_interval=10)
+    vec_env = model.get_env()
+
+    obs = vec_env.reset()
+    while True:
+        action, _states = model.predict(obs)
+        obs, rewards, dones, info = vec_env.step(action)
+        env.render()
+
+# Parameter
+setpoint = 22.0          # Sollwert in °C
+initial_temp = 15.0      # Starttemperatur in °C
+ambient_temp = 15.0      # Außentemperatur in °C
+time_step = 1            # Zeitintervall in Minuten
+total_time =5000       # Gesamte Simulationszeit in Minuten
+
+# PID-Koeffizienten
+Kp = 10.0   # Proportionaler Koeffizient
+Ti =  100.01  # Integraler Koeffizient
+Td = 250.0  # Differenzieller Koeffizient
+
+# Heizwendel Parameter
+coil_mass = 10.0          # Masse der Heizwendel (kg)
+specific_heat_coil = 0.5  # Spezifische Wärmekapazität der Heizwendel (J/(kg*K))
+coil_temp = initial_temp   # Anfangstemperatur der Heizwendel
+heat_transfer_coeff = 0.1  # Wärmeübertragungskoeffizient (W/K)
+
+# Initialisierung
+time = np.arange(0, total_time + time_step, time_step)
+temperature = np.zeros_like(time,dtype=float)
+setpoints = np.zeros_like(time,dtype=float)
+temperature[0] = initial_temp
+setpoints[0]= setpoint
+# PID-Regler Variablen
+integral = 0
+previous_error = 0
+
+
+# Simulation
+for i in range(1, len(time)):
+
+    #if i%600 ==0:
+    #    setpoint+= random.randint(-2,2)
+    error = setpoint - temperature[i - 1]
+    integral += error * time_step
+    derivative = (error - previous_error) / time_step
+
+    # PID-Regler Berechnung
+    control_signal = Kp * error + (Kp/Ti)*integral + Kp*Td * derivative
+
+    # Begrenzung der Steuergröße und Normierung auf 0 bis 1
+    control_signal = np.clip(control_signal, 0, 100) / 100
+
+    # Heizwendel-Erwärmung
+    power_input = control_signal * 100  # z.B. in Watt
+    coil_temp += (power_input - heat_transfer_coeff * (coil_temp - ambient_temp)) / (coil_mass * specific_heat_coil) * time_step
+    print(f"difference:{coil_temp-ambient_temp}",f"Power:{power_input}")
+
+    # Wärmeübertragung zum Raum
+    heating_power = heat_transfer_coeff * (coil_temp - temperature[i - 1])
+
+
+    # Temperaturänderung des Raums
+    delta_temp = heating_power * time_step / 60
+    temperature[i] = temperature[i - 1] + delta_temp
+
+    # Temperaturveränderung durch Umgebung
+    temperature[i] += (ambient_temp - temperature[i]) * 0.01
+
+    # Update der PID-Variablen
+    previous_error = error
+    setpoints[i]+=setpoint
+
+# Plotten der Ergebnisse
+plt.plot(time, temperature, label='Raumtemperatur')
+plt.plot(time,setpoints, color='r', linestyle='--', label='Sollwert')
+plt.xlabel('Zeit (Minuten)')
+plt.ylabel('Temperatur (°C)')
+plt.title('Temperaturregelung mit normiertem PID-Algorithmus')
+plt.legend()
+plt.grid(True)
+plt.show()