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testbenches/testbench_pid_pt2_evolutionPyGAD.py

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+# Any copyright is dedicated to the Public Domain.
+# https://creativecommons.org/publicdomain/zero/1.0/
+
+from pyconsys.Control import Control
+from pyconsys.PIDControl import PIDControl
+from pyconsys.PT2 import PT2
+from pyconsys.Rating import Rating
+import matplotlib.pyplot as plt
+import time
+import pygad
+
+# Hyperparameter
+
+# Anzahl der Gene
+num_genes = 3
+# Anzahl der Generationen
+num_generations = 20
+# Anzahl der Chromosomen
+sol_per_pop = 10
+# Anzahl der Eltern
+num_parents_mating = 10
+# Wahrscheinlichkeit zur Mutation
+mutation_percent_genes = 34
+
+# untere Grenze der Zufallszahl
+init_range_low = 0
+# obere Grenze der Zufallszahl
+init_range_high = 100
+# Datentyp
+gene_type = float
+
+
+pt2_a2 = 0.2
+pt2_a1 = 0.05
+pt2_a0 = 1
+pt2_b0 = 1
+
+pid_control = PIDControl(0, 0, 0)
+pt2 = PT2(pt2_a2, pt2_a1, pt2_a0, pt2_b0)
+rating = Rating()
+
+
+def fitness_func(ga_instance, solution, solution_idx):
+    p = solution[0]
+    i = solution[1]
+    d = solution[2]
+
+    time_steps = [x * Control.DELTA_T for x in range(0, 500)]
+    w_lst = [1 for x in time_steps]
+    x_lst = []
+    x = 0
+
+    pid_control.update_params(p, i, d)
+    pt2.reset()
+
+    for w in w_lst:
+        e = w - x
+        y = pid_control.get_xa(e)
+        x = pt2.get_xa(y)
+        x_lst.append(x)
+
+    fitness = rating.get_update_rating(x_lst, w)
+
+    return fitness
+
+# define a function to get the fitness score of a pid triple
+ga_instance = pygad.GA(num_generations=num_generations,
+                       num_parents_mating=num_parents_mating,
+                       fitness_func=fitness_func,
+                       sol_per_pop=sol_per_pop,
+                       init_range_low=init_range_low,
+                       init_range_high=init_range_high,
+                       num_genes=num_genes,
+                       mutation_percent_genes=mutation_percent_genes,
+                       gene_type=gene_type)
+
+#####################################################
+# start the evolution
+
+
+time_stamp = time.time()
+print("Evolution is running. Please wait...")
+# The constructor needs the fitness function as a callback
+ga_instance.run()
+# start the evolution
+
+tm = time.time() - time_stamp
+
+solution, solution_fitness, solution_idx = ga_instance.best_solution()
+
+print("Benötigte Zeit:", tm)
+print("Beste Parameter:", solution)
+print("Höchster Fitnesswert:", solution_fitness)
+ga_instance.plot_fitness()
+####################################################
+# plot the fittest control loop
+
+time_steps_fin = [x * Control.DELTA_T for x in range(0, 500)]
+w_lst_fin = [1 if x > 1 else 0 for x in time_steps_fin]
+#w_lst_fin = [1 for x in time_steps_fin]
+x_lst_fin = []
+x = 0
+best_pid_p = solution[0]
+best_pid_i = solution[1]
+best_pid_d = solution[2]
+
+pid_control.update_params(best_pid_p, best_pid_i, best_pid_d)
+pt2.reset()
+
+for w in w_lst_fin:
+    e = w - x
+    y = pid_control.get_xa(e)
+    x = pt2.get_xa(y)
+    x_lst_fin.append(x)
+
+print("##### best score: {:7.2f}".format(solution_fitness))
+print("##### evolution time: {:7.2f}".format(tm))
+
+txt_pid = "PID with P = {}, I = {}, D = {}".format(best_pid_p, best_pid_i, best_pid_d)
+txt_pt2 = "PT2 with a2 = {}, a1 = {}, a0 = {}, b0 = {}".format(pt2_a2, pt2_a1, pt2_a0, pt2_b0)
+plt.plot(time_steps_fin, w_lst_fin, label="w")
+plt.plot(time_steps_fin, x_lst_fin, label="x")
+plt.grid()
+plt.minorticks_on()
+plt.grid(which='major', linestyle='-', linewidth='0.5')
+plt.grid(which='minor', linestyle=':', linewidth='0.3')
+
+plt.xlabel('k in hundreds')
+plt.title("PT2 control loop with evolution PID")
+plt.text(1.5, 0.3, txt_pid, bbox=dict(facecolor='white', alpha=0.5))
+plt.text(1.5, 0.1, txt_pt2, bbox=dict(facecolor='white', alpha=0.5))
+plt.legend()
+plt.ylim(top=2, bottom=0)
+plt.show()