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https://github.com/WallabyLester/RBF-aPID-Controller.git
synced 2026-02-06 04:23:09 +08:00
57 lines
1.5 KiB
Python
57 lines
1.5 KiB
Python
import matplotlib.pyplot as plt
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import numpy as np
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from RBF_numpy import RBFNetwork
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from aPID import AdaptivePIDNP
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def simulate_system(controller, target, dt, T):
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""" Simulate control model as first order system.
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Parameters
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----------
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controller : AdaptivePID
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Any AdaptivePID class instance.
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target : float64
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Target setpoint.
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dt : float64
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Timestep.
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T : float64
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Total time range to simulate.
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Returns
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-------
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Timesteps and measured_value at each.
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"""
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time = np.arange(0, T, dt)
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measured_value = 0
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measurements = []
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for t in time:
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control_signal = controller.update(target, measured_value, dt)
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measured_value += (control_signal - measured_value) * dt # Simple first-order system
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measurements.append(measured_value)
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print(f"Control Signal: {control_signal:.2f}, Measurement: {measured_value:.2f}")
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return time, measurements
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if __name__ == "__main__":
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np.random.seed(20)
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rbf_np = RBFNetwork(input_dim=3, n_centers=5)
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apid_np = AdaptivePIDNP(Kp=4.0, Ki=0.1, Kd=0.01, rbf_network=rbf_np)
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target = 1.0
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dt = 0.1
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T = 10.0
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time, measurements = simulate_system(apid_np, target, dt, T)
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plt.plot(time, measurements, label="Measured Value")
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plt.axhline(y=target, color="r", linestyle="--", label="Target")
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plt.xlabel("Time (s)")
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plt.ylabel("Output")
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plt.title("Adaptive RBF Neural PID Controller")
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plt.legend()
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plt.grid()
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plt.show()
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