Difference between revisions of "Figure 3.24: Consensus protocols for sensor networks"
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|Chapter=System Modeling | |Chapter=System Modeling | ||
− | |Figure number=24 | + | |Figure number=3.24 |
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|Figure title=Consensus protocols for sensor networks | |Figure title=Consensus protocols for sensor networks | ||
− | |GitHub URL=https://github.com/murrayrm/fbs2e-python/blob/main/ | + | |GitHub URL=https://github.com/murrayrm/fbs2e-python/blob/main/example-3.17-consensus.py |
}} | }} | ||
[[Image:figure-3.24-consensus_dynamics.png|320px]] | [[Image:figure-3.24-consensus_dynamics.png|320px]] |
Latest revision as of 16:35, 28 May 2023
Chapter | System Modeling |
---|---|
Figure number | 3.24 |
Figure title | Consensus protocols for sensor networks |
GitHub URL | https://github.com/murrayrm/fbs2e-python/blob/main/example-3.17-consensus.py |
Requires | python-control |
Figure 3.24: Consensus protocols for sensor networks. (b) A simulation demonstrating the convergence of the consensus protocol (3.35) to the average value of the initial conditions.
# example-3.17-consensus.py - consensus protocols # RMM, 30 Aug 2021 # Figure 3.24: Consensus protocols for sensor networks. (a) A simple sensor # net- work with five nodes. In this network, node 1 communicates with node # 2 and node 2 communicates with nodes 1, 3, 4, 5, etc. (b) A simulation # demonstrating the convergence of the consensus protocol (3.35) to the # average value of the initial conditions. import control as ct import numpy as np import matplotlib.pyplot as plt # # System dynamcis # # Construct the Laplacian corresponding to our network L = np.array([ [ 1, -1, 0, 0, 0], [-1, 4, -1, -1, -1], [ 0, -1, 2, -1, 0], [ 0, -1, -1, 2, 0], [ 0, -1, 0, 0, 1] ]) # Now generate the discrete time dynamics matrix gamma = 0.1 A = np.eye(5) - gamma * L # Initial set of measurements for the system x0 = [10, 15, 25, 35, 40] # Create a discrete time system sys = ct.ss(A, np.zeros(5), np.zeros(5), 0, dt=True) # Set up the plotting grid to match the layout in the book fig = plt.figure(constrained_layout=True) gs = fig.add_gridspec(3, 2) # # (b) A simulation demonstrating the convergence of the consensus protocol # (3.35) to the average value of the initial conditions. # fig.add_subplot(gs[0, 1]) # first row, first column # Simulate the system response = ct.initial_response(sys, 40, x0) # Plot th3 results for i in range(response.nstates): plt.plot(response.time, response.states[i], 'b-') # Label the figure plt.xlabel("Iteration") plt.ylabel("Agent states $x_i$") plt.title("Consensus convergence") # Save the figure plt.savefig("figure-3.24-consensus_dynamics.png", bbox_inches='tight')