Performance Simulation of PID Composite Controller Based on CMAC Neural Network in MATLAB

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📶The work includes complete programs, documentation, references, and operation videos

🌠Preview of Simulation Conclusions

Testing results using Matlab 2024b are as follows:

Performance Simulation of PID Composite Controller Based on CMAC Neural Network in MATLABPerformance Simulation of PID Composite Controller Based on CMAC Neural Network in MATLABPerformance Simulation of PID Composite Controller Based on CMAC Neural Network in MATLABPerformance Simulation of PID Composite Controller Based on CMAC Neural Network in MATLABProgram Function Description<span><span>The functions and principles of this program are as follows:</span></span>

CMAC is a lookup table-based local approximation network, whose structure mimics the cerebellum’s “parallel distributed processing” mechanism. Its core advantages are reflected in two aspects:

Local Generalization Ability: CMAC only activates a small number of neurons (storage units) related to the input, rather than activating the entire network. The learning process updates only local weights, significantly improving learning speed to meet real-time control requirements;

Strong Fault Tolerance: A failure of a single storage unit only affects the local input area and does not cause the entire control system to fail, making it suitable for harsh industrial environments;

No Need for Precise Mathematical Models: The system’s unknown dynamics can be approximated through sample learning without the need to establish a nonlinear mathematical model of the controlled object in advance, lowering the engineering application threshold.

✨Partial Program

<span><span>Some core program content is as follows</span></span>

figure;% Adjust timesteady_state = sim_data.rin(end);tolerance = 0.05 * steady_state;stable_idx = find(abs(sim_data.yout - steady_state) &lt;= tolerance, 1, 'first');performance.settling_time = sim_data.time(stable_idx);%% 6. Display performance indicatorsfprintf('Control system performance indicators:
');fprintf('1. Steady-state error: %.6f
', performance.steady_state_error);fprintf('2. Mean Squared Error (MSE): %.6f
', performance.mse);fprintf('3. Integral Absolute Error (IAE): %.6f
', performance.iae);fprintf('4. Integral Time Absolute Error (ITAE): %.6f
', performance.itae);fprintf('5. Overshoot: %.2f%%
', performance.overshoot);fprintf('6. Rise time: %.4f seconds
', performance.rise_time);fprintf('7. Settling time: %.4f seconds
', performance.settling_time);%% 7. Result Visualization% Figure 1: Reference input and system outputfigure('Name', 'System Tracking Performance', 'Position', [100, 100, 1000, 600]);plot(sim_data.time, sim_data.rin, 'b-', 'LineWidth', 1.5);hold on; grid on;plot(sim_data.time, sim_data.yout, 'r-', 'LineWidth', 1.5);xlabel('Time (s)', 'FontSize', 12);ylabel('Output', 'FontSize', 12);title('Comparison of Reference Input and System Output', 'FontSize', 14);legend('Reference Input', 'System Output', 'Location', 'Best');axis tight;% Figure 2: Control signalfigure('Name', 'Control Signal', 'Position', [100, 200, 1000, 800]);subplot(3,1,1);plot(sim_data.time, sim_data.un, 'k-', 'LineWidth', 1.2);grid on;ylabel('CMAC Output', 'FontSize', 11);title('Control Signal Decomposition', 'FontSize', 14);subplot(3,1,2);plot(sim_data.time, sim_data.up, 'k-', 'LineWidth', 1.2);grid on;ylabel('PID Output', 'FontSize', 11);subplot(3,1,3);plot(sim_data.time, sim_data.u, 'r-', 'LineWidth', 1.2);grid on;xlabel('Time (s)', 'FontSize', 12);ylabel('Total Control Amount', 'FontSize', 11);% Figure 3: Error Analysisfigure('Name', 'Error Analysis', 'Position', [100, 300, 1000, 600]);subplot(2,1,1);plot(sim_data.time, sim_data.error, 'k-', 'LineWidth', 1.2);grid on;ylabel('Control Error', 'FontSize', 11);title('Error Analysis', 'FontSize', 14);subplot(2,1,2);plot(sim_data.time, abs(sim_data.error), 'b-', 'LineWidth', 1.2);grid on;xlabel('Time (s)', 'FontSize', 12);ylabel('Absolute Error', 'FontSize', 11);% Figure 4: Performance Indicator Bar Chartfigure('Name', 'Performance Indicators', 'Position', [100, 400, 1000, 600]);metrics1 = [performance.steady_state_error, performance.mse, ...           performance.iae, performance.overshoot/100, ...           performance.rise_time, performance.settling_time];load R0.matmetrics2 = metrics;bar([metrics2;metrics1]');grid on;ylabel('Indicator Value', 'FontSize', 12);title('Comparison of Control System Performance Indicators', 'FontSize', 14);set(gca, 'XTickLabel', {'Steady-state Error', 'MSE', 'IAE', 'Overshoot', 'Rise Time', 'Settling Time'});legend('PID','CMAC-PID');130

🌍Program Acquisition Method

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Performance Simulation of PID Composite Controller Based on CMAC Neural Network in MATLABPerformance Simulation of PID Composite Controller Based on CMAC Neural Network in MATLAB

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