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Novel Technique for PID Tuning by Particle Swarm Optimization

Novel Technique for PID Tuning by Particle Swarm Optimization. S. Easter Selvan Sethu Subramanian S. Theban Solomon. Introduction. PARTICLE : Volume-less individual; conditionally dislodged in search space. SWARMING : Behavior of organisms in search of conducive environment for sustenance.

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Novel Technique for PID Tuning by Particle Swarm Optimization

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  1. Novel Technique for PID Tuning by Particle Swarm Optimization S. Easter Selvan Sethu Subramanian S. Theban Solomon

  2. Introduction • PARTICLE : Volume-less individual; conditionally dislodged in search space. • SWARMING : Behavior of organisms in search of conducive environment for sustenance. • APPLICATION : Tuning PID controller by globally best solution.

  3. Proposed Features in PSO 1. Unbiased search for optimal solution. 2. Unifying the clusters in the potential space. 3. Fine search – selection of the fittest particle.

  4. Generation of Solution Space • Feasible set of Kp, Ki, Kd values generated based on Ziegler Nichols method and Nyquist criteria. • Solution space populated with particles in random positions.

  5. Unbiased Search • Each particle dislodged randomly by fixed step size. • If cost favorable – proceeds in same direction • Else returns to previous position; attempts random directions with increased step size. • Initially coarse search; towards end finer search.

  6. Cluster Unification • Particles settle in clusters at locations of favorable costs. • CASE I : Best particle in major cluster. • CASE II : Best particle in minor cluster. • Cluster with best particle drags the rest based on Euclidean distance – thereby unifying clusters.

  7. Selection of Best Particle • Particles assume virtual spheres whose radius is distance between best particle and themselves. • Particles radially move in search of cost better than best particle’s cost. • If better one found - virtual spheres updated. • Else search continues until absorbed by best particle. • Search terminated when majority absorbed.

  8. Experimental Results

  9. Experimental Results cont.

  10. System Response Comparison Ziegler Nichols Method PSO Method

  11. Swarm Behavior in PI Controller Surface Plot Particle Settlement

  12. PSO Results Initial Population Unbiased Search Result

  13. PSO Results cont. Unification of Clusters Best Particle

  14. Conclusion • 80% of tested cases form distinct clusters - faster convergence. • Extremely low settling time obtained by PSO compared to Ziegler-Nichols method. • Improper valley formation due to cost function leads to slow convergence.

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