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FOPID Controller Design Using Hybrid Particle Swarm Based Simulated Annealing Optimization and Also Stability Analysis

Sayarpour , Modsen | 2010

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 40541 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Sadatim, Naser
  7. Abstract:
  8. Although many mathematicians have searched on fractional calculus since many years ago, but its application in engineering, especially in modeling and control, does not have many antecedents. Because there are much freedom in choosing the order of differentiator and integrator in fractional calculus, it is possible to model the physical systems accurately. Also, there is the same reasoning in the control area. We can design such fractional controllers that have high performance. Among the fractional order controllers, the fractional order PID (FOPID) has been dealt with more than others and there are many methods to design it, such as Ziegler-Nichols, state space and method based on genetic algorithm. This thesis presents a new approach for designing a fractional order PID controller using a hybrid particle swarm based simulated annealing optimization method. The PSOBSA is an effective optimization algorithm for practical optimization problems. In fact, this method uses the strong points of PSO and SA algorithms together. The purpose of this method is to find an appropriate FOPID controller in order to fulfill several design specifications. Explanatory examples are presented and simulation results show the effectiveness of this algorithm. In continue stability theorems and analysis of linear fractional order systems is considered. Finally we focus on the stability test method for interval fractional order LTI systems. This method is a sufficient condition for the robust stability test procedure. Two examples are presented for showing the efficiency of this procedure
  9. Keywords:
  10. Fractional Calculus ; Robustness ; Particles Swarm Optimization (PSO) ; Simulated Annealing Method ; Robust Stability

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