Modeling of flexible manipulator structure using particle swarm optimization with Explorer

This paper presents an investigation into the development of novel Particle Swarm Optimization with Explorer and its application to system identification for a single-link flexible manipulator system. A simulation environment characterizing the dynamic behavior of the flexible manipulator system was first developed using finite difference (FD) method to acquire the input-output data of the system. In this study, system identification scheme is developed to obtain a dynamic model of the manipulator in parametric form using Particle Swarm Optimization with Explorer. The introduction of explorer solves problem of getting stuck at local minima, thus proposed a novel methodology namely as Particle Swarm Optimization with Explorer (PSOE). Its performance is assessed in comparison to a standard Particle Swarm Optimization in characterizing the flexible manipulator structure. Results demonstrate the advantages of Particle Swarm Optimization with Explorer over their standard counterpart in system identification.

[1]  Sanjay Sharma,et al.  Flexible robot manipulators , 2008 .

[2]  Narayana Prasad Padhy,et al.  Comparison of Particle Swarm Optimization and Genetic Algorithm for TCSC-based Controller Design , 2007 .

[3]  Ganapati Panda,et al.  Identification of nonlinear systems using particle swarm optimization technique , 2007, 2007 IEEE Congress on Evolutionary Computation.

[4]  Xianjun Shen,et al.  An Dynamic Adaptive Dissipative Particle Swarm Optimization with Mutation Operation , 2007, 2007 IEEE International Conference on Control and Automation.

[5]  Yi Wang,et al.  Particle Swarm Optimization (PSO) for the constrained portfolio optimization problem , 2011, Expert Syst. Appl..

[6]  B. Durmuş,et al.  Parameter Identification using Particle Swarm Optimization , 2011 .

[7]  M. O. Tokhi,et al.  PSO-Based Parametric Modelling of a Thin Plate Structure , 2009, 2009 Third UKSim European Symposium on Computer Modeling and Simulation.

[8]  R. Eberhart,et al.  Fuzzy adaptive particle swarm optimization , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[9]  O. Weck,et al.  A COMPARISON OF PARTICLE SWARM OPTIMIZATION AND THE GENETIC ALGORITHM , 2005 .

[10]  I.Z.M. Darus,et al.  Parametric modelling of a twin rotor system using genetic algorithms , 2004, First International Symposium on Control, Communications and Signal Processing, 2004..

[11]  Xiuqin Deng System Identification Based on Particle Swarm Optimization Algorithm , 2009, 2009 International Conference on Computational Intelligence and Security.

[12]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).