Particle Swarm Optimization of a Recurrent Neural Network Control for an Underactuated Rotary Crane with Particle Filter Based State Estimation

This paper addresses the control problem of an underactuated rotary crane system by using a recurrent neural network (RNN) and a particle filter (PF) based state estimation. The RNN is used as a state feedback controller which is designed by a constricted particle swarm optimization (PSO). As the study also considers the problem with assuming that the velocities of the system are not obtained, PF is utilized to estimate the latent states. Simulations show that the RNN could provide a superior evolutionary performance and less computational cost compared to a feed forward NN and that the PF is effective in estimating the unobserved states.

[1]  Ilya Kolmanovsky,et al.  Developments in nonholonomic control problems , 1995 .

[2]  G. Kitagawa.  Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models , 1996 .

[3]  Armando Blanco,et al.  A real-coded genetic algorithm for training recurrent neural networks , 2001, Neural Networks.

[4]  Peter J. Fleming,et al.  Evolutionary algorithms in control systems engineering: a survey , 2002 .

[5]  J.V. Candy,et al.  Bootstrap Particle Filtering , 2007, IEEE Signal Processing Magazine.

[6]  Ryou Kondo,et al.  Anti-Sway Control of a Rotary Crane via Two-Mode Switching Control , 2005 .

[7]  Fabrice Druaux,et al.  Stable adaptive control with recurrent neural networks for square MIMO non-linear systems , 2009, Eng. Appl. Artif. Intell..

[8]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[9]  E. Aiyoshi,et al.  Particle swarm optimization: a numerical stability analysis and parameter adjustment based on swarm activity , 2008 .

[10]  Peter J. Angeline,et al.  An evolutionary algorithm that constructs recurrent neural networks , 1994, IEEE Trans. Neural Networks.

[11]  Yoshiyuki Sakawa,et al.  Optimal control of container cranes , 1981, Autom..

[12]  Derek A. Linkens,et al.  Learning systems in intelligent control: an appraisal of fuzzy, neural and genetic algorithm control applications , 1996 .

[13]  Guy Albert Dumont,et al.  System identification and control using genetic algorithms , 1992, IEEE Trans. Syst. Man Cybern..