Particle Swarm Optimization of a Recurrent Neural Network Control for an Underactuated Rotary Crane with Particle Filter Based State Estimation
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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.
Keywordsrecurrent neural network particle swarm optimization nonlinear control underactuated system particle filter sequential Monte Carlo method
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