Recurrent neuro-controller design for an inverted pendulum using evolution strategy

As the recurrent neural network exhibits the excellent dynamic processing ability, a dynamic feedback control strategy using recurrent neuro-control is proposed to the application on the balance control of the inverted pendulum. Because the conventional error backpropagation methods for the training can not be used in the optimal design here due to that the only feedback evaluating performance is the failure signal, the extended (mu, lambda)-ES for the unsupervising learning of the control parameter is presented in this paper. Meanwhile, the stabilisation of the controlled system is guaranteed during the extended (mu, lambda)-ES learning phase using the constraints optimisation. Simulation results have shown that training efficiency of the extended (mu, lambda)-ES is better than the traditional (mu, lambda)-ES. It is also shown that the recurrent neuro-control for the dynamic system possesses excellent performance compared with the MLP neuro-control with the fewer feedback signals.