Controlling inverted pendulum based on neural network and particle filter

Using karman filter to train the neural network can overcome the drawbacks of BP algorithm such as falling into local minima and slow convergence. However, as the inverted pendulum model is a strong nonlinear, unstable system, we should first linearize the model system. This will lead to huge linearized error. Particle filter can be applied to the status estimation of any nonlinear and non-Gaussian system, and without linearizing the system, it has no linearized error. In this paper, we found the physical model, the filter system equation and the observation equation of the inverted pendulum controller and use particle filter to estimate the neural network parameters. We compare the control effect between karman filter and particle filter in the mode of off-line. The simulation results show that the performance of particle filter improves markedly than karman filter both on speed and precision.