System Identification of Two-Wheeled Robot Dynamics Using Neural Networks

Abstract

Abstract A system identification of two-wheeled robot (TWR) moving on planar space is presented by applying using neural networks. The system identification is to model the TWR dynamics which is a nonlinear system. The model is applied for estimating the TWR posture during the movements. Neural networks applied in the system identification is multi later perceptron. The neural networks consists of three layers with eight neurons at the first layer, five neurons at the second layer, and three neurons at the third layers. The neural networks is trained to model the TWR dynamics based on a set of input and output data. The system identification is demonstrated through computer simulations. The results show that the system identification using neural networks is able to model the TWR dynamics. The neural networks with learning rate 0.005 is able to estimate the TWR posture with convergence time 0.5 seconds.