Application of Self-learning Algorithm Based on Parameter Optimization in Control of Two Wheeled Robot

To overcome the disadvantages of the existing control algorithm of two-wheeled robot(TWR), the adaptive controller was designed by using the self-learning parameter optimization algorithm. The controller has a simple structure and does not need to rely on the accurate mathematical model, and the optimal control parameters can be obtained through learning and training many times. The controller was applied to the balance control of the two-wheeled robot, and was compared with the linear quadratic regulator (LQR) optimal controller. The simulation results verify the correctness and validity of the algorithm, highlights the strong robustness and bionics habits. The controller was applied to the physical system of two-wheeled robot, and achieved good control effect.