Control Experiment of a Wheel-Driven Mobile Inverted Pendulum Using Neural Network

Abstract

The mobile inverted pendulum is developed and tested for an intelligent control experiment of control engineers. Intelligent control algorithms are tested for the control experiment of a low cost mobile inverted pendulum system. Online learning and control using neural network of a wheel-driven mobile inverted pendulum system is presented. Neural network learning algorithm is embedded on a digital signal processing board along with primary proportional-integral-differential controllers to achieve real time control. Without knowing dynamics of the system, uncertainties in system dynamics are compensated by neural network in an online fashion. Digital filters are designed for a gyro sensor to compensate for a phase lag. Experimental studies of balancing the pendulum and tracking the desired trajectory of the cart for one dimensional motion are conducted. Results show the robustness of the proposed controller even when outer impacts as disturbance are present.