Neural inverse modeling and control of a base-excited inverted pendulum

Abstract

In this paper multilayer neural networks are used to control the balancing of a base-excited inverted pendulum. The pendulum has 2 degrees of rotational freedom and the base-point moves freely in three-dimensional space. The goal is to apply control torques to keep the pendulum in a prescribed orientation in spite of disturbing base-point movement. A control algorithm is proposed that utilizes a set of neural networks to compensate for the effect of system's nonlinearities. These networks are updated on-line, according to a learning algorithm, which guarantees the stability of the closed-loop system. Furthermore, since the pendulum's base-point movement is considered unmeasurable, a novel neural inverse model is employed to estimate it from measurable variables. The proposed neural controller has been tested through simulations. Its performance has also been compared with the performance of the most recently developed control technique on the same problem. It is shown that the proposed neural controller produces fast, yet well maintained damped responses with reasonable control torques and without a knowledge of the model or model parameters. Additionally, the developed controller does not require measurement of the base-point accelerations, which are difficult to obtain. The work presented here benefits practical problems such as the study of stable locomotion of human upper body and bipedal locomotion.