Controlador neuronal para el seguimiento de trayectorias en un péndulo de rueda inercial

In this paper, the problem of trajectory tracking control in an inertia wheel pendulum is studied. Results are presented in a constructive form. First, a model-based controller is obtained by using the output feedback linearization technique. Then, the controller is redesigned by incorporating a neural network with the aim of avoiding the exact parameters knowledge of the inertia wheel pendulum, obtaining a robust control scheme. A two-layer perceptron is used, whose output weights are updated in real-time using an adaption law derived from the analysis of convergence of the closed-loop system solutions. Barbalat's lemma is used to conclude that the pendulum tracking error trajectory converges to zero. Numerical simulations and real-time experiments are presented, which confirm the theoretical results.