Abstract
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.
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