Data-based Control of Feedback Linearizable Systems

Abstract

We present an extension of Willems' Fundamental Lemma to the class of multi-input multi-output discrete-time feedback linearizable nonlinear systems, thus providing a data-based representation of their input-output trajectories. Two sources of uncertainty are considered. First, the unknown linearizing input is inexactly approximated by a set of basis functions. Second, the measured output data is contaminated by additive noise. Further, we propose an approach to approximate the solution of the data-based simulation and output matching problems, and show that the difference from the true solution is bounded. Finally, the results are illustrated on an example of a fully-actuated double inverted pendulum.

References in corpus (2)

• Linear tracking MPC for nonlinear systems Part II: The data-driven case
• Efficient Off-Policy Q-Learning for Data-Based Discrete-Time LQR Problems

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