Data-Driven Learning for <i>H</i> $_{\infty}$ Control of Adaptive Cruise Control Systems

This paper develops a novel adaptive <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{\infty}$</tex-math></inline-formula> control scheme for adaptive cruise control (ACC) system via the data-driven learning. In the proposed technique, a continuous time ACC system is first constructed with unknown system dynamics. To estimate the unknown system dynamics, an adaptive estimator is then formulated utilizing the vectorization and Kronecker's products operations, enabling the reconstruction of the unknown system dynamics through the detectable input/output information. An adaptive law is utilized to ensure the convergence of the estimated parameters. Moreover, a data-driven learning technique is employed to resolve the constructed Riccati equation in an online manner. To accomplish this, the Riccati equation is reformulated via the Kronecker's products by using another adaptive law, whose convergence can be also effectively guaranteed. Unlike existing neural network-based approximate dynamic programming (ADP) algorithms, the data-driven learning scheme proposed in this paper does not involve neural network approximation errors, so the solution is relatively more accurate. Finally, simulation and experimental verification results are provided to verify the effectiveness of the presented <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{\infty}$</tex-math></inline-formula> control and data-driven learning algorithm.