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The control of nonlinear systems with unknown dynamics has been a significantfield of research for many years. This paper presents a novel data-drivenoptimal adaptive control structure with less control effort and fasteradaptation than standard adaptive control counterparts. The proposed controlstructure utilizes the system's recorded data to increase the speed ofadaptation and performance dramatically. In this study, we employ a conditionalgenerative adversarial net (CGAN) as a novel central pattern generator toreproduce the steady-state harmonic pattern of the control signals matching thesystem's uncertainties over a wide range. We can also use the CGAN architectureas a fault detector. The CGAN provides a low-dimensional latent space ofuncertainties. It enables rapid and convenient adaptation when there are manyparametric uncertainties, especially for large-scale systems. Then, weintroduce a novel meta-reinforcement learning framework to adapt the latentspace of CGAN to the system's uncertainties as an optimal direct adaptivecontroller without any system identifier. Another part of the control structureis a regulator that achieves semi-global asymptotic tracking using the Lyapunovstability analysis. Finally, via some simulations, we evaluate the capabilitiesof the proposed designs on two dynamical systems, a robot manipulator and alarge-scale musculoskeletal structure, in the presence of disturbance andperturbation.

Fast and Optimal Adaptive Tracking Control: A Novel Meta-Reinforcement Learning via Conditional Generative Adversarial Net