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In this paper, the design procedure for optimal model-free control algorithm is presented for the tracking problem of completely unknown nonlinear dynamic systems operating under unknown disturbances. The procedure includes a new structure in the context of model-free control and data-driven control algorithms. In the new structure, the unknown nonlinear functions are segmented into 1 unknown linear-in-states part and another unknown nonlinear part. The adaptive laws proposed for estimating the unknown system dynamics are regressor-free estimation methods in which there is no need for regressor parameters and, consequently, the persistent excitation condition is not required anymore. Moreover, the main controller gains are updated online, incorporating the adapted values of linear terms in the system dynamics. A comparative study is presented to show that the proposed optimal model-free control outperforms the state-of-the-art model-free control algorithms. In addition, the simulation results for the application of the algorithm on autonomous mobile robots are provided.

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