<p>This paper details the synthesis of a robust adaptive intelligent control strategy tailored for nonlinear systems characterized by parametric uncertainties and external disturbances. The adaptive intelligent component of the proposed controller, employing a projection algorithm, facilitates the bounded estimation of the weights within a Radial Basis Function Neural Network (RBFNN). This RBFNN serves to approximate the system's inherent nonlinearities. Concurrently, the robust control component is engineered through the integrated application of H-infinity and guaranteed-cost control methodologies, thereby ensuring robust closed-loop stability and performance against the identified uncertainties and disturbances. To guarantee the desired performance, the proposed control strategy leverages model reference techniques, enforcing closed-loop convergence to a reference model’s dynamics. A Lyapunov stability analysis is performed to formally establish the global uniform ultimate boundedness of the error trajectories. Moreover, the Equilibrium Optimizer (EO) algorithm is employed to ascertain the guaranteed-cost H-infinity controller's optimal gains that yield the minimal ultimate bound for the error trajectories. The EO algorithm is further implemented to identify the RBFNN's optimized hyperparameters, estimated RBFNN's weights norm bound, and the adaptation rate matrix, with the aim of minimizing a cost function defined by the Integral of Time-weighted Norm of Error (ITNE). To demonstrate the efficacy of the proposed controller, two distinct nonlinear systems, including a second-order system and a third-order magnetic levitation system (MLS), are employed. The simulation results substantiate the controller's capacity to achieve precise trajectory tracking of the reference model, concurrently accommodating system nonlinearities and parametric uncertainties, as well as effectively rejecting external disturbances.</p>