In the six decades of conventional TUNING-BASED adaptive control, the unattained fundamental goals, in the absence or detrimental artificial excitation, have been (1) exponential regulation, as with robust controllers, and (2) perfect learning of the plant model. Such unattained fundamental goals have hampered real-time learning in aerospace vehicle control. Over a quarter century after I started my career by extending conventional adaptive controllers from linear to nonlinear systems, I reach those decades-old goals with a new non-tuning paradigm: regulation-triggered batch identification. The parameter estimate in the controller is held constant and, only once the regulation error grows "too large," a parameter estimate update, based on the data since the last update, is "triggered." Such a simple parameter estimator provably, and remarkably, terminates updating after a number of state growth-triggered updates which is no greater than the number of unknown parameters. This yields exponential regulation and perfect identification except for zero-measure initial conditions. I present a design for a more general class of nonlinear systems than ever before, a flight control example (the "wing rock" instability), and an extension to a PDE problem. This is joint work with Iasson Karafyllis from the Mathematics Department of the National Technical University of Athens.