This book presents the design of efficient adaptive nonlinear controllers for strict-feedback nonlinear systems by taking into account multiple challenges for increased safety and reliability. The considered challenges are external disturbances, uncertain dynamics, actuation faults, unmeasured states, constrained input, unknown control direction, and singularity in the control law. Some adaptive nonlinear control schemes, which are relatively easy to apply for practitioners in order to tackle simultaneously and efficiently some and/or all aforementioned issues are presented. The book presents the design of schemes based on a reaching law-based Sliding Mode Control strategies, combined with the Input-Output Feedback Linearization technique. These schemes use Radial Basis Function Neural Networks (RBFNN) and Fuzzy Neural Networks (FNNs) to approximate the unknown system dynamics. It is illustrated how a model-free high-gain state observer is employed for estimating the unavailable system state variables. For dealing with the unknown control direction, a Nussbaum type function is presented. The schemes presented in this book have a wide scope of potential applications as they overcome important restrictions imposed by some assumptions found in many works, while ensuring very good transient and steady state performances (no peak overshoot, shorter settling time, smaller error bound and Root-Mean-Square-Error) with low leveled continuous control efforts. These canceled restrictions are the requirements about the knowledge of bounds for system dynamics uncertainties, for RBFNN or FNN approximation errors, for actuator's faults and for external disturbances, the knowledge of control direction, the availability of full-state measurement, and the requirement about