This work focuses on the development of a unified practical framework for control of single-input-single-output nonlinear processes with uncertainty and actuator constraints. Using a general state-space Lyapunov-based approach, the developed framework yields a direct nonlinear controller design method that integrates robustness, optimality, and explicit constraint-handling capabilities, and provides, at the same time, an explicit and intuitive characterization of the state-space regions of guaranteed closed-loop stability. This characterization captures, quantitatively, the limitations imposed by uncertainty and input constraints on our ability to steer the process dynamics in a desired direction. The proposed control method leads to the derivation of explicit analytical formulas for bounded robust optimal state feedback control laws that enforce stability and robust asymptotic reference-input tracking in the presence of active input constraints. The performance of the control laws is illustrated through the use of a chemical reactor example and compared with existing process control strategies.