A novel generalized run-to-run control (GR2R) control strategy is presented for the optimization and control of nonlinear preparative chromatographic processes. The GR2R approach synergistically employs a hybrid (both physical and empirical) model to control chromatographic processes in the presence of sporadic and autocorrelated disturbances. First, parameters of the physical model through experiments are determined, and then the physical model is used to estimate initial parameters of the nonlinear empirical model (Hammerstein) using orthogonal forward regression. Parameters of the nonlinear empirical model are updated at the end of each run using a nonlinear recursive parameter estimation method. The updated empirical model is then used in the control algorithm (model predictive control) to estimate operating conditions for the next batch. Processes operating under fixed optimal conditions are compared with those operating with GR2R control for both gradient and displacement chromatography. The GR2R outperforms the fixed conditions in the presence of various disturbances (such as bed capacity, column efficiency, and feed load) and is an effective strategy for the optimization and control of complex chromatographic processes.