This chapter introduces a framework for enhancing the robustness of model-based controllers in uncertain dynamic systems. Traditional control algorithms often rely on idealized models, leaving them vulnerable to performance degradation in the presence of parameter variations, unmodeled dynamics, and external disturbances. To address this, we present an enhanced model-following controlEnhanced Model-Following Control (enhanced MFC) (MFC) structure that actively reduces model mismatches through a parallel compensationParallel compensation loop, referred to as an uncertainty reducerUncertainty reducer. By integrating a nominal model into the control loop, the system dynamically compensates for deviations from expected behavior, attenuating internal uncertainties and improving disturbance rejectionDisturbance rejection. This dual-model-based approach combinesSerial pre-compensator serial pre-compensatorsPre-compensator for decoupling and linearization with parallel post-compensatorsPost-compensator for robustness enhancement—achieving high tracking precision without sacrificing computational efficiencyComputational efficiency. Through rigorous theoretical analysis, numerical simulationsNumerical simulation, and experimental validationsExperimental validation, the chapter demonstrates how the introduced architecture significantly outperforms conventional high-gain or model-based designs. The enhanced controller maintains accurate trajectory trackingTrajectory tracking and zero steady-state error even under severe uncertainties, such as payload changes, unmodeled damping, and actuator dynamics. Importantly, the framework is modular, intuitive, and easily integrated into existing systems—offering a practical and scalable solution for real-world applications that demand both performance and resilience.