This final chapter synthesizes the key concepts and methodologies presented throughout the book, offering a cohesive perspective on model-based control for mass–stiffness–damping systemsMass–stiffness–damping system (MKC systemsMKC system). Emphasizing that all control is inherently model-driven—whether derived from first principles or data—the chapter highlights the dual role of models: as tools for understanding plant behavior and as references for shaping desired system dynamics. The core message of this book is clear: even imperfect models can serve as powerful enablers of robust control when uncertainty is explicitly addressed. Through techniques such as structural augmentationStructural augmentation, internal model controlInternal Model Control (IMC), and adaptive feedback, the text demonstrates how high-performance control can be achieved by blending theoretical precision with practical design simplicity. A modular, multiloopMultiloop control architecture underpins the introduced strategies, balancing complexity with transparency. Inner-loop linearization, outer-loop feedback, integral actionIntegral action, model-following compensation, and feedforward compensationFeedforward compensation are seamlessly integrated to deliver adaptable, robust performance—even in uncertain or time-varying environments. Looking ahead, the chapter discusses the evolving role of adaptive and intelligent control in future systems that demand autonomy, fault toleranceFault tolerance, and real-time learning. The book concludes by encouraging hybrid approaches that unify classical control with modern AIArtificial Intelligence (AI) tools—paving the way for resilient, data-aware control systems capable of thriving in increasingly dynamic and uncertain landscapes.