ThisEnhanced Model-Reference Adaptive Control (enhanced MRAC) chapter introduces a robust and scalable decentralized adaptive control framework for large-scale mass–stiffness–damping systemsMass–stiffness–damping system (MKC systemsMKC system), addressing the challenges posed by high dimensionalityHigh dimensionality, nonlinear couplings, and dynamic uncertainties. By decomposing the global dynamics into second-order local subsystems, the approach applies adaptive pole-placement to achieve real-time stabilization and performance tuning across distributed control units. Central to this methodology is a recursive least squares (RLS) estimation algorithm that continuously identifies local dynamic parameters, enabling each controller to adjust active stiffness and damping gains in real time. This ensures rapid response, disturbance rejectionDisturbance rejection, and resilience to modeling errors or changing operating conditions. To enhance fault toleranceFault tolerance and reliability, the adaptive controller is integrated with a model-reference auxiliary system, providing performance continuity during periods of low excitation or adaptation suspension. Through analytical derivation and extensive numerical case studies—including a 26th-order rotor-bearing system—the chapter demonstrates that the introduced control architecture effectively mitigates vibrations, suppresses cross-coupling effects, and preserves stability even under substantial uncertainties. The decentralized design avoids the complexity and fragility of centralized systems, making it well-suited for real-world applications such as rotating machineryRotating machinery, structural vibration control, and interconnected mechanical networks. This work represents a significant advancement in adaptive control for high-order, multivariable systemsMultivariable system operating under uncertain and dynamic environments.