This chapter explores model-based feedback linearization as a powerful control strategy for nonlinear mass–stiffness–damping systemsMass–stiffness–damping system (MKC systemsMKC system), particularly those with coupled actuator dynamics. As real-world systems increasingly exhibit structural and parametric nonlinearitiesParametric nonlinearity, traditional linear control methods often fall short. Feedback linearization offers a transformative approach by algebraically converting nonlinear systems into equivalent linear forms, enabling the application of well-established linear control techniques. Using robotic manipulators with significant motor dynamics as a representative example, this chapter presents two practical algorithms that achieve decoupling and linearization of electromechanical systemsElectromechanical system through serial compensators derived from physical modeling, rather than abstract mathematical constructs. Algorithm 6.1 achieves near-perfect decoupling by transforming the full motor-manipulator dynamics into independent triple integratorsTriple integrator, while Algorithm 6.2 provides a simplified, computationally efficient scheme yielding approximate double integratorsDouble integrator. Both methods account for actuator–link coupling and provide significantly improved tracking performance, particularly in high-speed operations where unmodeled motor dynamics can degrade control. Through detailed derivations and illustrative examples, the chapter demonstrates that a more complete system model—incorporating motor and link dynamics—enables robust and high-precision control. By avoiding complex differential geometryDifferential geometry and offering implementable solutions, this chapter bridgesBridge theoretical rigor and engineering practicality, laying a strong foundation for robust control of nonlinear electromechanical systemsElectromechanical system in modern robotics and beyond.