This paper proposes a safety-constrained interaction control scheme for robotic manipulators by integrating model predictive control (MPC) and active disturbance rejection control (ADRC). The proposed method is specifically designed for manipulators with complex nonlinear dynamics. To ensure that the control system satisfies safety constraints during human–robot interaction, MPC is incorporated into the impedance control framework to construct a model predictive impedance controller (MPIC). By exploiting the prediction and constraint-handling capabilities of MPC, the controller provides guaranteed safety throughout the interaction process. Meanwhile, ADRC is employed to track the target joint control signals generated by the MPIC, where an extended state observer is utilized to compensate for dynamic modeling errors and nonlinear disturbances within the system, thereby achieving accurate trajectory tracking. The proposed method is validated through both simulation and real-world experiments, achieving high-performance interaction control with safety constraints at a 2 ms control cycle. The controller exhibits active compliant interaction behavior when the interaction stays within the constraint boundaries, while maintaining strict adherence to the safety constraints when the interaction tends to violate them.