<p>Robot teleoperation plays an important role in industrial manufacturing in unknown and dangerous environments beyond human reach. In telerobotic manufacturing tasks, environmental interaction forces may vary significantly from task to task. Therefore, it is crucial to provide operators with the specific proportional feedback of environmental interaction forces to enhance their environmental awareness and manipulation capabilities. However, variable time delays and various scales of environmental interaction force feedback seriously affect the system stability, which should be rigorously addressed when designing control parameters. To cope with these difficulties, a position and scaled force tracking control framework is proposed and the Lyapunov-Krasovskii theory is used to obtain a simple algebraic stability criterion with the scaling factor of the environmental interaction force feedback. In addition, a low-pass filter-based radial basis function neural network is designed to avoid the effect of the measurement noise and the sudden change of the non-passive environmental interaction force on the system stability. Compared with different controllers in various telerobotic manufacturing tasks such as heavy lifting, cutting, and polishing, our proposed method achieves better position and scaled force tracking performance.</p>