Industrial wireless sensor and actuator networks are increasingly used in closed-loop control systems. In such systems, packet dropout, channel degradation, and communication delay can deteriorate tracking performance. This paper proposes a Radio-Quality-Aware Model Predictive Control (MPC) strategy for an industrial conveyor drive system operating under time-varying wireless communication conditions. Unlike classical MPC, the proposed controller incorporates radio-channel information, including the signal-to-interference-plus-noise ratio (SINR), packet error rate (PER), and network-induced delay, into the predictive control formulation, thereby adapting the control action to degraded communication conditions. In the MATLAB simulations, an industrial load torque profile based on sensor-measured signals was used, and the wireless communication scenario was divided into three regions representing favorable, moderately degraded, and severely degraded conditions. The results demonstrate that the proposed MPC maintains the angular speed closer to its reference value than the classical MPC, particularly under severe degradation when the SINR decreases to 5 dB. Compared with the classical MPC, the proposed Radio-Quality-Aware MPC reduces the integral of absolute error (IAE) by approximately 90.6%, the root mean square error (RMSE) by 91.4%, the maximum absolute tracking error by 92.7%, and the input variation index by 67.2%, while maintaining a comparable level of control energy. Overall, these results demonstrate that the proposed Radio-Quality-Aware Model Predictive Control framework improves tracking accuracy, actuator smoothness, and system robustness in industrial wireless control applications.

Hybrid-actuated linkage mechanisms combine a main drive with a controllable auxiliary input to shape motion while distributing actuator demand. This narrative review examines mechanism design, control strategies, and energy management for presses and related cyclic machinery. The mechanisms are grouped into five-bar, seven-bar, slider-crank and differential, and application-specific configurations. The synthesis identifies trade-offs among motion programmability, dwell generation, actuator loading, and structural complexity; it does not establish a universal ranking of topologies. Control methods are compared by function, including local feedback, adaptation and iterative learning, disturbance rejection, predictive optimization, and position–force or position–pressure switching. Energy measures are distinguished as demand shaping, loss reduction, regeneration, storage, and auxiliary harvesting. Press and die-cushion studies provide application-relevant evidence, whereas many robotic, vehicle, and vibration-harvesting studies provide transferable principles requiring further validation. Reported servo-energy shares, torque reductions, installed power, and energy per cycle cannot be compared as equivalent efficiency indicators. An integrated design framework links mechanism selection, control organization, and energy flow across the operating cycle. Future comparisons require common forming tasks, explicit energy-accounting boundaries, and experimental validation of coupled mechanical, control, and storage decisions.

Integrated Stephenson II linkage with MPC and sensors; experimentally validated precise force control, consistent quality, reduced wear in high-load presses.