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.

The deployment of autonomous predictive vibration-based diagnostics in Edge-IIoT requires balancing the cost of Micro-Electro-Mechanical System (MEMS) accelerometers against their metrological limitations. This study proposes a parametric Sensor Digital Twin (SDT) framework for virtual benchmarking of measurement chains at the hardware-software co-design stage. The SDT model emulates mechanical and electrical filtering, aliasing, noise, and quantization; its fidelity was validated experimentally using a physical ADXL345 sensor, with an RMS noise error of 8.56%. Virtual profiles of the commercial ADXL345 and ADXL357 sensors were generated using the reference NLN-EMP centrifugal pump dataset acquired with a Wilcoxon 786B-10 piezoelectric accelerometer. Their diagnostic performance was evaluated using eight diagnostic features and five heterogeneous machine-learning algorithms under interpolation, forward extrapolation, and backward extrapolation scenarios across fault severity levels. In the interpolation scenario, the ADXL345 and ADXL357 profiles achieved Macro F1-scores of 0.8999 and 0.8947, respectively, compared with 0.9567 for the reference measurement chain. In the forward extrapolation scenario, the ADXL357 profile achieved a Macro F1-score of 0.7171, compared with 0.6768 for the reference measurement chain. The results confirm that the SDT can support sensor hardware selection through virtual benchmarking, while the considered MEMS accelerometers provide comparable diagnostic performance in the evaluated scenarios when a representative training dataset is available.

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.

Amid Kazakhstan’s accelerated digitalization and national smart-manufacturing agenda, this paper presents a practice-oriented economic framework for evaluating, at the pre-design stage, the feasibility of deploying collaborative and mobile robotics in the automotive industry. The goal is to align engineering choices with production economics before on-site trials, reducing the risk of inflated expectations and showcase-only pilots. Methodologically, the framework combines conceptual modeling with quantitative screening: it decomposes total cost of ownership into capital and operating elements; maps operational effects across body/assembly stations, quality control, and inplant logistics; and ties these effects to measurable KPIs – overall equipment effectiveness (and its Availability- Performance-Quality factors), cycle time, defect rates, planned/unplanned downtime, and safety incidents. A distinctive feature is the explicit integration of infrastructure constraints and risk factors – precision and calibration requirements, connectivity and communication quality, safety for human – robot collaboration, cybersecurity exposure, and workforce acceptance – into both the economics and a managerial go/no-go checklist. The framework yields (i) a reproducible pilot → expand → scale pathway with milestone metrics, (ii) a structured data package to enable subsequent statistical verification and learning across pilots, and (iii) adaptable accounting rules that fit local procurement and budgeting practices. By making trade-offs transparent and context-aware, the approach supports Kazakhstani automotive firms in selecting viable use cases, sizing investments, and sequencing deployments in line with national digitalization priorities