## 3 Advances in Control Strategies  
Industrial robots have significantly advanced, incorporating diverse and innovative control strategies to optimize their performance in complex and dynamic manufacturing environments. Figure 2 details the Industry 4.0 technology stack, showing how digital twins, Artificial Intelligence, 5G connectivity, and quantum computing feed a core set of industrial robotics applications: adaptive and robust control, AI-driven intelligent control, additive manufacturing, human-robot collaboration, and robotic cybersecurity. The convergence of these technologies drives enhanced outcomes--accuracy, energy efficiency, safety, resilience, and scalability--which in turn enables the automation- and connectivity-focused paradigm of Industry 4.0. Building on that foundation, Figure 3 introduces additional human-centric layers--bio-inspired algorithms, circular-economy optimization, and operator well-being and ergonomics--and relabels the application tier as human-centric applications. These extensions shift the optimization target from purely technical KPIs to broader societal metrics, delivering safety, sustainability (energy, materials, life-cycle carbon), inclusion, and re \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Precision** & **Cycle-Time** & **Energy** & **Typical Application** & \multirow{2}{*}{**Key Refs.**} \\  & **(um)** & **(ms)** & **(J/cycle)** & **Domain** & \\ \hline Bio-inspired Evolutionary Control & 18 \(\pm\) 6 & 30 \(\pm\) 9 & 9.2 \(\pm\) 2.0 & Redundant manipulators & [40; 70] \\ \hline Quantum-inspired Optimization & 8 \(\pm\) 3 & 19 \(\pm\) 5 & 6.0 \(\pm\) 1.0 & Multi-robot coordination & [20; 71] \\ \hline \hline \end{tabular}  
* Values are cross-study arithmetic means (\(\pm\) 1 SD) aggregated from the 110 studies with comparable metrics (of the full 160-paper corpus). Precision is expressed as mean Cartesian end-effector error; cycle time is one complete task trajectory; energy is whole-robot electrical consumption per cycle. Baseline PID data provide context for the percentage improvements quoted elsewhere in the manuscript. Context: The full systematic review corpus comprises 160 peer-reviewed studies, including a bio-inspired/human-centric subset (Appendix A, Table A3); studies lacking comparable metrics were synthesized qualitatively and are not reflected in the summary values above.  
\end{table} 
Table 1: _Cont._  
Figure 2: Industry 4.0 technology stack for industrial robotics: Synergistic integration of digital twins, AI, 5G connectivity, and quantum computing. silience as consolidated outcomes. Collectively, these outcomes feed the transition toward Industry 5.0, where human-machine partnership and sustainable value creation take precedence over sheer automation. In summary, Figures 2 and 3 illustrate a two-stage evolution:  
1. Figure 2 (Industry 4.0)--integration of advanced technologies for cyber-physical connectivity and autonomous optimization. 2. Figure 3 (Industry 5.0)--augmentation with human-centric technologies that embed social, ethical, and environmental priorities inside the control loop. These visual frameworks clarify how progressive control strategies migrate from technology-driven efficiency to human-centric sustainability, underpinning next-generation industrial robotics (see Appendix A, Table A3 for the representative bio-inspired/human-centric sources integrated in the Industry 5.0 layers). Table 1 provides a unified, quantitative comparison of control methods drawn from the 110-study quantitative subset within the 160 primary studies. The figures represent arithmetic means (\(\pm\)1 SD) normalized, where reported, to comparable task-level metrics: Cartesian end-effector precision, single-trajectory cycle time, and per-cycle electrical energy (see Section 2.4 for the normalization assumptions). Three key insights emerge:  
1.