## 6 Conclusions  
This systematic review of 160 peer-reviewed industrial robotics control studies (2023-2025) shows that the field is undergoing a decisive transition from automation-centric Industry 4.0 architectures toward human-centric, sustainability-aware Industry 5.0 ecosystems. Addressing Research Question 1, we find clear performance differentiation across the control paradigms (Table 1): model-free Deep RL improves positional accuracy by  
Figure 7: Evolution of industrial robot control strategies (1980–2030). Predominant control paradigms by decade with representative sources: PID/PLC servo and early real-time control (1980s) [142; 143; 144; 145]; adaptive and fuzzy control expansion (1990s) [146; 147; 148; 149]; constrained MPC for multi-axis motion (2000s) [150; 151; 152]; neural networks, deep learning, and reinforcement learning (2010s) [153; 154; 155; 156]; digital twins, HRC, and collaborative-robot safety in Industry 4.0 (2020s) [150; 112; 113; 137; 141]; emergent 5G/URLLC, edge DRL, and quantum-inspired control (2023–2025) [133; 134; 135; 157]; projected hybrid AI + Edge + Quantum stacks aligned with Industry 5.0 horizons (~2030) [136; 138; 139; 140; 158; 159; 160]. Data compiled from historical, survey, standards, and roadmap sources. -70% and reduces energy by \(\sim\)38% relative to PID baselines; digital twin-assisted MPC and quantum-inspired optimization achieve <10 \(\upmu\)m mean error and <7 J/cycle energy in ultra-precision settings; and bio-inspired evolutionary approaches offer adaptability for redundant, highly non-linear manipulators, albeit with >30 ms cycle-time penalties in fast takt-time lines. Research Question 2 asked how AI, digital twins, and high-bandwidth connectivity (5G/URLLC) are transforming industrial robot control. Evidence synthesized across Sections 3 and 5 indicates that DT-linked reconfiguration can yield \(\sim\)10-20% throughput gains and \(\sim\)15-20% energy savings versus static/fixed-gain baselines in multi-axis cells; 5G non-public industrial networks targeting <10 ms control latency are maturing; and early quantum-inspired scheduling/optimization demonstrates up to \(\sim\)30% control-loop latency reduction in lab-scale URLLC testbeds. Human-robot collaboration layers built atop these digital stacks deliver up to \(\sim\)30% safety improvements beyond static-zone assumptions, while bio-inspired/soft-robotic advances expand task diversity and ergonomic fit (Figure 3; Section 3.7; Appendix A, Table A3). Research Question 3 focused on persistent technical, ethical, and environmental challenges. Cross-cutting gaps remain in computational scalability (multi-agent DRL loads; AM toolpath compute), energy and embodied resource footprints, certifiable fail-safe HRC, cyber-resilience under 5G exposure, ethics/trust in collaborative workcells, and standards for soft-robot durability and circular-economy reporting (Section 4; Table 3). Our sustainability synthesis (Section 3.5; Table 2) shows that integrating energy, material circularity, and life-cycle carbon metrics into control dashboards is both feasible and urgently needed: reported ranges include \(\sim\)20-30% material-waste savings in DT-coupled AM lines and 0.8-1.4% national CO\({}_{2}\)-intensity reductions per +10k industrial robots when coupled with process modernization. Limitations: Findings draw on published data that vary in metric definitions, test conditions, and reporting granularity; only 110 of the 160 studies provided sufficiently comparable metrics for descriptive aggregation. Publication and language filters (English; 2023-2025 window) risk omitting non-indexed innovations and under-representing long-term reliability evidence. Historical foundations predating 2023 were consulted for context (Section 5.3) but not included in quantitative tallies. Reported improvement percentages are directional and tied to the baselines specified in each source; they are not pooled effect sizes.