## 3 Advances in Control Strategies
### Energy-Efficient Control Strategies  
Energy efficiency is increasingly critical in industrial robotics for cost reduction and alignment with sustainability targets across Industry 4.0 and Industry 5.0. [PERSON] et al. [63] reduced energy consumption by 17% relative to cubic-spline trajectory planning. [PERSON] et al. [80] cut energy use by 20% compared with round-robin Ultra-Reliable Low-Latency Communications (URLLC) scheduling. [PERSON] et al. [74] achieved a 15% power reduction versus proportional pressure control, supporting sustainability objectives in both the Industry 4.0 and 5.0 frameworks [73; 81; 82]. [PERSON] et al. [43] lowered energy consumption by 17% relative to rate-coded neural policies, and [PERSON] et al. [19] saved 18% energy compared with motor-driven parallel-jaw grippers. [PERSON] et al. [68] reported a 15% reduction in energy demand through IoT-integrated robotic systems versus non-IoT networked operation, reinforcing emerging sustainable design practices.  
Circular-Economy Metrics  
Industrial sustainability must extend beyond kilowatt-hours to encompass material circularity and life-cycle emissions. A recent gate-to-gate life-cycle assessment (LCA) of robot remanufacturing found that refurbishing a six-axis arm returns ~800-1200 kg year\({}^{-1}\) of steel and aluminum to the supply chain, saving 60-80% of the raw material required for a new unit [83]. A complementary regulatory analysis under the new EU Machinery Regulation formalizes \"remanufacture-ready\" design provisions, effectively embedding circularity into safety compliance [84].  
Emissions-side KPIs are equally important. Process-level LCAs report that a robot-assisted printing cell emits \(\sim\)2.2 kg CO\({}_{2}\)-eq h\({}^{-1}\) when amortized over 10 years [85], whereas payload-optimized trajectories yield up to 15% energy savings relative to fixed-speed programs [86]. Macro-level econometric studies show 0.8-1.4% CO\({}_{2}\)-intensity reductions per additional 10k robots against pre-automation baselines [87; 88], and AI scheduling can reduce firm-level carbon intensity by \(-\)0.04% relative to static MES planning [89].  
Selecting robust indicators remains non-trivial. Recent reviews call for harmonized circular-economy metrics linking material flow, energy, and socio-economic data across the micro- (process), meso- (factory), and macro-levels [90; 91; 92]. [PERSON] [90] systematically reviews circular-economy metrics and advocates standardized frameworks to improve comparability; [PERSON] et al. [91] highlight the integration gaps in manufacturing sustainability reporting (\(\approx\)10% uptake shortfall); [PERSON] et al. [92] outline a research agenda identifying \(\sim\)15% cross-sector alignment gaps. European benchmarking ([PERSON] et al. [93]) shows wide regional dispersion--Spain currently realizes only \(\sim\)32% of its potential secondary materials' utilization--underscoring the distance between policy intent and shop-floor implementation.  
Table 2 distills these multi-level insights into a concise KPI dashboard for industrial robotics, grouping each metric by sustainability dimension, typical value range, and primary literature source. Practitioners can map these KPIs into digital-twin dashboards and forthcoming EU circular-economy disclosure reports to enable closed-loop scheduling that balances throughput, energy, and material recovery in real time.  
Embedding these KPIs into digital-twin dashboards can enable closed-loop scheduling that balances throughput, energy demand, and circularity in real time, aligning robot deployments with the Industry 5.0 goals of resilience and sustainable value creation.