This paper presents a Sim2Real policy transfer framework for distributed control in cyber-physical production systems using State-Based Potential Games (SbPGs). While fuzzy inference systems (FISs) or other conventional control policies provide interpretable and stable control policies for manufacturing processes, their direct deployment in real systems is often affected by Sim2Real discrepancies caused by actuator imperfections, sensor uncertainty, and process variability. To address this limitation, we propose a hybrid control architecture in which an optimized rule-based conventional control policy (i.e., FIS used in a non-adaptive, expert-knowledge-driven manner) serves as a baseline controller and SbPG-based policy adaptation refines the control actions online, while keeping the distributed manner, and is proven to converge. To evaluate robustness during Sim2Real deployment, deterministic and stochastic noise injection mechanisms are introduced to emulate systematic actuator biases and random disturbances. The proposed framework is validated on a laboratory-scale distributed production system. Experimental results in both simulation and real-world environments demonstrate that the SbPG-based adaptation compensates for disturbances and maintains production objectives under actuator, sensor, and parameter uncertainties. Compared to standalone FIS control, the proposed approach consistently reduces overflow and power consumption while satisfying production demands under noisy operating conditions. Additional ablation studies further confirm the robustness of the policy transfer strategy and the effectiveness of global and local interpolation mechanisms in the SbPG learning.
Yuwono et al. (Mon,) studied this question.