## 2 Summary of the Special Issue Contents
The core model is minimalist as far as its underlying assumptions are concerned. Hence, it does not constrain the decision autonomy of collaborating cyber-physical entities and \"only\" provides channels for transferring and synchronizing the information that ensues from their decisions. The proposed approach identified the elementary concepts, such as functions, calls, variables, and reports, as the basis for modeling and providing I4.0-compliant, CPS-based services in a manufacturing environment. They were developed via standardized technologies enabling semantic interoperability and openness (OPC UA, MQTT). The universal CPS-based service integration mechanism was validated in an experimental pilot production and logistics system that included a variety of heterogeneous and autonomous resources, such as manufacturing cells, AGVs, robots, and human-robot collaborative cells. These CPS components were connected and controlled via the plug and collaborate mechanism of a MES system in a number of complex scenarios. The manuscript \"_Decentralized Multi-Agent Control of a Manipulator in Continuous Task Learning_\", by [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [8], formulates a decentralized and multi-agent approach in the form of an RL (Reinforcement Learning) problem, demonstrating the possibility of decentralizing a single manipulator controller by applying multiple agents in learning continuous actions. The purpose of this paper was to compare the feasibility of decentralization for a single robot to show the modularity in the joints, as well as to make a comparison between the centralized and decentralized approach. The results show that it is possible to decentralize the control action on the robot. Using multiple agents shows the stagnation of the learning process when using the same reward function that is used for a single agent. It is believed that this is due to the lack of communication between the agents and the generality of the reward when considering two agents. The paper \"_Predictive Control for Small Unmanned Ground Vehicles_ via _a Multi-Dimensional Taylor Network_\", by [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [9], presents an improved predictive control scheme based on the MTN (multi-dimensional Taylor network) for tracking the control of SUGVs (Small Unmanned Ground Vehicles). The traditional objective function was improved to obtain a predictive objective function with the differential term. The optimal control quantity was given in real time through iterative optimization. A tracking control experiment was carried out on an SUGV to verify the effectiveness of the proposed scheme. The results show that the proposed scheme is effective and has good a real-time, robustness, and convergence performance, which ensure that the vehicle can quickly and accurately track the desired yaw velocity signal and that it is superior to the traditional MTN and RBF (Radial Basis Function) predictive control schemes. The article _\"Design of a NARX-ANN-Based SP Controller for Control of an Irrigation Main Canal Pool\"_, by [PERSON], [PERSON] and [PERSON] [10], proposes a modification of the well-known [PERSON] predictor controller, in which the internal linear model was substituted by the combination of a NARX-ANN-based model and a TD-NARX-ANN-based model, in order to take into account the dynamic nonlinearities in the effective control of water distribution in an irrigation main canal pool. By the application of system identification procedures, NARX-ANN and a TD-NARX-ANN with recurrent architectures were obtained which describe with high accuracy the non-linear dynamic behavior of the water distribution in the studied canal pool. The NARX-ANN structure with an input layer with 13 memory blocks for the input and output signals, three neurons in the hidden layer and one neuron in the output layer, provided the best model performance. A third model--which was linear and represented by a time-delay first-order transfer function--was obtained using an identification procedure.