This paper deals with the design and implementation of a universal cyber-physical model capable of simulating any production process in order to optimize its logistics systems. The basic idea is the direct possibility of testing and debugging advanced logistics algorithms using a digital twin outside the production line. Since the digital twin requires a physical connection to a real line for its operation, this connection is substituted by a modular cyber-physical system (CPS), which replicates the same physical inputs and outputs as a real production line. Especially in fully functional production facilities, there is a trend towards optimizing logistics systems in order to increase efficiency and reduce idle time. Virtualization techniques in the form of a digital twin are standardly used for this purpose. The possibility of an initial test of the physical implementation of proposed optimization changes before they are fully implemented into operation is a pragmatic question that still resonates on the production side. Such concerns are justified because the proposed changes in the optimization of production logistics based on simulations from a digital twin tend to be initially costly and affect the existing functional production infrastructure. Therefore, we created a universal CPS based on requirements from our cooperating manufacturing companies. The model fully physically reproduces the real conditions of simulated production and verifies in advance the quality of proposed optimization changes virtually by the digital twin. Optimization costs are also significantly reduced, as it is not necessary to verify the optimization impact directly in production, but only in the physical model. To demonstrate the versatility of deployment, we chose a configuration simulating a robotic assembly workplace and its logistics.

The paper deals with application possibilities of indoor RTLS (real-time location system) mobile robot navigation. The aim is to point out one of the possibilities of practical RTLS implementation in practice. The work describes the connection procedure between localization technology and the TurtleBot2 mobile robot in the ROS (Robot Operating system) environment. The first part of the work is focused on configuration of the selected RTLS technology, which works on the UWB (ultra-wideband) principle. The configuration as well as the subsequent location data acquisition are already implemented in ROS. The second part of the work deals with TurtleBot2 navigation based on the obtained location data. At the end of the paper are the results summarized obtained in the premises of a robotic laboratory.

This article discusses the possibility of deploying regression recursive continuous identification methods, which are using regularized exponential forgetting, during long-term monitoring of time-varying systems. The emphasis is placed on the long-term deployment since the advantage of this algorithm will only become evident with long-term deployment. These continuous identification algorithms are going to be applied on the mathematical model of the identified system for the needs of adaptive control. The article presents the implemented continuous identification techniques, where the non-informative data, that can possibly destabilize the numerical calculations of the identified system parameters, are weighted by the Dyadic reduction algorithm. These techniques are using an alternative covariance matrix for its “forgetting” in order to maintain the initial system dynamics in the mathematical model of the identified system. The matrix also suppresses the impact of high amounts of non-informative data received from the long-term operation the industrial systems with slow dynamics. The focus is mainly set on the non-informative data, which emerge especially during the long-term run of the tuned systems operating in between the bounds of the selected working point. A “Motor-Flywheel” laboratory model is used for the validation tests of the modified and the standard regression recursive algorithms.