# Pushing Mechatronic Applications to the Limits via Smart Motion Control
## 1 Introduction  
Recently, imposing technological achievements have been announced in miscellaneous fields of actuation, sensing, control design, MBSE [1, 2, 3, 4, 5], and secure communications [6]. They are key drivers of modern the cyber-physical world. As mentioned in [7], those technology areas were developed often independently in the past, hence not respecting generic principles specified by control community [8, 9, 10] and applicable across domains. In particular, numerous feedback control loops, vertically distributed through several layers, imply strict conditions for all elements of the ecosystem. Hence actual matters in modern machines, in particular their control parts, brought fully new requirements on every part of the whole system including mutual integration. They must be known and accepted within the whole supply chain. Smart mechatronic systems face rising constraints on speed, precision, adaptability, size, self-diagnostic and interoperability while simultaneously needing to accommodate ever more demanding smart cognitive features. Compared to [7], a list of specific challenges can be updated as:  
* Computer vision and AI at-the-edge are and will be key enablers for industrial automation/robotics, namely, for fast inspection and motion through vision in-the-loop, sensing feature information, which is not measurable with 'classical' techniques. This requires new SoC (System on Chip) and ASIC components. * Growing flexibility (elasticity) of mechatronic systems together is causing increased presence of residual vibrations [11; 12; 13]. Together with growing speed, they often overlap with bandwidth required for feedback control. * Trajectories of robots in general have periodic signatures; therefore, feedback control algorithms should handle ultrafast repetitive disturbances and setpoints [14; 15]. * The previous additional energy-effective measurements on moving or rotating parts that can be done only in a wireless way. Thus, fast deterministic industrial wireless communications must be considered in the 'chain'. * The robots and mechatronic systems are composed of highly complex kinematic platforms [16] with numerous flexible loads and interacting dependent axes [17]. Consequently, control devices should be capable to execute advanced control strategies (centralized) [18]. * Routine machine adaptation to new environments and new products is necessary, hence self-adaptation principles are requested [19]. * The machines/robots must often work in complex multi-stage production lines (often collaborative) with zero defects, hence must be fully connectable to upper factory layers and provide self-diagnostic/inspection information, i.e., follow Industry 4.0 paradigms. In this context, drive self-diagnostic and performance assessment were identified as essential. As a response to the above-mentioned challenges, the I-MECH project defined various measurable objectives, which are categorized as scientific and technical (ST), system integration (SI), and system operational (SO) as follows:  
**ST1:** To develop toolchains for utilization of progressive MBSE methods for RT control, design, and automatic assessment of robots while the latest standards for co-simulation are considered, e.g., FMI (functional mock-up interface) [20; 21; 22; 23]. **ST2:** To develop a smart Instrumentation Layer collecting sensor or visual data from accessory electronic components that are joined to the moving segments of the mechatronic system to increase its available performance [24; 25; 26] (Section 4). **ST3:** To produce interoperable and modular SW and HW building blocks (BB) that are consistent with the SOA paradigm, i.e., smart Control Layer [27; 28; 29; 30] (Section 4). **ST4:** To provide connectivity to predictive production planning layer (System Behavior Layer) and create particular self-diagnostic and condition monitoring BBs. They should be executed at the lower layer and gain important information for plant-wide decisions.