Water distribution is fundamental to modern society, and there are many associated challenges in the context of large metropolitan areas. A multi-domain approach is required for designing modern solutions for the existing infrastructure, including control and monitoring systems, data science and Machine Learning. Considering the large scale water distribution networks in metropolitan areas, machine and deep learning algorithms can provide improved adaptability for control applications. This paper presents a monitoring and control machine learning-based architecture for a smart water distribution system. Automated test scenarios and learning methods are proposed and designed to predict the network configuration for a modern implementation of a multiple model control supervisor with increased adaptability to changing operating conditions. The high-level processing and components for smart water distribution systems are supported by the smart meters, providing real-time data, push-based and decoupled software architectures and reactive programming.

A system with multiple controllers and a multiple-model architecture is one of the most effective solutions for the real-time control of nonlinear systems. The employment of these structures necessitates the resolution of certain difficulties, such as selecting the optimal algorithm or switching control algorithms. Based on the concepts of auto-transfer, the paper provides a way for switching the numerous controller structures’ algorithms. This paper presents a real-time dynamic model and platform of a magnetic levitation system (Maglev). The method’s applicability was demonstrated by utilizing a real-time architecture with an RST controller mechanism and real. In conclusion, the software was implemented and demonstrated by using the LabVIEW platform in real-time, and the results reveal that this solution can stabilize the ball’s location and has strong disturbance rejection because of the multi-zone effect.

The introduction of Machine Learning in large scale utility networks extends the room for improvement in the quality of service and maintenance costs. The ever expanding network of smart meters allows for a more accurate estimation of the state of the water distribution systems, at the same time requiring modern data processing solutions. By fusion with the more traditional approach in this field of research it is possible to enhance the existing capabilities for network analysis and to extend the algorithms to the level of cognitive abilities that form a basis for more efficient decision support system. In this paper we extend the fault sensitivity analysis for water distribution systems with the insights provided by state-of-the-art Machine Learning algorithms for data clustering and anomaly detection.

Graphics processing units and video cards have seen a surge of usage in domains other than graphics computers, due to advances in hardware and software technologies; however, little uptake has been in the domain of systems engineering and real time control. This research article will demonstrate the use of video cards in multimodel adaptive robust control, using openGL and compute shaders. A software simulation will show the behavior of the adaptive robust multimodel control scheme as the target process is exposed to both parametric and structural disturbances and will show the viability of using graphics processing units in real time systems control.

One of the key functions of control techniques of the longitudinal dynamics vehicle is an Adaptive Cruise Control (ACC) system employing hierarchical, which keeping distance desired between the preceding vehicle and following vehicle. The upper controller calculates the desired acceleration, while the lower controller provides the throttle/brake commands based on the output signal of the upper controller for the following vehicle. To coordinate between the throttle and brake control are smooth, a logic switch is proposed with a boundary layer. The following vehicle establish a nonlinear longitudinal model, containing of vehicle inertial, powertrain dynamics. The brake system is simplified to be a first-order model. The simulation results show that the performance of the proposed methods is demonstrated to be very practical and these results clearly highlight the benefits of the logic switch and using brake control compared to those observed [6].