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Autonomous vehicles constitute the next revolution in transportation. They are designed to improve road efficiency and driving safety. Autonomous vehicles are capable, using artificial intelligence, of recognizing their surroundings, navigating, and avoiding obstacles without human intervention. Integrating Artificial Neural Networks (ANNs) into the safety Programmable Logic Controllers (fail-safe PLCs) to create an algorithm that controls an autonomous vehicle and ensures safety on the roads is presented. ANNs are a supervised machinelearning model based on a computing system built to simulate the way the human brain processes and analyzes information. A fail-safe PLC offers a safety concept in the field of machine and personnel protection. The fail-safe system is connected directly to the ANN’s program. It is responsible for shutting down the ANN in cases where the ANN output starts providing false results that could danger humans or property. Also, the fail-safe PLC works in conjunction with the main PLC to serve as a backup to the neural network system. A set of training examples involving over 30 data was evaluated to train the ANN. In addition, a fail-safe PLC program was designed to perform under special conditions. Indoor obstacle avoidance courses were used as an example to examine the effectiveness of the obstacle avoidance system. Simulation results show that the system successfully predicted and responded correctly to the training data and avoided all obstacles on the obstacle-avoidance courses. The system examined real-world data and the experimental results verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper discusses the sensitivity of programmable logic controllers (PLCs) to voltage sags based on mechanistic analysis and numerous tests. First, the sag sensitivity caused by different values of rated working voltage is shown through an analysis of the power supply and discrete input/output modules of PLCs. Second, a detailed sag-tolerance experimental scheme is proposed, and PLC sensitivity curves that differ from early curves are established through several experimental tests. Experiments are conducted with nine typical PLCs. The sensitivity of the power supply and discrete input modules are researched first, and then, the effect of the key characteristics of voltage sag, namely, magnitude, duration, points-on-wave of sag initiation, odd harmonics, multiple sags, and continuous sags, is tested. The uncertain area of the PLC sag tolerance is identified after intensively processing the extensive experimental data and curve fitting. A more accurate sag influence assessment for PLCs can be made with the given curves compared to the early curves. Furthermore, experiments have revealed that certain PLCs may work in an uncertain status during voltage sags. The reason and the method of elimination with the proper choice of dc bus capacitance for PLCs are also analyzed. [ABSTRACT FROM AUTHOR]

Programmable logic controllers (PLCs) are hard real-time embedded systems designed to interact with (through sensors and actuators) and control physical processes in pharmaceutical, manufacturing, energy, and automotive industries. PLCs are the fundamental building blocks in modern industrial automation systems; they are designed to operate in extreme, harsh environments for decades without interruption. This letter presents and evaluates a novel, scalable hardware/software architecture for multi/many-core PLCs capable of pipelining the “sensing-executing-actuating” stages of a control system and thus reducing the scan cycle time (SCT) and maintaining a high-throughput communication (HTC). SCT and HTC are two of the most important key performance indicators (KPIs) in industrial control systems because they determine the accuracy of the control algorithms. [ABSTRACT FROM PUBLISHER]