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Automation techniques have been widely applied in different industry segments, among others, to increase both productivity and safety. In the mining industry, with the usage of such systems, the operator can be removed from hazardous environments without compromising task execution and it is possible to achieve more efficient and standardized operation. In this work a study case on the application of machine learning algorithms to a mining system example is presented, in which reinforcement learning algorithms were used to solve a control problem. As an example, a machine chain consisting of a Bucket Wheel Excavator, a Belt Wagon and a Hopper Car was used. This system has two material transfer points that need to remain aligned during operation in order to allow continuous material flow. To keep the alignment, the controller makes use of seven degrees of freedom given by slewing, luffing and crawler drives. Experimental tests were done in a simulated environment with two state-of-the-art algorithms, namely Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC). The trained agents were evaluated in terms of episode return and length, as well as alignment quality and action values used. Results show that, for the given task, the PPO agent performs quantitatively and qualitatively better than the SAC agent. However, none of the agents were able to completely solve the proposed testing task.

This paper introduces an international student project on a cyber-physical systems application with an area of concentration in smart traffic light systems. A smart traffic light takes into account the natural flow of traffic which results in a certain traffic rhythm. With this in mind, the smart traffic light control system as part of a cyber-physical system will have an important impact on the quality of urban living in cities. Therefore, the smart traffic light project also focuses on the requirements for using sensors, actuators, and communication technologies to calculate the arrival time of vehicles at a traffic light at a road intersection by monitoring the actual speed of the vehicles. Based on this calculation, it is assumed that the vehicles will arrive on time at the traffic light when it has changed from its red phase to the green phase.