This paper is focused on the design and verification of the characteristics of a speed control loop with a fractional-order PI controller. The controller parameters are tuned by two methods: the pole placement method and the phase margin method. The fractional-order controller itself has been implemented in two ways, namely using MATLAB FOMCON Toolbox and in the form of finite power series expansion. The characteristics of the speed control loop using both tuning methods and with both types of controllers have been verified experimentally on an industrial servo drive with permanent magnet synchronous motors.
An appropriate design of the model predictive control algorithm for the artificial pancreas to control glycemia in patients with type 1 diabetes should ideally take into account the asymmetrical nature of glycemia dynamics that consequently results in asymmetrical requirements on the control performance with respect to normoglycemia. This demand is primarily physiology involved since the state of hypoglycemia is much more dangerous and easier to reach by careless insulin administration than hyperglycemia. As the traditional formulation of model predictive control considers only symmetrical penalization of the control error, a more sophisticated strategy is required. One of the approaches is to apply hard constraints on the controlled variable. However, the issue with this is that the control may become infeasible if there are considered too tight constraints of the manipulated variable and the controlled variable at the same time. Therefore, in order to ensure the control feasibility while constraining the controlled variable, we proposed a soft constraints formulation introducing special slack variables and a quadratic penalty to the corresponding cost function. Independently weighted soft constraints of the controlled variable with asymmetric bounds are considered while anticipating the desired suppression of hypoglycemia during automated glycemia control. The proposed strategy was validated within a simulation experiment, and it turned out that the soft constraint formulation allowed avoiding hypoglycemia at the expense of mild hyperglycemia.
Robot guidance in industry is a significant issue that needs to be dealt with in modern manufacturing facilities. One of the common tasks in this area is the pick and place problem. For proper implementation of an automatic pick and place application using a robotic arm for object grasping, it is necessary to detect the accurate pose of the objects of interest. In this contribution, a novel engineering approach to object positioning, based on image processing is proposed. In this approach, the operation is composed of a cascade of convolutional neural networks. This cascade consists of 2 different types of networks. The first one is the object detection network called YOLOv5. It is used to process the raw image data from the scene to provide precise localization and determine the position of the objects of interest. After that, crops of the detected objects are created and processed by the second neural network, namely EfficientNet. This classification network is used to determine the rotation angle of the detected objects. The proposed approach provides a precision rate of 0.997 and a recall rate of 0.999 for locating and determining the correct position. For angle classification, EfficientNet provides an accuracy of 0.951. All tests are performed on the testing set of the legitimate positioning problem.
The paper shows how the aeronautical heritage is affected by airborne pollutants and the air temperature-humidity complex. Monitoring meteorological and environmental data in a heritage site intended for protection, these effects are analyzed. The starting point is thus the acquisition of the meteorological and environmental data from the intended hangar with stored historical aircrafts taken part in fights of the second world war. The following step is an evaluation of the penetration of moisture into the hangar environment from outside. This moisture penetration is deducted from wet/dry cycles obtained from air humidity and temperature fluctuations in the hangar environment. In addition, an evolution of the aircraft surface temperature is pertinent to potential moisturizing the aircraft surface. As a measure of moisture penetration, a time of wetness (ToW) is determined indoors. Furthermore, pollutants infiltrated into the hangar environment are derived from a standardized methodology. Beside the pollution infiltration, the indoor generated pollution is estimated per material from which the historic aircrafts are constituted. Because of major material in the aircraft, the deposition rate of indoor pollutants onto an aluminum alloy is determined by means of a standardized statistical model. Finally, monitoring the hangar environment, the heritage aircraft vulnerability to corrosion is estimated based on atmosphere corrosivity modeling.
The success of the real-world implementation of advanced control policies relies on the robustness of the designed control laws. This paper presents a new software package for the robust model predictive control (MPC) synthesis in the framework of the Multi-Parametric toolbox (MPT) that is representing one of the most successful open-source tools in this field. In this paper, we introduce the Tube MPC design in a user-friendly way. The goal of the paper is to demonstrate that the wide research community may benefit from the ease of the advanced controller design in a few lines of code and its implementation to control the laboratory plant.
