In recent years, as fractional calculus becomes more and more broadly used in research across different academic disciplines, there are increasing demands for the numerical tools for the computation of fractional integration/differentiation, and the simulation of fractional order systems. Time to time, being asked about which tool is suitable for a specific application, the authors decide to carry out this survey to present recapitulative information of the available tools in the literature, in hope of benefiting researchers with different academic backgrounds. With this motivation, the present article collects the scattered tools into a dashboard view, briefly introduces their usage and algorithms, evaluates the accuracy, compares the performance, and provides informative comments for selection.
Refrigeration control is usually realized by means of model-based feedback controllers, which requires high-computational load and time-consuming model identification efforts. The implementation of feedback control requires a compromise between performance and robust stability. Considering these difficulties, an online learning operation controller for one-stage refrigeration cycle is presented, which consists of two components: a model-based feedback component and a learning feedforward component. The feedback controller is utilized to guarantee robustness. Meanwhile, the optimized performance is reached by the learning feedforward controller including a one-hidden-layer structure with B-spline basis functions. The comparison results of benchmark problems validate the effectiveness of this strategy and show that a perfect tracking performance can still be achieved without extensive modeling.
Temperature control is present in many industrial processes, making this skill mandatory for the control engineers. For this reason, different training temperature platforms have been created for this purpose. However, many of these platforms are expensive, require elaborate facility accommodations, and have higher heating and cooling times, making not suitable for teaching and training. This paper presents a low-cost educational platform for temperature control training. The platform employs a Peltier module as a heating element, which has lower heating and cooling time than other thermal system implementations. A low-cost real-time thermal camera is employed as a temperature feedback sensor instead of a standard thermal sensor. The control algorithm is developed in Matlab-Simulink and employs an Arduino board as hardware in the loop to manage the Peltier module. A temperature control experiment is performed to show that the platform is suitable for teaching and training experiences not only in the classroom but for engineers in the industry.
This paper presents a multi-objective stochastic optimization method for tuning of the controller parameters of Refrigeration Systems based on Vapour Compression. Stochastic Multi Parameter Divergence Optimization (SMDO) algorithm is modified for minimization of the Multi Objective function for optimization process. System control performance is improved by tuning of the PI controller parameters according to discrete time model of the refrigeration system with multi objective function by adding conditional integral structure that is preferred to reduce the steady state error of the system. Simulations are compared with existing results via many graphical and numerical solutions.
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