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1. FLC-ROS: A generic and configurable ROS package for developing fuzzy logic controllers1

   https://doi.org/10.3233/JIFS-210341  

   Karimoddini, Ali; Hailemichael, Abel; Jamshidi, Mo (April 2022, Journal of Intelligent & Fuzzy Systems)

   Fuzzy logic controllers can handle complex systems by incorporating expert’s knowledge in the absence of formal mathematical models. Further, fuzzy logic controllers can effectively capture and accommodate uncertainties that are inherent in real-world controlled systems. On the other hand, Robot Operating System (ROS) has been widely used for many robotic applications due to its modular structure and efficient message-passing mechanisms for the integration of system’s components. For this reason, Robot Operating System is an ideal tool for developing software stacks for robotic applications. This paper develops a generic and configurable Robot Operating System package for the implementation of fuzzy logic controllers, particularly type-1 and interval type-2, which are based on either Mamdani or Takagi-Sugeno-Kang fuzzy inference mechanisms. This is achieved by employing a systematic object-oriented approach using the Unified Model Language (UML) to implement the fuzzy inference system as a single class that is composed of fuzzifier, inference, and defuzzifier classes. The deployment of the developed Robot Operating System package is demonstrated by implementing an interval type-2 fuzzy logic control of an Unmanned Aerial Vehicle (UAV). 

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2. Fast and Secure Operation of Voltage Source Inverter based DERs using Model Predictive Droop Control

   https://doi.org/10.1109/ECCE.2018.8558323  

   Alhosaini, Waleed; Mahmud, Mohammad Hazzaz; Zhao, Yue (September 2018, Fast and Secure Operation of VSI based DERs using Model Predictive Droop Control)

   Reliability of the power grid can be improved by the use of microgrids (MGs) concept, which regulates the voltage and frequency at the point of common coupling (PCC) during normal and/or faulty conditions. Droop characteristics based hierarchical control strategies are commonly used in MGs, where power converters can operate in parallel. However, the need of multiple control loops not only adds complexity to the controller design, but also reduces the dynamic response of the system. In the future power system, grid-tied converters with fast dynamic response are desired to handle the uncertainties induced by high penetration of distributed energy resources. Therefore, this paper presents a novel model predictive control to ensure fast dynamic response of high power three-level converters in stand-alone operating mode as well as grid-tied operating mode. The proposed controller is applied to a MG which consists of a solar inverter connected in parallel with an energy storage system to the PCC, where a local load is tied. Both simulation and experimental results are presented to demonstrate robustness and the high dynamic performance of the proposed controller under rapidly changing atmospheric conditions and different grid operating modes. 

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3. Inexactness of Second Order Cone Relaxations for Calculating Operating Envelopes

   https://doi.org/10.1109/SmartGridComm57358.2023.10333939  

   Moring, Hannah; Mathieu, Johanna L. (October 2023, IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids)

   As the number of distributed energy resources participating in power networks increases, it becomes increasingly more important to actively manage network constraints to ensure safe operation. One proposed method that has gained significant attention and implementation, particularly in Australia, is the use of dynamic operating envelopes. Operating envelopes represent net export limits set by the system operator on every node in the distribution network that change as system conditions change. They are calculated using an optimal power flow problem, frequently using a linearization or relaxation of the nonlinear power flow equations. This paper presents two case studies and some numerical analysis to explain why a second order cone relaxation of the power flow equations will lead to ineffective operating envelopes. A modification to the objective function which allows the second order cone relaxation to nearly recover the solution to the nonlinear formulation is also presented. 

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4. Reshaping Load-Dependent Mesh Excitation Waveforms of Spur Gears—An Analytical Framework on Tip Relief Modeling and Design

   https://doi.org/10.3390/machines13020161  

   Zheng, Xingyuan; Zhu, Weidong; Li, Gang; Hu, Yumei (February 2025, Machines)

   Tip relief is a critical design feature of modern spur gears, aimed at improving dynamic performance through a typical design strategy involving peak-to-peak minimization of mesh excitations. However, due to the hyperstatic nature of simultaneous tooth engagements, the applied torque not only affects mesh deformation amplitudes as normally considered but also alters mesh excitation waveforms, leaving great challenges for the typical design to meet various operating conditions. This paper develops an analytical framework to reshape mesh excitation waveforms, aimed at flexibly reducing vibration intensities across different operating loads and speeds. The load-dependency of excitation harmonics with tip relief is efficiently characterized by an improved analytical mesh excitation model. A tip relief design method is proposed, which automatically recombines harmonic contents of mesh excitations to adapt target operating speeds. Comparisons with finite element models and experiments confirmed the accuracies of quasi-static and dynamic analyses. Parametric studies and application examples further demonstrate the acceptable feasibility and effectiveness of the present method. 

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5. Machine learning enabled integrated formulation and process design framework for a pharmaceutical 3D printing platform

   https://doi.org/10.1002/aic.17990  

   Sundarkumar, Varun; Nagy, Zoltan_K; Reklaitis, Gintaras_V (December 2022, AIChE Journal)

   Abstract The pharmaceutical manufacturing sector needs to rapidly evolve to absorb the next wave of disruptive industrial innovations—Industry 4.0. This involves incorporating technologies like artificial intelligence and 3D printing (3DP) to automate and personalize the drug production processes. This study aims to build a formulation and process design (FPD) framework for a pharmaceutical 3DP platform that recommends operating (formulation and process) conditions at which consistent drop printing can be obtained. The platform used in this study is a displacement‐based drop‐on‐demand 3D printer that manufactures dosages by additively depositing the drug formulation as droplets on a substrate. The FPD framework is built in two parts: the first part involves building a machine learning model to simulate the forward problem—predicting printer operation for given operating conditions and the second part seeks to solve and experimentally validate the inverse problem—predicting operating conditions that can yield desired printer operation. 

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