This article describes the results of a TCS Programme that investigated approaches for the improved control of a French fry production line. Initial knowledge elicitation from process experts, coupled with data analys...
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This article describes the results of a TCS Programme that investigated approaches for the improved control of a French fry production line. Initial knowledge elicitation from process experts, coupled with data analysis, revealed the existing operational strategy. The information gathered highlighted limitations of the current strategy, indicated how it could be improved on and allowed a benefits analysis to be undertaken to financially Justify the work. Results from the implementation of the control enhancements demonstrate that the predicted improvements were achieved.
This paper presents a comprehensive review of advanced control methods specifically designed for floating offshore wind turbines (FOWTs) above the rated wind speed. Focusing on primary control objectives, including po...
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This paper presents a comprehensive review of advanced control methods specifically designed for floating offshore wind turbines (FOWTs) above the rated wind speed. Focusing on primary control objectives, including power regulation at rated values, platform pitch mitigation, and structural load reduction, this paper begins by outlining the requirements and challenges inherent in FOWT control systems. It delves into the fundamental aspects of the FOWT system control framework, thereby highlighting challenges, control objectives, and conventional methods derived from bottom-fixed wind turbines. Our review then categorizes advanced control methods above the rated wind speed into three distinct approaches: model-based control, data-driven model-based control, and data-driven model-free control. Each approach is examined in terms of its specific strengths and weaknesses in practical application. The insights provided in this review contribute to a deeper understanding of the dynamic landscape of control strategies for FOWTs, thus offering guidance for researchers and practitioners in the field.
The present study sheds new light on advanced control methods of photovoltaic (PV) emulators using finite set model predictive control (FS-MPC). In the first part of the study, a predictive PV emulator (P-PVE) based o...
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The present study sheds new light on advanced control methods of photovoltaic (PV) emulators using finite set model predictive control (FS-MPC). In the first part of the study, a predictive PV emulator (P-PVE) based on a Buck converter is proposed and tested under hard climatic conditions and load variations. The high performance of the P-PVE in terms of dynamic response, reference tracking, accuracy, simplicity, and efficiency is confirmed experimentally when compared with those of the commonly used one based PI controller. The second part of the study proposes an efficient cascaded predictive control (CPC) method applied on two topologies of PV systems, namely the stand-alone system and the grid-connected system. In each topology, the P-PVE is cascaded to a maximum power point tracking Boost converter in order to track efficiently the maximum power point. In addition to the high performance offered by the FS-MPC, the proposed control strategy allows to control all cascaded converters at the same time in one stage instead of controlling them separately, thus providing more flexibility and simple controllability. Extensive experimental results are done confirming the correctness and the effectiveness of the proposed CPC under hard climatic conditions, even in the presence of distorted grid voltage.
The application of advanced control methods to large-scale systems in variable industrial environment requires modeling and identification platform capable of keeping global model with description of its uncertainties...
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ISBN:
(纸本)9781424453634
The application of advanced control methods to large-scale systems in variable industrial environment requires modeling and identification platform capable of keeping global model with description of its uncertainties, building global model from sub-systems, retrieval of sub-models with mutual consistency, model actualization from new sub-models or new data, etc. This article treats the problem of assembling global model for large-scale system from interconnected and possibly overlapping sub-models, i.e. there can be duplicity in the models. The quality of sub-models can also be different and is taken into account. The article presents two new results: merging of multiple models for the same system by using equivalent data and consistent combination of arbitrary connected models with parametric uncertainty into single model by using statistics of random vectors convolution.
This paper provides a comprehensive review of the existing research on the Dual Active Bridge (DAB) DC-DC converter, focusing on modeling methods, modulation strategies, optimization algorithms, and controlmethods. A...
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This paper provides a comprehensive review of the existing research on the Dual Active Bridge (DAB) DC-DC converter, focusing on modeling methods, modulation strategies, optimization algorithms, and controlmethods. A comparative analysis of selected methods along with guidelines to assist engineers and researchers in their study of DAB is also presented. Firstly, a comprehensive review of modulation strategies for DAB is provided, ranging from classical phase-shift modulation to the popular asymmetric duty modulation. The intrinsic relationships among different modulation methods are summarized, and a comparison is made based on the difficulty of control and DAB operating characteristics. Secondly, the various modeling methods for DAB are described, including reduced-order modeling, generalized state-space averaging modeling, and discrete-time modeling methods. A comparison is made based on the suitability for different application scenarios, providing recommendations for the adoption of different modeling methods. Furthermore, a survey of optimization algorithms for modulation methods is presented, including classical algorithms, swarm intelligence optimization, and reinforcement learning algorithms. A number of criteria are proposed for different algorithms, and an analysis of the unresolved challenges and future prospects is provided. Finally, the advanced control methods for DAB are summarized based on control effectiveness and applicability. The article concludes with a summary and an outlook on future research directions is also provided.
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