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The problem of plasma vertical stabilization based on the model predictive control has been considered. It is shown that MPC algorithms are superior compared to the LQR-optimal controller, because they allow taking constraints into account and provide high-performance control. It is also shown that in the case of the traditional MPC-scheme it is possible to reduce.
Model Predictive Control is an important technique used in the process control industries. It has developed considerably in the last few years, because it is the most general way of posing the process control problem in the time domain. The Model Predictive Control formulation integrates optimal control, stochastic control, control of processes with dead time, multivariable control and future references. The finite control horizon makes it possible to handle constraints and non linear processes in general which are frequently found in industry. Focusing on implementation issues for Model Predictive Controllers in industry, it fills the gap between the empirical way practitioners use control algorithms and the sometimes abstractly formulated techniques developed by researchers. The text is firmly based on material from lectures given to senior undergraduate and graduate students and articles written by the authors.
This book provides a thorough understanding of the basic principles, synthesis, analysis, and control of virtual inertia systems. It uses the latest technical tools to mitigate power system stability and control problems under the presence of high distributed generators (DGs) and renewable energy sources (RESs) penetration. This book uses a simple virtual inertia control structure based on the frequency response model, complemented with various control methods and algorithms to achieve an adaptive virtual inertia control respect to the frequency stability and control issues. The chapters capture the important aspects in virtual inertia synthesis and control with the objective of solving the stability and control problems regarding the changes of system inertia caused by the integration of DGs/RESs. Different topics on the synthesis and application of virtual inertia are thoroughly covered with the description and analysis of numerous conventional and modern control methods for enhancing the full spectrum of power system stability and control. Filled with illustrative examples, this book gives the necessary fundamentals and insight into practical aspects. This book stimulates further research and offers practical solutions to real-world power system stability and control problems with respect to the system inertia variation triggered by the integration of RESs/DGs. It will be of use to engineers, academic researchers, and university students interested in power systems dynamics, analysis, stability and control.
This thesis is devoted to developing a robust Model Predictive Control (MPC) strategy based on Gaussian Processes (GP), especially for Drinking Water Networks (DWN). Nowadays there are many different MPC strategies developed for DWN, such as certain-equivalent MPC (CEMPC) and chance-constrained MPC (CC-MPC). The general control objectives for DWN are economic by managing the water supply to minimise water production and transport costs, all the tanks running in safe ways with their limitations and reducing the undesired abrupt changes by minimising their slew rate and obtaining smooth signals. For the deterministic system model, the control objectives are elementary fulfilled. But the main challenge for DWN is to propagate and incorporate exogenous and endogenous uncertainties to MPC closed loop over the prediction horizon. Considering the control-oriented model of the DWN, the MPC controller design is hereby divided into two parts: system disturbances forecasting and the robust MPC controller design. Case studies based on Barcelona DWN have been executed to verify the performance of proposed methodologies. The first part of this thesis leads to forecast system disturbances by using GP. In a DWN system, system disturbances come mainly water demands associated to consumer sectors. Hence, it is necessary to model each water demand and forecast the water demand in a short term that covers the MPC prediction horizon. GP regression is regarded as one of state-of-the-art regression methods able to select model parameters by using Bayesian Inference theory with a collection of past data. Besides, it is believed that the GP regression method has a difficult for the multiple-step ahead forecasting. Hence, the Double-seasonal Holt-winters method is used for forecasting the expected disturbances while the stochastic disturbances are forecasted by using GP. Finally, the desired forecasting results are a set of Gaussian distributions over the MPC prediction horizon. The second part of this thesis is to incorporate the forecasting results from GP within MPC closed loop. This MPC strategy based on GP is named GP-MPC. Using the given system model, the deterministic state evolutions can be obtained while the uncertainty of state propagation over a given prediction horizon can be also achieved though the linear approximation of GP. Therefore, the worst-case state evolutions over the MPC prediction horizon can also be determined in the MPC cost function and constraints. The desired performance of applying GP-MPC in the closed-loop system is that the system has more safety than the CE-MPC and meanwhile it probably brings more expenses. Comparisons of GP-MPC and previous developed approaches are carried out by a case study of the three-tank system inspired in the Barcelona DWN. A set of key performance indicators are defined to compare performances of different MPC strategies. Finally, through the simulation results, the GP-MPC has the similar performance as the CC-MPC, both of which have much more expenses than the CE-MPC. As a result of considering the uncertainties inside the system, more expenses is necessary to maintain the safety of the whole system. Hence, the GP-MPC is more advanced. Moreover, the proposed GP-MPC is required to be tested with the whole DWN and using the real data from a DWN system. So the future works of this thesis have been outlined.
The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies ..., new challenges. Much of this development work resides in industrial reports, feasibility study papers and the reports of advanced collaborative projects. The series offers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for wider and rapid dissemination. The water and wastewater industry has undergone many changes in recent years. Of particular importance has been a renewed emphasis on improving resource management with tighter regulatory controls setting new targets on pricing, industry efficiency and loss reduction for both water and wastewater with more stringent environmental discharge conditions for wastewater. Meantime, the demand for water and wastewater services grows as the population increases and wishes for improved living conditions involving, among other items, domestic appliances that use water. Consequently, the installed infrastructure of the industry has to be continuously upgraded and extended, and employed more effectively to accommodate the new demands, both in throughput and in meeting the new regulatory conditions. Investment in fixed infrastructure is capital-intensive and slow to come on-stream. One outcome of these changes and demands is that the industry is examining the potential benefits of, and in many cases using, more advanced control systems.