A neurodynamic optimization approach to constrained pseudoconvex optimization.

Guo, Zhishan.Thesis (M.Phil.)--Chinese University of Hong Kong, 2011.Includes bibliographical references (p. 71-82).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement i --- p.iiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Constrained Pseudoconvex Optimization --- p.1Chapter 1.2 --- Recurrent Neural Networks --- p.4Chapter 1.3 --- Thesis Organization --- p.7Chapter 2 --- Literature Review --- p.8Chapter 2.1 --- Pseudo convex Optimization --- p.8Chapter 2.2 --- Recurrent Neural Networks --- p.10Chapter 3 --- Model Description and Convergence Analysis --- p.17Chapter 3.1 --- Model Descriptions --- p.18Chapter 3.2 --- Global Convergence --- p.20Chapter 4 --- Numerical Examples --- p.27Chapter 4.1 --- Gaussian Optimization --- p.28Chapter 4.2 --- Quadratic Fractional Programming --- p.36Chapter 4.3 --- Nonlinear Convex Programming --- p.39Chapter 5 --- Real-time Data Reconciliation --- p.42Chapter 5.1 --- Introduction --- p.42Chapter 5.2 --- Theoretical Analysis and Performance Measurement --- p.44Chapter 5.3 --- Examples --- p.45Chapter 6 --- Real-time Portfolio Optimization --- p.53Chapter 6.1 --- Introduction --- p.53Chapter 6.2 --- Model Description --- p.54Chapter 6.3 --- Theoretical Analysis --- p.56Chapter 6.4 --- Illustrative Examples --- p.58Chapter 7 --- Conclusions and Future Works --- p.67Chapter 7.1 --- Concluding Remarks --- p.67Chapter 7.2 --- Future Works --- p.68Chapter A --- Publication List --- p.69Bibliography --- p.7

Fixed-Time Gradient Flows for Solving Constrained Optimization: A Unified Approach

The accelerated method in solving optimization problems has always been an absorbing topic. Based on the fixed-time (FxT) stability of nonlinear dynamical systems, we provide a unified approach for designing FxT gradient flows (FxTGFs). First, a general class of nonlinear functions in designing FxTGFs is provided. A unified method for designing first-order FxTGFs is shown under PolyakL jasiewicz inequality assumption, a weaker condition than strong convexity. When there exist both bounded and vanishing disturbances in the gradient flow, a specific class of nonsmooth robust FxTGFs with disturbance rejection is presented. Under the strict convexity assumption, Newton-based FxTGFs is given and further extended to solve time-varying optimization. Besides, the proposed FxTGFs are further used for solving equation-constrained optimization. Moreover, an FxT proximal gradient flow with a wide range of parameters is provided for solving nonsmooth composite optimization. To show the effectiveness of various FxTGFs, the static regret analysis for several typical FxTGFs are also provided in detail. Finally, the proposed FxTGFs are applied to solve two network problems, i.e., the network consensus problem and solving a system linear equations, respectively, from the respective of optimization. Particularly, by choosing component-wisely sign-preserving functions, these problems can be solved in a distributed way, which extends the existing results. The accelerated convergence and robustness of the proposed FxTGFs are validated in several numerical examples stemming from practical applications

Review of distributed control and optimization in energy internet: From traditional methods to artificial intelligence-based methods

Abstract: Energy internet (EI) can alleviate the arduous challenges brought about by the energy crisis and global warming and has aroused the concern of many scholars. In the research of EI control systems, the access of distributed energy causes the power system to exhibit complex nonlinearity, high uncertainty and strong coupling. Traditional control and optimization methods often have limited effectiveness in solving these problems. With the widespread application of distributed control technology and the maturity of artificial intelligence (AI) technology, the combination of distributed control and AI has become an effective method to break through current research bottlenecks. This study reviews the research progress of EI distributed control technologies based on AI in recent years. It can be found that AI‐based distributed control methods have many advantages in maintaining EI stability and achieving optimal energy management. This combination of AI and distributed control makes EI control systems more intelligent, safe and efficient, which will be an important direction for future research. The purpose of this study is to provide a reference as well as useful research ideas for the study of EI control systems

Neurodynamic approaches to model predictive control.

Pan, Yunpeng.Thesis (M.Phil.)--Chinese University of Hong Kong, 2009.Includes bibliographical references (p. 98-107).Abstract also in Chinese.Abstract --- p.ip.iiiAcknowledgement --- p.ivChapter 1 --- Introduction --- p.2Chapter 1.1 --- Model Predictive Control --- p.2Chapter 1.2 --- Neural Networks --- p.3Chapter 1.3 --- Existing studies --- p.6Chapter 1.4 --- Thesis structure --- p.7Chapter 2 --- Two Recurrent Neural Networks Approaches to Linear Model Predictive Control --- p.9Chapter 2.1 --- Problem Formulation --- p.9Chapter 2.1.1 --- Quadratic Programming Formulation --- p.10Chapter 2.1.2 --- Linear Programming Formulation --- p.13Chapter 2.2 --- Neural Network Approaches --- p.15Chapter 2.2.1 --- Neural Network Model 1 --- p.15Chapter 2.2.2 --- Neural Network Model 2 --- p.16Chapter 2.2.3 --- Control Scheme --- p.17Chapter 2.3 --- Simulation Results --- p.18Chapter 3 --- Model Predictive Control for Nonlinear Affine Systems Based on the Simplified Dual Neural Network --- p.22Chapter 3.1 --- Problem Formulation --- p.22Chapter 3.2 --- A Neural Network Approach --- p.25Chapter 3.2.1 --- The Simplified Dual Network --- p.26Chapter 3.2.2 --- RNN-based MPC Scheme --- p.28Chapter 3.3 --- Simulation Results --- p.28Chapter 3.3.1 --- Example 1 --- p.28Chapter 3.3.2 --- Example 2 --- p.29Chapter 3.3.3 --- Example 3 --- p.33Chapter 4 --- Nonlinear Model Predictive Control Using a Recurrent Neural Network --- p.36Chapter 4.1 --- Problem Formulation --- p.36Chapter 4.2 --- A Recurrent Neural Network Approach --- p.40Chapter 4.2.1 --- Neural Network Model --- p.40Chapter 4.2.2 --- Learning Algorithm --- p.41Chapter 4.2.3 --- Control Scheme --- p.41Chapter 4.3 --- Application to Mobile Robot Tracking --- p.42Chapter 4.3.1 --- Example 1 --- p.44Chapter 4.3/2 --- Example 2 --- p.44Chapter 4.3.3 --- Example 3 --- p.46Chapter 4.3.4 --- Example 4 --- p.48Chapter 5 --- Model Predictive Control of Unknown Nonlinear Dynamic Sys- tems Based on Recurrent Neural Networks --- p.50Chapter 5.1 --- MPC System Description --- p.51Chapter 5.1.1 --- Model Predictive Control --- p.51Chapter 5.1.2 --- Dynamical System Identification --- p.52Chapter 5.2 --- Problem Formulation --- p.54Chapter 5.3 --- Dynamic Optimization --- p.58Chapter 5.3.1 --- The Simplified Dual Neural Network --- p.59Chapter 5.3.2 --- A Recursive Learning Algorithm --- p.60Chapter 5.3.3 --- Convergence Analysis --- p.61Chapter 5.4 --- RNN-based MPC Scheme --- p.65Chapter 5.5 --- Simulation Results --- p.67Chapter 5.5.1 --- Example 1 --- p.67Chapter 5.5.2 --- Example 2 --- p.68Chapter 5.5.3 --- Example 3 --- p.76Chapter 6 --- Model Predictive Control for Systems With Bounded Uncertainties Using a Discrete-Time Recurrent Neural Network --- p.81Chapter 6.1 --- Problem Formulation --- p.82Chapter 6.1.1 --- Process Model --- p.82Chapter 6.1.2 --- Robust. MPC Design --- p.82Chapter 6.2 --- Recurrent Neural Network Approach --- p.86Chapter 6.2.1 --- Neural Network Model --- p.86Chapter 6.2.2 --- Convergence Analysis --- p.88Chapter 6.2.3 --- Control Scheme --- p.90Chapter 6.3 --- Simulation Results --- p.91Chapter 7 --- Summary and future works --- p.95Chapter 7.1 --- Summary --- p.95Chapter 7.2 --- Future works --- p.96Bibliography --- p.9

Model Predictive Control Based on Deep Learning for Solar Parabolic-Trough Plants

En la actualidad, cada vez es mayor el interés por utilizar energías renovables, entre las que se encuentra la energía solar. Las plantas de colectores cilindro-parabólicos son un tipo de planta termosolar donde se hace incidir la radiación del Sol en unos tubos mediante el uso de unos espejos con forma de parábola. En el interior de estos tubos circula un fluido, generalmente aceite o agua, que se calienta para generar vapor y hacer girar una turbina, produciendo energía eléctrica. Uno de los métodos más utilizados para manejar estas plantas es el control predictivo basado en modelo (model predictive control, MPC), cuyo funcionamiento consiste en obtener las señales de control óptimas que se enviarán a la planta basándose en el uso de un modelo de la misma. Este método permite predecir el estado que adoptará el sistema según la estrategia de control escogida a lo largo de un horizonte de tiempo. El MPC tiene como desventaja un gran coste computacional asociado a la resolución de un problema de optimización en cada instante de muestreo. Esto dificulta su implementación en plantas comerciales y de gran tamaño, por lo que, actualmente, uno de los principales retos es la disminución de estos tiempos de cálculo, ya sea tecnológicamente o mediante el uso de técnicas subóptimas que simplifiquen el problema. En este proyecto, se propone el uso de redes neuronales que aprendan offline de la salida proporcionada por un controlador predictivo para luego poder aproximarla. Se han entrenado diferentes redes neuronales utilizando un conjunto de datos de 30 días de simulación y modificando el número de entradas. Los resultados muestran que las redes neuronales son capaces de proporcionar prácticamente la misma potencia que el MPC con variaciones más suaves de la salida y muy bajas violaciones de las restricciones, incluso disminuyendo el número de entradas. El trabajo desarrollado se ha publicado en Renewable Energy, una revista del primer cuartil en Green & sustainable science & technology y Energy and fuels.Nowadays, there is an increasing interest in using renewable energy sources, including solar energy. Parabolic trough plants are a type of solar thermal power plant in which solar radiation is reflected onto tubes with parabolic mirrors. Inside these tubes circulates a fluid, usually oil or water, which is heated to generate steam and turn a turbine to produce electricity. One of the most widely used methods to control these plants is model predictive control (MPC), which obtains the optimal control signals to send to the plant based on the use of a model. This method makes it possible to predict its future state according to the chosen control strategy over a time horizon. The MPC has the disadvantage of a significant computational cost associated with resolving an optimization problem at each sampling time. This makes it challenging to implement in commercial and large plants, so currently, one of the main challenges is to reduce these computational times, either technologically or by using suboptimal techniques that simplify the problem. This project proposes the use of neural networks that learn offline from the output provided by a predictive controller to then approximate it. Different neural networks have been trained using a 30-day simulation dataset and modifying the number of irradiance and temperature inputs. The results show that the neural networks can provide practically the same power as the MPC with smoother variations of the output and very low violations of the constraints, even when decreasing the number of inputs. The work has been published in Renewable Energy, a first quartile journal in Green & sustainable science & technology and Energy and fuels.Universidad de Sevilla. Máster en Ingeniería Industria