Multi-Path Alpha-Fair Resource Allocation at Scale in Distributed Software Defined Networks

The performance of computer networks relies on how bandwidth is shared among different flows. Fair resource allocation is a challenging problem particularly when the flows evolve over time. To address this issue, bandwidth sharing techniques that quickly react to the traffic fluctuations are of interest, especially in large scale settings with hundreds of nodes and thousands of flows. In this context, we propose a distributed algorithm based on the Alternating Direction Method of Multipliers (ADMM) that tackles the multi-path fair resource allocation problem in a distributed SDN control architecture. Our ADMM-based algorithm continuously generates a sequence of resource allocation solutions converging to the fair allocation while always remaining feasible, a property that standard primal-dual decomposition methods often lack. Thanks to the distribution of all computer intensive operations, we demonstrate that we can handle large instances at scale

HVAC control using support vector regression models

Master'sMASTER OF ENGINEERIN

Development and applications of novel optimal control algorithms

The main concern of this thesis is to advance and improve the existing knowledge of a dynamic optimal control technique known as DISOPE. so as to make it attractive on one hand for its implementation in the process industry and, on the other hand, as a novel nonlinear optimal control algorithm. The main feature of the technique is that it has been designed so as to achieve the correct optimum of the process in spite of inaccuracies in the mathematical model employed in the computations. Several extensions of the basic continuous time DIS OPE technique are proposed in this work. For the development of the algorithms, emphasis is placed on making the techniques implementable in digital computer based industrial process control problems. These extensions include discrete-time. and set-point tracking versions, extensions for handling control and state dependent inequality constraints. and a hierarchical version. Applications of DISOPE are proposed in the following areas: nonlinear predictive control, predictive optimising control based on adaptive state-space linear dynamic models, and batch process optimisation. All the algorithms and techniques proposed in this thesis have been implemented in software and tested with relevant simulations. These studies include dynamic simulations of low order chemical reaction systems and studies on the dynamic optimisation of an industrial-scale multicomponent distillation column using a rigorous process simulator. Comparisons with existing techniques are provided and suggestions are made for future research in the area treated in this thesis

Design of of model-based controllers via parametric programming

Imperial Users onl

A Model-Based Holistic Power Management Framework: A Study on Shipboard Power Systems for Navy Applications

The recent development of Integrated Power Systems (IPS) for shipboard application has opened the horizon to introduce new technologies that address the increasing power demand along with the associated performance specifications. Similarly, the Shipboard Power System (SPS) features system components with multiple dynamic characteristics and require stringent regulations, leveraging a challenge for an efficient system level management. The shipboard power management needs to support the survivability, reliability, autonomy, and economy as the key features for design consideration. To address these multiple issues for an increasing system load and to embrace future technologies, an autonomic power management framework is required to maintain the system level objectives. To address the lack of the efficient management scheme, a generic model-based holistic power management framework is developed for naval SPS applications. The relationship between the system parameters are introduced in the form of models to be used by the model-based predictive controller for achieving the various power management goals. An intelligent diagnostic support system is developed to support the decision making capabilities of the main framework. Naïve Bayes’ theorem is used to classify the status of SPS to help dispatch the appropriate controls. A voltage control module is developed and implemented on a real-time test bed to verify the computation time. Variants of the limited look-ahead controls (LLC) are used throughout the dissertation to support the management framework design. Additionally, the ARIMA prediction is embedded in the approach to forecast the environmental variables in the system design. The developed generic framework binds the multiple functionalities in the form of overall system modules. Finally, the dissertation develops the distributed controller using the Interaction Balance Principle to solve the interconnected subsystem optimization problem. The LLC approach is used at the local level, and the conjugate gradient method coordinates all the lower level controllers to achieve the overall optimal solution. This novel approach provides better computing performance, more flexibility in design, and improved fault handling. The case-study demonstrates the applicability of the method and compares with the centralized approach. In addition, several measures to characterize the performance of the distributed controls approach are studied

Contributions to distributed MPC: coalitional and learning approaches

A growing number of works and applications are consolidating the research area of distributed control with partial and varying communication topologies. In this context, many of the works included in this thesis focus on the so-called coalitional MPC. This approach is characterized by the dynamic formation of groups of cooperative MPC agents (referred to as coalitions) and seeks to provide a performance close to the centralized one with lighter computations and communication demands. The thesis includes a literature review of existing distributed control methods that boost scalability and flexibility by exploiting the degree of interaction between local controllers. Likewise, we present a hierarchical coalitional MPC for traffic freeways and new methods to address the agents' clustering problem, which, given its combinatoria! nature, becomes a key issue for the real-time implementation of this type of controller. Additionally, new theoretical results to provide this clustering strategy with robust and stability guarantees to track changing targets are included. Further works of this thesis focus on the application of learning techniques in distributed and decentralized MPC schemes, thus paving the way for a future extension to the coalitional framework. In this regard, we have focused on the use of neural networks to aid distributed negotiations, and on the development of a multi­ agent learning MPC based on a collaborative data collection

Centralized and non-centralized model predictive control of a multizone building

Nowadays, reduction in energy use has become an important issue by the vast majority of institutions in all the world. Although the energy efficiency of systems and components for heating, ventilating, and air conditioning (HVAC) has improved considerably over recent years, there is still potential for substantial improvements. Consequently, this project deals with an advanced control technique, model predictive control, that can provide significant energy savings in comparison with conventional, non-predictive techniques. Nevertheless, the main goal is to try a comparison between two possible approaches to obtain such building energy control problem: (1) Centralized control, and (2) Distributed control. To accomplish this, mathematical software MATLAB has been used. Also YALMIP, which is a free MATLAB toolbox for rapid prototyping of optimization problem, has been used

Centralized and non-centralized model predictive control of a multizone building

Nowadays, reduction in energy use has become an important issue by the vast majority of institutions in all the world. Although the energy efficiency of systems and components for heating, ventilating, and air conditioning (HVAC) has improved considerably over recent years, there is still potential for substantial improvements. Consequently, this project deals with an advanced control technique, model predictive control, that can provide significant energy savings in comparison with conventional, non-predictive techniques. Nevertheless, the main goal is to try a comparison between two possible approaches to obtain such building energy control problem: (1) Centralized control, and (2) Distributed control. To accomplish this, mathematical software MATLAB has been used. Also YALMIP, which is a free MATLAB toolbox for rapid prototyping of optimization problem, has been used