Gotowa bibliografia na temat „Optimized Control”

Transportation underlines the vehicle industry's critical role in a country's economic future.The amount of goods moved, specically by trucks, is only expected to increase inthe near future. This work attempts to tackle the problem of optimizing fuel consumptionin Volvo trucks, when there are hard constraints on the delivery time and speed limits.Knowledge of the truck such as position, state, conguration etc., along with the completeroute information of the transport mission is used for fuel optimization.Advancements in computation, storage, and communication on cloud based systems, hasmade it possible to easily incorporate such systems in assisting modern eet. In this work,an algorithm is developed in a cloud based system to compute a speed plan for the completemission for achieving fuel minimization. This computation is decoupled from thelocal control operations on the truck such as prediction control, safety, cruise control, etc.;and serves as a guide to the truck driver to reach the destination on time by consumingminimum fuel.To achieve fuel minimization under hard constraints on delivery (or arrival) time andspeed limits, a non-linear optimization problem is formulated for the high delity modelestimated from real-time drive cycles. This optimization problem is solved using a Nonlinearprogramming solver in Matlab.The optimal policy was tested on two drive cycles provided by Volvo. The policy wascompared with two dierent scenarios, where the mission demands hard constraints ontravel time and the speed limits in addition to no trac uncertainties (deterministic). with a cruise controller running at a constant set speed throughout the mission. Itis observed that there is no signicant fuel savings. with maximum possible fuel consumption; achieved without the help of optimalspeed plan (worst case). It is seen that there is a notable improvement in fuelsaving.In a real world scenario, a transport mission is interrupted by uncertainties such as trac ow, road blocks, re-routing, etc. To this end, a stochastic optimization algorithm is proposedto deal with the uncertainties modeled using historical trac ow data. Possiblesolution methodologies are suggested to tackle this stochastic optimization problem.

Tracking control algorithms often target the convergence of a tracking error. However, this can be at the expense of other important system characteristics, such as the control effort used to annihilate the tracking error, transient response, or steady-state characteristics, for example. Furthermore, most tracking control methods assume prior knowledge of the system dynamics, which is not always a realistic assumption, especially in the case of highly complex systems. In this thesis, a model-free optimized tracking control architectural heuristic is proposed. The suggested feedback system is composed of two control loops. The first is the tracking loop. It focuses on the convergence of the tracking error. It is implemented using two different model-free control algorithms for comparison purpose: Reinforcement Learning (RL) and the Nonlinear Threshold Accepting (NLTA) technique. The RL scheme reformulates the tracking error combinations into a form of Markov-Decision-Process (MDP) and applies Q-Learning to build the best tracking control policy for the dynamic system under consideration. On the other hand, the NLTA algorithm is applied to tune the gains of a PID controller. The second control loop is in the form of a nonlinear state feedback loop. It is implemented using a feedforward artificial neural network (ANN) to optimize a system-wide cost function which can be flexible enough to encompass a set of desired design requirements pertaining to the targeted system behavior. This may include, for instance, the target overshoot, settling time, rise time, etc. The proposed architectural heuristic provides a model-free framework to tackle such control problems, in the sense that the plant's dynamic model is not required to be known in advance. Yet, at least a subset of the stability region of the optimized gains has to be known in advance so that it can provide a search space for the optimization algorithms. Simulation results on two dynamic systems demonstrate the superiority of the proposed control scheme.

<p>In an optimal power control scheme for a Code Division Multiple Access (CDMA) system all mobile stations signals should arrive to the base station at equal power. If not, stronger singals may cause too much interference and block out weaker ones. Commonly used power control schemes utilizes the Signal to Interference Ratio (SIR) to design a Power Control Command (PCC) to adjust the transmit power of the mobile station. A significant problem within the conventional methods is the slow SIR recovery due to deep channel fades. Conventional methods base the PCC on the previous channel state when in fact, the channel state may have significantly changed when transmission occurs. These channel changes may cause the SIR to drop or rise drastically and lead to uncontrollable Multi Access Interference (MAI) resulting in power escalation and making the system unstable. In order to overcome power escalation and improve the recovery from deep fades a novel power control method has been developed. Based on Linear Quadratic Control and Kalman filtering for channel prediction this method designs the PCC based on the coming channel state instead of the current. This optimizes the PCC for the channel state where transmission occurs. Simulations show that this control scheme outperforms previous methods by making the impacts of the deep fades less severe on the SIR and also improves the overall SIR behaviour.</p>

The Technology today is changing at a fast pace. The growth of computers and telecommunications over the past three decades has been extraordinary. We today are at the point where all technologies related to communication and data transfer are submerging to a common platform. A number of different methods are available for data communication or data transfer. The important factor in all communication setups is to satisfy user demands with low cost and reliability. The area of interest for this thesis is future energy substations and wind mills. In order to make things more straight forward and see its different options and capabilities the focus is on designing and implementing a new energy protocol called Energy Real Time Protocol (eRTP) based on Iyad Real Time Protocol (iRTP) [2]. The protocol is designed to meet the requirements of power and energy networks in terms of sending the energy parameters with VoIP data (optional) among power stations at different locations. Keeping in mind the importance transferring energy parameters in real-time, the presented protocol has built upon small individual algorithms/modules designed for multi-core architecture. Each module is supposed to be processed by an individual core/processor in parallel.

Thesis (Ph. D.)--Aerospace Engineering, Georgia Institute of Technology, 2008.<br>Committee Chair: Johnson, Eric; Committee Co-Chair: Calise, Anthony; Committee Member: Prasad, J.V.R.; Committee Member: Tannenbaum, Allen; Committee Member: Tsiotras, Panagiotis.

Visual effects for film and animation often require simulated granular materials, such as sand, wheat, or dirt, to meet a director's needs. Simulating granular materials can be time consuming, in both computation and labor, as these particulate materials have complex behavior and an enormous amount of small-scale detail. Furthermore, a single cubic meter of granular material, where each grain is a cubic millimeter, would contain a billion granules, and simulating all such interacting granules would take an impractical amount of time for productions. This calls for a simplified model for granular materials that retains high surface detail and granular behavior yet requires significantly less computational time. Our proposed method simulates a minimal number of individual granules while retaining particulate detail on the surface by supporting surface particles with simplified interior granular models. We introduce a multi-state model where, depending on the material state of the interior granules, we replace interior granules with a simplified simulation model for the state they are in and automate the transitions between those states. The majority of simulation time can thus be focused on visible portions of the material, reducing the time spent on non-visible portions, while maintaining the appearance and behavior of the mass as a whole.

This research argues that a diesel-based isolated electrical system can be optimized byintegrating a high share of solar photovoltaic (PV) generation and that the frequencystability of such system can be improved by including the PV participation in frequencyregulation. A case study is developed in order to explore an island’s expansion of theinstalled generating capacity and its optimization. This study uses the tool HOMER tosolve the optimization problem and PowerFactory to verify the frequency stability of theproposed system. The PV integration allows for a reduction of diesel fuel consumption,emissions and generation costs. Additionally, in high PV penetration scenarios, the reducedinertia in such systems can lead to high frequency deviations that may trip the systemprotection. The study demonstrates that the instantaneous frequency deviation after a loadand generation imbalance can be reduced by designing the PVs to operate with an allocatedreserve and a decentralized time-based secondary frequency control. The frequency stabilitywas achieved after different disturbance scenarios under high PV penetration and reducedavailable inertia, indicating that high PV integration is economically and technically feasiblein small island grids.<br>I detta examensarbete studeras hur ett dieselbaserat och isolerat elsystem kan optimeras genom att integrera en hög andel solceller (PV) i elproduktionen och att frekvensstabilitet kan förbättras när PV användas i regleringen. En fallstudie har utvecklats under denna forskning för att analysera en ökning av den installerade generationskapacitet vid en ö samt hur detta kan optimeras. I denna studie användas verktyget HOMER för modeloptimering och PowerFactory för att testa den optimerade systemfrekvens stabilitet. Med PV generation kan diesel konsumption, utsläpp och kostnader minskas för hela systemet. En hög andel PV i generationen reducerar elsystemet totala svängmassa vilket kan ledda till avvikelser i systemfrekvensen som kan ursaka att skyddsystem aktiveras. Studien demonstrerar att den momentana systemavvikelsen efter en obalans kan reduceras genom att designa PV i systemet med en allokerad reserv och en decentraliserad och tidsbaserad sekundär frekvensreglering. Frekvensstabiliteten nåddes i olika obalans scenarier med hög andel solcellgeneration och misnkat svängsmassa. Detta tyder på att en hög andel PV integration är både ekonomisk- och tekniskt möjligt i mindre elsystem.