In industrial fuel gas preparation, there are several compositional properties that must be controlled within specified limits. This allows client plants to use the fuel gas mixture safely without having to adjust and control the composition themselves. The variables to be controlled are the Higher Heating Value (HHV), Wobbe Index (WI), Flame Speed Index (FSI), and Pressure (P). These variables are controlled by adjusting the volumetric flow rates of several inlet gas streams of which some are makeup streams (always available) and some are wild streams that vary in composition and availability (by-products of plants). The inlet streams need to be adjusted in the correct ratios to keep all the controlled variables (CVs) within limits while minimising the cost of the gas blend. Furthermore, the controller needs to compensate for fluctuations in inlet stream compositions and total fuel gas demand (the total discharge from the header). This dissertation describes the modelling and model validation of an industrial fuel gas header as well as a simulation study of three different Model Predictive Control (MPC) strategies for controlling the system while minimising the overall operating cost. / Dissertation (MEng)--University of Pretoria, 2011. / Electrical, Electronic and Computer Engineering / unrestricted

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第22808号 / 情博第738号 / 新制||情||126(附属図書館) / 京都大学大学院情報学研究科システム科学専攻 / (主査)教授 大塚 敏之, 教授 加納 学, 教授 太田 快人 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

Tripod, an integrated bi-level transportation management system, is a smartphone application from the potential user’s point of view which would be instantly accessed prior to performing the trip. Since there are constantly several alternatives for any trip, Tripod considers a series and combination of various parameters, including departure time, mode and route, and rewards for each alternative with a number of redeemable points for goods and services called “Tokens”. The framework responsible for computing the optimized number of tokens awarded to the set of available alternatives in order to minimize the system-wide energy consumption under a constrained Token budget, is the System Optimization (SO) implemented in Tripod. To do so, a higher number of tokens would be awarded to the alternatives, guaranteeing a larger energy saving, less energy consumption, alternatively. SO is multimodal whereby public transit, private car, carpooling, etc. are being considered as the potential travel modes. Furthermore, SO is dynamic, predictive and personalized: the same alternative is rewarded differently, depending on the current and predicted future condition of the network and on the individual’s profile. In order to solve this problem, a multimodal simulation-based optimization model will be elaborated.

Process economic improvement subject to safety, operational and environmental constraints is an ultimate goal of using on-line process optimization and control techniques. The dynamic nature of present-day market conditions motivates the consideration of process dynamics within the economic optimization calculation. Two key paradigms for implementing real-time dynamic economic optimization are a dynamic real-time optimization (DRTO) and regulatory MPC two-layer architecture, and a single-level economic model predictive control (EMPC) con figuration. In the two-layer architecture, the economically optimal set-point trajectories computed in an upper DRTO layer are provided to the MPC layer, while in the single-layer EMPC con figuration the economics are incorporated within the MPC objective function. There are limited studies on a systematic performance comparison between these two approaches. Furthermore, these studies do not simultaneously consider the economic, disturbance rejection and computational performance criteria. Thus, it may not be clear under what conditions one particular method is preferable over the other. These reasons motivate a more comprehensive comparison between the two paradigms, with both open and closed-loop predictions considered in the DRTO calculations. In order to conduct this comparison, we utilize two process case studies for the economic analysis and performance comparison of on-line optimization systems. The first case study is a process involving two stirred-tank reactors in-series with an intermediate mixing point, and the second case study is a linear multi-input single-output (MISO) system. These processes are represented using a fi rst principles model in the form of differential-algebraic equations (DAEs) system for the first case study and a simplified linear model of a polymerization reactor for the second case study problem. Both of the case study processes include constraints associated with input variables, safety considerations, and output quality. In these case study problems, the objective of optimal process operation is net profit improvement. The following performance evaluation criteria are considered in this study: (I) optimal value of the economic objective function, (II) average run time (ART) over a same operating time interval, (III) cumulative output constraint violation (COCV) for each constraint. The update time of the single-layer approach is selected to be equal to that of the control layer in the two-layer formulations, while the update time of the economic layer in the two-layer formulation is bigger than that of the single-layer approach. The nonlinear programing (NLP) problems which result in the single-layer and two-layer formulations and the quadratic programing problem which corresponds to the MPC formulation are solved using the fmincon and quadprog optimization solvers in MATLAB. Performance assessment of the single-layer and two-layer formulations is evaluated in the presence of a variety of unknown disturbance scenarios for the first case study problem. The effect of a dynamic transition in the product quality is considered in the performance comparison of the single-layer and two-layer methods in the second case-study problem. The first case study problem results show that for all unknown disturbance scenarios, the economic performance of the single-layer approach is slightly higher than that of the two layer formulations. However, the average computation times for the DRTO-MPC two-layer formulations are at least one order of magnitude lower than that of the EMPC formulation. Also, comparison results of the COCV for the EMPC formulation for different sizes of update time intervals could justify the necessity of the MPC control layer to reduce the COCV for the economic optimization problems with update times larger than that of the MPC control layer. A similar computational advantage of the OL- and CL-DRTO-MPC over the EMPC is observed for the second case study problem. In particular, it is shown that increasing the economic horizon length in the EMPC formulation to a sufficiently large value may result a higher economic improvement. However, the increase in economic optimization horizon would increase the resulting NLP problem size. The computational burden could limit the use of the EMPC formulation with larger economic optimization horizons in real-time applications. The ART of the dual-layer methods is at least two orders of magnitude lower than that of the EMPC methods with an appropriate horizon length. The CL-DRTO-MPC economic performance is slightly less than that of the EMPC formulation with the same economic optimization horizon. In conclusion, the performance comparison on the basis of multiple criteria in this study demonstrates that the economic performance criterion is not necessarily the only important metric, and the operational constraint limitations and the optimization problem solution time could have an important impact on the selection of the most suitable real-time optimization approach. / Thesis / Master of Applied Science (MASc)

Esta tese descreve o desenvolvimento e implementação de um otimizador em tempo real da operação do conversor da unidade de craqueamento catalítico em leito fluidizado (FCC) da refinaria Henrique Lage localizada em São José dos Campos. É desenvolvida uma estratégia de otimização integrada ao controlador preditivo cuja função objetivo e restrições incorporam componentes dinâmicos e estáticos. Os problemas de controle multivariável e otimização econômica são resolvidos simultaneamente num mesmo algoritmo. É utilizado um modelo rigoroso do processo para determinar as condições econômicas ótimas do estado estacionário do conversor. Por intermédio do modelo dinâmico, obtido através de testes em degrau na planta, é determinada a melhor trajetória para conduzir o processo para o seu ponto de maior lucratividade, sem violar as suas restrições operacionais durante as transições. As variáveis controladas e manipuladas possuem restrições tanto no transiente como no estado estacionário. O problema de otimização não linear resultante é resolvido através de um algoritmo SQP. A nova estratégia de otimização forneceu excelentes resultados nas simulações com as condições normais de operação e as perturbações típicas da unidade. Na comparação desta estratégia com a convencional, cujas funções de controle e otimização estão separadas, a mesma mostrou um melhor desempenho por manter o processo mais estável. Na fase de implementação do otimizador, o controlador preditivo multivariável existente no conversor foi adaptado para a nova estrutura de otimização através da inclusão das funções de otimização que envolvem o modelo rigoroso do estado estacionário do processo. O controlador/otimizador foi inserido no software da Petrobras denominado SICON, o qual já era utilizado como plataforma de sintonia e manutenção do controlador multivariável linear do conversor. Esse software tem facilidades para desenvolvimento e implementação de estratégias de controle avançado. Neste trabalho são apresentados os aspectos mais importantes da integração do otimizador na estrutura de automação da refinaria e o seu comissionamento. Estão também relacionadas as principais dificuldades encontradas na implementação do otimizador e a forma que as mesmas foram solucionadas. Os resultados obtidos na planta mostram um bom desempenho do novo algoritmo tanto em relação aos benefícios econômicos como na estabilização da unidade. / This thesis describes the development and implementation of a real time optimizer of the operation of a fluid catalytic cracking (FCC) converter in the Henrique Lage refinery located in São José dos Campos. It is developed an optimization strategy integrated into the predictive controller whose objective function and constraints incorporate dynamic and static components. The multivariable control and the economic optimization problems are solved simultaneously by the same algorithm. A rigorous process model is used to determine the converter steady-state optimum economic operating conditions. By utilizing a dynamic model, obtained through step tests in the plant, calculations are carried out in order to determine the best path to drive the process to its more profitable operating point, without violating operational constraints during the transient modes. All controlled and manipulated variables are constrained, both in the steady-state and in the dynamic states. The resulting optimization problem is nonlinear and is solved by an SQP algorithm. The new optimization strategy has provided excellent results in the simulations performed at normal operating conditions and typical disturbances. Comparing to the conventional strategy, whose control and optimization functions are separated, the new one has shown a better performance by maintaining a smoother operation of the process. In the optimizer implementation phase, the existent predictive linear controller was adapted to the new optimization structure by including the optimization function that involves the rigorous steady-state model of the process. The optimizing controller was inserted in Petrobras SICON software, which was already being used as a tuning and maintenance platform of the linear multivariable controller of the converter. This software has nice features for the development and implementation of advanced process control strategies. In this work it is presented the more important aspects of the optimizer integration into the refinery automation structure and its commissioning. The main difficulties of the optimizer implementation and the way how they were solved are also related. Plant results show a good performance of the new algorithm in terms of economic benefits and unit stabilization.

Esta tese descreve o desenvolvimento e implementação de um otimizador em tempo real da operação do conversor da unidade de craqueamento catalítico em leito fluidizado (FCC) da refinaria Henrique Lage localizada em São José dos Campos. É desenvolvida uma estratégia de otimização integrada ao controlador preditivo cuja função objetivo e restrições incorporam componentes dinâmicos e estáticos. Os problemas de controle multivariável e otimização econômica são resolvidos simultaneamente num mesmo algoritmo. É utilizado um modelo rigoroso do processo para determinar as condições econômicas ótimas do estado estacionário do conversor. Por intermédio do modelo dinâmico, obtido através de testes em degrau na planta, é determinada a melhor trajetória para conduzir o processo para o seu ponto de maior lucratividade, sem violar as suas restrições operacionais durante as transições. As variáveis controladas e manipuladas possuem restrições tanto no transiente como no estado estacionário. O problema de otimização não linear resultante é resolvido através de um algoritmo SQP. A nova estratégia de otimização forneceu excelentes resultados nas simulações com as condições normais de operação e as perturbações típicas da unidade. Na comparação desta estratégia com a convencional, cujas funções de controle e otimização estão separadas, a mesma mostrou um melhor desempenho por manter o processo mais estável. Na fase de implementação do otimizador, o controlador preditivo multivariável existente no conversor foi adaptado para a nova estrutura de otimização através da inclusão das funções de otimização que envolvem o modelo rigoroso do estado estacionário do processo. O controlador/otimizador foi inserido no software da Petrobras denominado SICON, o qual já era utilizado como plataforma de sintonia e manutenção do controlador multivariável linear do conversor. Esse software tem facilidades para desenvolvimento e implementação de estratégias de controle avançado. Neste trabalho são apresentados os aspectos mais importantes da integração do otimizador na estrutura de automação da refinaria e o seu comissionamento. Estão também relacionadas as principais dificuldades encontradas na implementação do otimizador e a forma que as mesmas foram solucionadas. Os resultados obtidos na planta mostram um bom desempenho do novo algoritmo tanto em relação aos benefícios econômicos como na estabilização da unidade. / This thesis describes the development and implementation of a real time optimizer of the operation of a fluid catalytic cracking (FCC) converter in the Henrique Lage refinery located in São José dos Campos. It is developed an optimization strategy integrated into the predictive controller whose objective function and constraints incorporate dynamic and static components. The multivariable control and the economic optimization problems are solved simultaneously by the same algorithm. A rigorous process model is used to determine the converter steady-state optimum economic operating conditions. By utilizing a dynamic model, obtained through step tests in the plant, calculations are carried out in order to determine the best path to drive the process to its more profitable operating point, without violating operational constraints during the transient modes. All controlled and manipulated variables are constrained, both in the steady-state and in the dynamic states. The resulting optimization problem is nonlinear and is solved by an SQP algorithm. The new optimization strategy has provided excellent results in the simulations performed at normal operating conditions and typical disturbances. Comparing to the conventional strategy, whose control and optimization functions are separated, the new one has shown a better performance by maintaining a smoother operation of the process. In the optimizer implementation phase, the existent predictive linear controller was adapted to the new optimization structure by including the optimization function that involves the rigorous steady-state model of the process. The optimizing controller was inserted in Petrobras SICON software, which was already being used as a tuning and maintenance platform of the linear multivariable controller of the converter. This software has nice features for the development and implementation of advanced process control strategies. In this work it is presented the more important aspects of the optimizer integration into the refinery automation structure and its commissioning. The main difficulties of the optimizer implementation and the way how they were solved are also related. Plant results show a good performance of the new algorithm in terms of economic benefits and unit stabilization.

Real-Time Optimization (RTO) is a family of techniques that pursue to improve the performance of chemical processes. As general scheme, the method reevaluates the process conditions in a frequent basis and tries to adjust some selected variables, taking into account the plant state, actual operational constraints and optimization objectives. Several RTO approaches have born from the academy research and industrial practices, at the same time that more applications have been implemented in real facilities. Between the main motivations to apply RTO are the dynamic of markets, the seek for quality in the process results and environmental sustainability. That is why the interest on deeply understand the phases and steps involved in an RTO application has increased in recent years. Nevertheless, the fact that most of the existing RTO systems have been developed by commercial organizations makes it difficult to meet that understanding. This work studies the nature of RTO systems from a software point of view. Software requirements for a generic system are identied. Based on that, a software architecture is proposed that could be adapted for specfic cases. Benefits of the designed architecture are listed. At the same time, the work proposes a new approach to implement that architecture as a Multi-Agent System (MAS). Two RTO system prototypes were developed then, one for a well-know academic case study and the other oriented to be used in a real unit. The benefits of the MAS approach and the architecture, for researching on the RTO field and implementation on real plants, are analyzed in the text. A sub-product of the development, a software framework covering main concepts from the RTO ontology, is proposed as well. As the framework was designed to be generic, it can be used in new applications development and extended to very specific scenarios. / Otimização em Tempo Real (OTR) é uma família de técnicas que buscam melhorar o desempenho dos processos químicos. Como esquema geral, o método reavalia frequentemente as condições do processo e tenta ajustar algumas variáveis selecionadas, levando em considera ção o estado da planta, restrições operacionais e os objetivos da otimização. Várias abordagens para OTR t^em surgido da pesquisa acadêmica e das práticas industriais, ao mesmo tempo em que mais aplicações têm sido implementadas em plantas reais. As principais motivações para aplicar OTR são: a dinâmica dos mercados, a busca de qualidade nos resultados dos processos e a sustentabilidade ambiental. É por isso que o interesse em entender as fases e etapas envolvidas em uma aplicação OTR cresceu nos últimos anos. No entanto, o fato de que a maioria dos sistemas OTR em operação foram desenvolvidos por organizações comerciais dificulta o caminho para chegar nesse entendimento. Este trabalho analisa a natureza dos sistemas OTR desde o ponto de vista do software. Os requerimentos para um sistema genérico são levantados. Baseado nisso, é proposta uma arquitetura de software que pode ser adaptada para casos específicos. Os benefícios da arquitetura projetada foram listados. Ao mesmo tempo, o trabalho propõe uma nova abordagem para implementar essa arquitetura: Sistema Multi-Agentes (SMA). Dois protótipos de sistema OTR foram desenvolvidos. O primeiro aplicado num estudo de caso bem conhecido na literatura acadêmica. O segundo voltado para ser usado em uma unidade industrial. Os benefícios da abordagem SMA e da arquitetura, tanto na pesquisa relacionada com OTR, quanto na implementação em plantas reais, são analisados no texto. Um arcabouço de software que abrange os principais conceitos da ontologia OTR é proposto como resultado derivado do desenvolvimento. O arcabouço foi projetado para ser genérico, possibilitando seu uso no desenvolvimento de novas aplicações OTR e sua extensão a cenários muito específicos.

The increasing economic competition drives the industry to implement tools that improve their processes efficiencies. The process automation is one of these tools, and the Real Time Optimization (RTO) is an automation methodology that considers economic aspects to update the process control in accordance with market prices and disturbances. Basically, RTO uses a steady-state phenomenological model to predict the process behavior, and then, optimizes an economic objective function subject to this model. Although largely implemented in industry, there is not a general agreement about the benefits of implementing RTO due to some limitations discussed in the present work: structural plant/model mismatch, identifiability issues and low frequency of set points update. Some alternative RTO approaches have been proposed in literature to handle the problem of structural plant/model mismatch. However, there is not a sensible comparison evaluating the scope and limitations of these RTO approaches under different aspects. For this reason, the classical two-step method is compared to more recently derivative-based methods (Modifier Adaptation, Integrated System Optimization and Parameter estimation, and Sufficient Conditions of Feasibility and Optimality) using a Monte Carlo methodology. The results of this comparison show that the classical RTO method is consistent, providing a model flexible enough to represent the process topology, a parameter estimation method appropriate to handle measurement noise characteristics and a method to improve the sample information quality. At each iteration, the RTO methodology updates some key parameter of the model, where it is possible to observe identifiability issues caused by lack of measurements and measurement noise, resulting in bad prediction ability. Therefore, four different parameter estimation approaches (Rotational Discrimination, Automatic Selection and Parameter estimation, Reparametrization via Differential Geometry and classical nonlinear Least Square) are evaluated with respect to their prediction accuracy, robustness and speed. The results show that the Rotational Discrimination method is the most suitable to be implemented in a RTO framework, since it requires less a priori information, it is simple to be implemented and avoid the overfitting caused by the Least Square method. The third RTO drawback discussed in the present thesis is the low frequency of set points update, this problem increases the period in which the process operates at suboptimum conditions. An alternative to handle this problem is proposed in this thesis, by integrating the classic RTO and Self-Optimizing control (SOC) using a new Model Predictive Control strategy. The new approach demonstrates that it is possible to reduce the problem of low frequency of set points updates, improving the economic performance. Finally, the practical aspects of the RTO implementation are carried out in an industrial case study, a Vapor Recompression Distillation (VRD) process located in Paulínea refinery from Petrobras. The conclusions of this study suggest that the model parameters are successfully estimated by the Rotational Discrimination method; the RTO is able to improve the process profit in about 3%, equivalent to 2 million dollars per year; and the integration of SOC and RTO may be an interesting control alternative for the VRD process. / O aumento da concorrência motiva a indústria a implementar ferramentas que melhorem a eficiência de seus processos. A automação é uma dessas ferramentas, e o Real Time Optimization (RTO) ou Otimização em Tempo Real, é uma metodologia de automação que considera aspectos econômicos e restrições de processos e equipamentos para atualizar o controle do processo, de acordo com preços de mercado e distúrbios. Basicamente, o RTO usa um modelo fenomenológico em estado estacionário para predizer o comportamento do processo, em seguida, otimiza uma função objetivo econômica sujeita a esse modelo. Embora amplamente utilizado na indústria, não há ainda um consenso geral sobre os benefícios da implementação do RTO, devido a algumas limitações discutidas no presente trabalho: incompatibilidade estrutural entre planta e modelo, problemas de identificabilidade e baixa frequência de atualização dos set points. Algumas metodologias de RTO foram propostas na literatura para lidar com o problema da incompatibilidade entre planta e modelo. No entanto, não há uma comparação que avalie a abrangência e as limitações destas diversas abordagens de RTO, sob diferentes aspectos. Por esta razão, o método clássico de RTO é comparado com metodologias mais recentes, baseadas em derivadas (Modifier Adaptation, Integrated System Optimization and Parameter estimation, and Sufficient Conditions of Feasibility and Optimality), utilizando-se o método de Monte Carlo. Os resultados desta comparação mostram que o método clássico de RTO é coerente, desde que seja proporcionado um modelo suficientemente flexível para se representar a topologia do processo, um método de estimação de parâmetros apropriado para lidar com características de ruído de medição e um método para melhorar a qualidade da informação da amostra. Já os problemas de identificabilidade podem ser observados a cada iteração de RTO, quando o método atualiza alguns parâmetros-chave do modelo, o que é causado principalmente pela ausência de medidas e ruídos. Por esse motivo, quatro abordagens de estimação de parâmetros (Discriminação Rotacional, Seleção Automática e Estimação de Parâmetros, Reparametrização via Geometria Diferencial e o clássico Mínimos Quadrados não-lineares) são avaliados em relação à sua capacidade de predição, robustez e velocidade. Os resultados revelam que o método de Discriminação Rotacional é o mais adequado para ser implementado em um ciclo de RTO, já que requer menos informação a priori, é simples de ser implementado e evita o sobreajuste observado no método de Mínimos Quadrados. A terceira desvantagem associada ao RTO é a baixa frequência de atualização dos set points, o que aumenta o período em que o processo opera em condições subotimas. Uma alternativa para lidar com este problema é proposta no presente trabalho, integrando-se o RTO e o Self-Optimizing Control (SOC) através de um novo algoritmo de Model Predictive Control (MPC). Os resultados obtidos com a nova abordagem demonstram que é possível reduzir o problema da baixa frequência de atualização dos set points, melhorando o desempenho econômico do processo. Por fim, os aspectos práticos da implementação do RTO são discutidos em um estudo de caso industrial, que trata de um processo de destilação com bomba de calor, localizado na Refinaria de Paulínia (REPLAN - Petrobras). Os resultados deste estudo sugerem que os parâmetros do modelo são estimados com sucesso pelo método de Discriminação Rotacional; que o RTO é capaz de aumentar o lucro do processo em cerca de 3%, o equivalente a 2 milhões de dólares por ano; e que a integração entre SOC e RTO pode ser uma alternativa interessante para o controle deste processo de destilação.

Applications of the existing steady-state plant-wide optimization and the single-scale fast-rate dynamic optimization strategies to an integrated plant with material recycle have been impeded by several factors. While the steady-state optimization formulation is very simple, the very long transient dynamics of an integrated plant have limited the optimizers execution rate to be extremely low, yielding a suboptimal performance. In contrast, performing dynamic plant-wide optimization at the same rate as local controllers requires exorbitant on-line computational load and may increase the sensitivity to high-frequency dynamics that are irrelevant to the plant-level interactions, which are slow-scale in nature. This thesis proposes a novel multi-scale dynamic optimization and control strategy suitable for an integrated plant. The dynamic plant-wide optimizer in this framework executes at a slow rate to track the slow-scale plant-wide interactions and economics, while leaving the local controllers to handle fast changes related to the local units. Moreover, this slow execution rate demands less computational and modeling requirement than the fast-rate optimizer. An important issue of this method is obtaining a suitable dynamic model when first-principles are unavailable. The difficulties in the system identification process are designing proper input signal to excite this ill-conditioned system and handling the lack of slow-scale dynamic data when the plant experiment cannot be conducted for a long time compared to the settling time. This work presents a grey-box modeling method to incorporate steady-state information to improve the model prediction accuracy. A case study of an integrated plant example is presented to address limitations of the nonlinear model predictive control (NMPC) in terms of the on-line computation and its inability to handle stochastic uncertainties. Then, the approximate dynamic programming (ADP) framework is investigated. This method computes an optimal operating policy under uncertainties off-line. Then, the on-line multi-stage optimization can be transformed into a single-stage problem, thus reducing the real-time computational effort drastically. However, the existing ADP framework is not suitable for an integrated plant with high dimensional state and action space. In this study, we combine several techniques with ADP to apply nonlinear optimal control to the integrated plant example and show its efficacy over NMPC.

This thesis presents an integrated framework for online obstacle avoidance of rotary-wing unmanned aerial vehicles (UAVs), which can provide UAVs an obstacle field navigation capability in a partially or completely unknown obstacle-rich environment. The framework is composed of a LIDAR interface, a local obstacle grid generation, a receding horizon (RH) trajectory optimizer, a global shortest path search algorithm, and a climb rate limit detection logic. The key feature of the framework is the use of an optimization-based trajectory generation in which the obstacle avoidance problem is formulated as a nonlinear trajectory optimization problem with state and input constraints over the finite range of the sensor. This local trajectory optimization is combined with a global path search algorithm which provides a useful initial guess to the nonlinear optimization solver. Optimization is the natural process of finding the best trajectory that is dynamically feasible, safe within the vehicle's flight envelope, and collision-free at the same time. The optimal trajectory is continuously updated in real time by the numerical optimization solver, Nonlinear Trajectory Generation (NTG), which is a direct solver based on the spline approximation of trajectory for dynamically flat systems. In fact, the overall approach of this thesis to finding the optimal trajectory is similar to the model predictive control (MPC) or the receding horizon control (RHC), except that this thesis followed a two-layer design; thus, the optimal solution works as a guidance command to be followed by the controller of the vehicle. The framework is implemented in a real-time simulation environment, the Georgia Tech UAV Simulation Tool (GUST), and integrated in the onboard software of the rotary-wing UAV test-bed at Georgia Tech. Initially, the 2D vertical avoidance capability of real obstacles was tested in flight. Then the flight test evaluations were extended to the benchmark tests for 3D avoidance capability over the virtual obstacles, and finally it was demonstrated on real obstacles located at the McKenna MOUT site in Fort Benning, Georgia. Simulations and flight test evaluations demonstrate the feasibility of the developed framework for UAV applications involving low-altitude flight in an urban area.