Trajectory generation for aircraft subject to dynamic weather uncertainty

Kamgarpour, Maryam; Dadok, Vera M.; Tomlin, Claire J.

doi:10.1109/cdc.2010.5717889

Cited by 30 publications

(19 citation statements)

References 22 publications

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“…This approach can be thought of as similar to approaches commonly used in aviation path planning in which simplified models are used to compute trajectories and more precise and complicated models are used for control, e.g., [64]. We assume that DR energy arbitrage does not affect market prices, so our results are valid only if TCLs constitute a small fraction of the total market.…”

Section: Energy Arbitragementioning

confidence: 92%

Modeling, Analysis, and Control of Demand Response Resources

Mathieu

2012

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While the traditional goal of an electric power system has been to control supply to fulfill demand, the demand-side can plan an active role in power systems via Demand Response (DR), defined by the Department of Energy (DOE) as "a tariff or program established to motivate changes in electric use by end-use customers in response to changes in the price of electricity over time, or to give incentive payments designed to induce lower electricity use at times of high market prices or when grid reliability is jeopardized" [29]. DR can provide a variety of benefits including reducing peak electric loads when the power system is stressed and fast timescale energy balancing. Therefore, DR can improve grid reliability and reduce wholesale energy prices and their volatility.This dissertation focuses on analyzing both recent and emerging DR paradigms. Recent DR programs have focused on peak load reduction in commercial buildings and industrial facilities (C&I facilities). We present methods for using 15-minute-interval electric load data, commonly available from C&I facilities, to help building managers understand building energy consumption and 'ask the right questions' to discover opportunities for DR. Additionally, we present a regression-based model of whole building electric load, i.e., a baseline model, which allows us to quantify DR performance. We use this baseline model to understand the performance of 38 C&I facilities participating in an automated dynamic pricing DR program in California. In this program, facilities are expected to exhibit the same response each DR event. We find that baseline model error makes it difficult to precisely quantify changes in electricity consumption and understand if C&I facilities exhibit event-to-event variability in their response to DR signals. Therefore, we present a method to compute baseline model error and a metric to determine how much observed DR variability results from baseline model error rather than real variability in response. We find that, in general, baseline model error is large. Though some facilities exhibit real DR variability, most observed variability results from baseline model error. In some cases, however, aggregations of C&I facilities exhibit real DR variability, which could create challenges for power system operation. These results have implications for DR program design and deployment. 2Emerging DR paradigms focus on faster timescale DR. Here, we investigate methods to coordinate aggregations of residential thermostatically controlled loads (TCLs), including air conditioners and refrigerators, to manage frequency and energy imbalances in power systems. We focus on opportunities to centrally control loads with high accuracy but low requirements for sensing and communications infrastructure. Specifically, we compare cases when measured load state information (e.g., power consumption and temperature) is 1) available in real time; 2) available, but not in real time; and 3) not available. We develop Markov Chain models to describe the temperature state e...

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Section: Energy Arbitragementioning

confidence: 92%

Modeling, Analysis, and Control of Demand Response Resources

Mathieu

2012

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“…groups of extremely simple robotic agents, with limited communication computation and sensing abilities, designed to be deployed together in order to accomplish various tasks [36,47]. As regions with storms and hazardous weather are analogous as obstacles in the airspace, the problem of control of multiple aerial robots through hazardous weather is similar to the problem of control of multiple agents through obstacles [29,30]. Modularization is accepted as the most naturally occurring structure able to exhibit complex behaviors from a network of individually simple components, which interact with each other in relatively simple ways.…”

Section: Introductionmentioning

confidence: 99%

Multi Aerial Robot Planning

Sebbane

2014

Intelligent Systems, Control and Automation: Science and Engineering

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Multi-robot systems are a major research topic in robotics. Designing, testing and deploying in the real world a large number of aerial robots is a concrete possibility due to the recent technological advances. The first section of this chapter treats the different aspects of cooperation in a multi-agent systems. A cooperative control should be designed in terms of the available feedback information. A cascade-type guidance law is proposed, followed by consensus approach and flocking behavior. Since information flow over the network changes over time, cooperative control must react accordingly but ensure group cooperative behavior which is the major issue in analysis and synthesis. Connectivity and convergence of formations are also studied. Team approach is followed by deterministic decision making. Plans may be required for a team of aerial robots to plan for sensing, plan for action or plan for communication. Distributed receding horizon control as well as conflict resolution, artificial potentials and symbolic planning are thus analyzed. Then, association with limited communications is studied, followed by genetic algorithms and game theory reasoning. Next, multi-agent decision making under uncertainty is considered, formulating the Bayesian decentralized team decision problem, with and without explicit communication. Algorithms for optimal planning are then introduced as well as for task allocation and distributed chance constrained task allocation. Finally, some case studies are presented such as reconnaissance mission that can be defined as the road search problem or the general vehicle routing problem. Then, an approach is considered to coordinate a group of aerial robots without a central supervision, by using only local interactions between the robots. The third case is the optimization of perimeter patrol operation. If an aerial robot must be close from a location to monitor it correctly and the number of aerial robots does not allow covering each site simultaneously, a path planning problem arises. Finally stochastic strategies for surveillance are presented.Y.

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“…This problem has been well-studied in the literature. 9,13 We will consider the motion of the aircraft as a discrete-time stochastic system. The stochasticity stems from the uncertainty in the trajectory evolution, due to potential wind and process noise, and in the convective weather cell characterization.…”

Section: Reach-avoid Methodologymentioning

confidence: 99%

Optimal Aircraft Trajectory Planning in the Presence of Stochastic Convective Weather Cells

González-Arribas

Hentzen

Sanjurjo-Rivo

et al. 2017

17th AIAA Aviation Technology, Integration, and Operations Conference

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The Air Traffic Management system is heavily influenced by meteorological uncertainty, and convective weather cells represent one of the most relevant uncertain meteorological phenomena. They are weather hazards that must be avoided through tactical trajectory modifications. As a consequence of the existence in uncertainty in meteorological forecasts and nowcasts, it is important to consider the convective weather cells to be avoided as a stochastic, time-dependent process. In this paper we present a comparative analysis of two methodologies for handling stochastic storms in trajectory planning: one based on stochastic reachability and a second one, based on robust optimal control. In the former, the thunderstorm avoidance problem is modelled as a stochastic reach-avoid problem, consid-ering the motion of the aircraft as a discrete-time stochastic system and the weather haz-ards as random set-valued obstacles. Dynamic programming is used to compute a Markov feedback policy that maximizes the probability of reaching the target before entering the unsafe set, i.e., the hazardous weather zones. For the latter, the stochastic dynamics of the storms are modeled in continuous time. We implement an optimal control formulation that allows different possible realizations of the stochastic process to be considered.The resulting problem is then transcribed to a nonlinear programming (NLP) problem through the use of direct numerical methods. A benchmark case study is presented, in which the effectiveness of the two proposed approaches are analyzed.

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Trajectory generation for aircraft subject to dynamic weather uncertainty

Cited by 30 publications

References 22 publications

Modeling, Analysis, and Control of Demand Response Resources

Modeling, Analysis, and Control of Demand Response Resources

Multi Aerial Robot Planning

Optimal Aircraft Trajectory Planning in the Presence of Stochastic Convective Weather Cells

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