This article presents a novel model-based adaptive monitoring framework for the estimation of oil spills using mobile sensors. In the first of a four-stage process, simulation of a combined ocean, wind, and oil model ...
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This article presents a novel model-based adaptive monitoring framework for the estimation of oil spills using mobile sensors. In the first of a four-stage process, simulation of a combined ocean, wind, and oil model provides a state trajectory over a finite time horizon, used in the second stage to solve an adjoint optimization problem for sensing locations. In the third stage, a reduced-order model is identified from the state trajectory, utilized alongside measurements to produce smoothed state estimates in the fourth stage, which update and re-initialize the first-stage simulation. In the second stage, sensors are directed to optimal sensing locations using the solution of a partial differential equation (PDE)-constrained optimization problem. This problem formulation represents a key contributory idea, utilizing the definition of spill uncertainty as a scalar PDE to be minimized subject to sensor, ocean, wind, and oil constraints. Spill uncertainty is a function of uncertainty in 1) the bespoke model of the ocean, wind, and oil spill;2) the reduced order model identified from sensor data;and 3) the data assimilation method employed to estimate the states of the environment and spill. The uncertainty minimization is spatiotemporally weighted by a function of spill probability and information utility, prioritizing critical measurements. A numerical case study spanning a 2500-km(2) coastal area is presented, with four mobile sensors arriving 12 h after an oil leak. Compared to industry standard ``ladder pathing,'' the proposed method achieves an 80% reduction in oil distribution error and a 62% reduction in sensor distance traveled.
This paper addresses the attitude stabilization problem for fractionated space systems where the onboard computers cannot run continuously. An auxiliary filter is constructed to imitate the intermittent computer or st...
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ISBN:
(纸本)9781713867890
This paper addresses the attitude stabilization problem for fractionated space systems where the onboard computers cannot run continuously. An auxiliary filter is constructed to imitate the intermittent computer or state acquisition of a small size, power, and weight constrained spacecraft such a CubeSat. The proposed controller does not require angular rate or inertia knowledge and guarantees attitude stabilization and boundedness for all closed loop signals for persistently excited gains driving the auxiliary filter. A novel Lyapunov function is constructed to guarantee stability including auxiliary functions for strictification of controller. Simulation of a 3U form factor CubeSat example is carried out to demonstrate the feasibility of the controller.
This paper addresses the attitude stabilization problem for fractionated space systems where the onboard computers cannot run continuously. An auxiliary filter is constructed to imitate the intermittent computer or st...
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This paper addresses the attitude stabilization problem for fractionated space systems where the onboard computers cannot run continuously. An auxiliary filter is constructed to imitate the intermittent computer or state acquisition of a small size, power, and weight constrained spacecraft such a CubeSat. The proposed controller does not require angular rate or inertia knowledge and guarantees attitude stabilization and boundedness for all closed loop signals for persistently excited gains driving the auxiliary filter. A novel Lyapunov function is constructed to guarantee stability including auxiliary functions for strictification of the controller. Simulation of a 3U form factor CubeSat example is carried out to demonstrate the feasibility of the controller.
In this paper, we present an experimental test bench to implement various cooperative control strategies for multi-agent systems, and illustrate its use with experimental results for a source-seeking problem, where a ...
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In this paper, we present an experimental test bench to implement various cooperative control strategies for multi-agent systems, and illustrate its use with experimental results for a source-seeking problem, where a group of small wheeled robots termed as Zooids should locate a source of a given spatial scalar field. This algorithm is implemented as a validation to demonstrate the capabilities of the test bench. We propose to achieve this by utilising an internal target-based position controller, under the assumptions of convexity of the scalar, continuous/discrete field and availability of local measurements of the field, so that agents can calculate its gradient and its Hessian. We then show in experiments, that using estimated gradients and Hessians (with data communicated from neighbours) in the presence of noisy measurements of the field strength provides satisfactory results for convex fields, under various algorithms such as Steepest Descent, Gauss-Newton, Levenberg Marquardt. These algorithms are analysed, and experimental results are discussed. Copyright (C) 2020 The Authors.
In this paper, we consider a self-triggered formulation of model predictive control. In this variant, the controller decides at the current sampling instant itself when the next sample should be taken and the optimiza...
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In this paper, we consider a self-triggered formulation of model predictive control. In this variant, the controller decides at the current sampling instant itself when the next sample should be taken and the optimization problem be solved anew. We incorporate a pointwise-in-time resource constraint into the optimization problem, whose exact form can be chosen by the user. Thereby, the proposed scheme is made resource-aware with respect to a universal resource, which may pertain in practice for instance to communication, computation, energy or financial resources. We show that by virtue of the pointwise-in-time constraints, also a transient and an asymptotic average constraint on the resource usage are guaranteed. Furthermore, we derive conditions on the resource under which the proposed scheme achieves recursive feasibility and convergence. Finally, we demonstrate our theoretical results in a numerical example. Copyright (C) 2020 The Authors.
This paper presents a model based adaptive monitoring method for the estimation of flow tracers, with application to mapping, prediction and observation of oil spills in the immediate aftermath of an incident. Autonom...
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This paper presents a model based adaptive monitoring method for the estimation of flow tracers, with application to mapping, prediction and observation of oil spills in the immediate aftermath of an incident. Autonomous agents are guided to optimal sensing locations via the solution of a PDE constrained optimisation problem, obtained using the adjoint method. The proposed method employs a dynamic model of the combined ocean and oil dynamics, with states that are updated in real-time using a Kalman filter that fuses agent-based measurements with a reduced-order model of the ocean circulation dynamics. In turn, the updated predictions from the fluid model are used to identify and update the reduced order model, in a process of continuous feedback. The proposed method exhibits a 30% oil presence mapping and prediction improvement compared to standard industrial oil observation sensor guidance and model use. Copyright (C) 2020 The Authors.
In this paper, we present an experimental test bench to implement various cooperative control strategies for multi-agent systems, and illustrate its use with experimental results for a source-seeking problem, where a ...
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In this paper, we present an experimental test bench to implement various cooperative control strategies for multi-agent systems, and illustrate its use with experimental results for a source-seeking problem, where a group of small wheeled robots termed as Zooids should locate a source of a given spatial scalar field. This algorithm is implemented as a validation to demonstrate the capabilities of the test bench. We propose to achieve this by utilising an internal target-based position controller, under the assumptions of convexity of the scalar, continuous/discrete field and availability of local measurements of the field, so that agents can calculate its gradient and its Hessian. We then show in experiments, that using estimated gradients and Hessians (with data communicated from neighbours) in the presence of noisy measurements of the field strength provides satisfactory results for convex fields, under various algorithms such as Steepest Descent, Gauss-Newton, Levenberg Marquardt. These algorithms are analysed, and experimental results are discussed.
This paper presents a model based adaptive monitoring method for the estimation of flow tracers, with application to mapping, prediction and observation of oil spills in the immediate aftermath of an incident. Autonom...
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This paper presents a model based adaptive monitoring method for the estimation of flow tracers, with application to mapping, prediction and observation of oil spills in the immediate aftermath of an incident. Autonomous agents are guided to optimal sensing locations via the solution of a PDE constrained optimisation problem, obtained using the adjoint method. The proposed method employs a dynamic model of the combined ocean and oil dynamics, with states that are updated in real-time using a Kalman filter that fuses agent-based measurements with a reduced-order model of the ocean circulation dynamics. In turn, the updated predictions from the fluid model are used to identify and update the reduced order model, in a process of continuous feedback. The proposed method exhibits a 30% oil presence mapping and prediction improvement compared to standard industrial oil observation sensor guidance and model use.
In this paper, we consider a self-triggered formulation of model predictive control. In this variant, the controller decides at the current sampling instant itself when the next sample should be taken and the optimiza...
详细信息
In this paper, we consider a self-triggered formulation of model predictive control. In this variant, the controller decides at the current sampling instant itself when the next sample should be taken and the optimization problem be solved anew. We incorporate a pointwise-in-time resource constraint into the optimization problem, whose exact form can be chosen by the user. Thereby, the proposed scheme is made resource-aware with respect to a universal resource, which may pertain in practice for instance to communication, computation, energy or financial resources. We show that by virtue of the pointwise-in-time constraints, also a transient and an asymptotic average constraint on the resource usage are guaranteed. Furthermore, we derive conditions on the resource under which the proposed scheme achieves recursive feasibility and convergence. Finally, we demonstrate our theoretical results in a numerical example.
In this paper we develop novel results on self triggering control of nonlinear systems, subject to perturbations and actuation delays. First, considering an unperturbed nonlinear system with bounded actuation delays, ...
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ISBN:
(纸本)9781612848013
In this paper we develop novel results on self triggering control of nonlinear systems, subject to perturbations and actuation delays. First, considering an unperturbed nonlinear system with bounded actuation delays, we provide conditions that guarantee the existence of a self triggering control strategy stabilizing the closed-loop system. Then, considering parameter uncertainties, disturbances, and bounded actuation delays, we provide conditions guaranteeing the existence of a self triggering strategy, that keeps the state arbitrarily close to the equilibrium point. In both cases, we provide a methodology for the computation of the next execution time. We show on an example the relevant benefits obtained with this approach, in terms of energy consumption, with respect to control algorithms based on a constant sampling, with a sensible reduction of the average sampling time.
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