On the real-time receding horizon control in harbor defense

Lee, Seungho; Dullerud, Geir E.; Polak, E.

doi:10.1109/acc.2015.7171889

Cited by 4 publications

(4 citation statements)

References 12 publications

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“…We have deposited the video for these experiments at https://uofi.box.com/JNRL. For detailed explanations regarding the implementation, see .…”

Section: Resultsmentioning

confidence: 99%

“…In the conference papers , we have developed a centralized receding horizon control (RHC) feedback law for controlling a fleet of unmanned, or human piloted under computer instruction, computer controlled craft tasked with the protection of a harbor from terrorist ( intruder ) attacks. By a centralized RHC law, we mean one that considers the defending and intruding fleets as single “aggregated” dynamical systems.…”

Section: Introductionmentioning

confidence: 99%

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Validation of a strategy for harbor defense based on the use of a min‐max algorithm receding horizon control law

Foraker

Lee

Polak

2016

Naval Research Logistics

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We present a validation of a centralized feedback control law for robotic or partially robotic water craft whose task is to defend a harbor from an intruding fleet of water craft. Our work was motivated by the need to provide harbor defenses against hostile, possibly suicidal intruders, preferably using unmanned craft to limit potential casualties. Our feedback control law is a sample‐data receding horizon control law, which requires the solution of a complex max‐min problem at the start of each sample time. In developing this control law, we had to deal with three challenges. The first was to develop a max‐min problem that captures realistically the nature of the defense‐intrusion game. The second was to ensure the solution of this max‐min problem can be accomplished in a small fraction of the sample time that would be needed to control a possibly fast moving craft. The third, to which this article is dedicated, was to validate the effectiveness of our control law first through computer simulations pitting a computer against a computer or a computer against a human, then through the use of model hovercraft in a laboratory, and finally on the Chesapeake Bay, using Yard Patrol boats. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 247–259, 2016

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“…We have deposited the video for these experiments at https://uofi.box.com/JNRL. For detailed explanations regarding the implementation, see .…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Validation of a strategy for harbor defense based on the use of a min‐max algorithm receding horizon control law

Foraker

Lee

Polak

2016

Naval Research Logistics

Self Cite

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“…JOptimizer is an open source Java library that aims to solve convex optimization problems [10]. This library has been used in several projects, such as [11] where it was used for solving quadratic optimization problems in Markov decision processes, or [12], where it was used to compute the control law of a model predictive controller for a harbor defense scenario.…”

Section: Luis Garcia and Alejandro Astudillo Contributed Equally To This Workmentioning

confidence: 99%

Fast Model Predictive Control on a Smartphone-based Flight Controller

García

Astudillo

Rosero

2019

2019 IEEE 4th Colombian Conference on Automatic Control (CCAC)

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Several attempts have been made to execute control of a system using only a smartphone's processor running computationally inexpensive algorithms such as PID, LQR or H∞ controllers. This paper presents design and implementation of model predictive controllers on a smartphone using the numerical optimization framework CasADi. To evaluate this framework's performance (and compare its results with those from a Java library JOptimizer's deployment) the implemented model predictive control algorithm was subjected to simulations of running a quadrotor control system on a smartphone. It attained a tracking error of 0.0693 m. These evaluation results open the possibility of implementing more computationally expensive algorithms on a smartphone's processor including online or real-time usage.

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“…Researches are paid for applying RHC approach in various systems, such as state delayed systems, 12 input delayed systems, 13 switching systems, 14 stochastic systems with control and state multiplicative noise, 15 linear discrete time systems with probabilistic system parameters, 16 and discrete time mean-field-type games. 17 Meanwhile, RHC are considered in some application areas, such as induction motors, 18 harbor-defense, 19 torque control of gasoline engines, 20 and high-speed train. 21 Undoubtedly, RHC approach has been applied to trajectory tracking problems of re-entry vehicles.…”

Section: Introductionmentioning

confidence: 99%

Guidance for hypersonic re‐entry using receding horizon control with finite terminal weighting matrix

Sun

Jin

Zhang

2022

Optim Control Appl Methods

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In this article, nominal-trajectory-based guidance approach for hypersonic reentry vehicles is considered using the Vinh's equations. The nominal trajectory is generated via pseudo-spectral method and then tracked by receding horizon control approach. Specially, several strategies are adopted to simplify the practical application. First, we formulate the receding optimal problem discretely using the approximated discrete linear state error dynamics. Thus, the differential matrix Riccati equation is substituted by a difference matrix Riccati equation.Second, we set the terminal state weighting matrix finite which brings flexibility of adjusting the sample period and prediction horizon. Third, the time-varying state error dynamics is approximated by time invariant system. Lastly, instead of obtaining terminal weighting matrices which satisfy a linear matrix inequality using the LMI toolbox, we set them the same constant matrix. Naturally, numerical simulation is available to verify the validity and efficiency of the proposed approach.

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On the real-time receding horizon control in harbor defense

Cited by 4 publications

References 12 publications

Validation of a strategy for harbor defense based on the use of a min‐max algorithm receding horizon control law

Validation of a strategy for harbor defense based on the use of a min‐max algorithm receding horizon control law

Fast Model Predictive Control on a Smartphone-based Flight Controller

Guidance for hypersonic re‐entry using receding horizon control with finite terminal weighting matrix

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