Generalized Optimal Control for Autonomous Mine Countermeasures Missions

Kragelund, Sean; Walton, Claire; Kaminer, Isaac; Dobrokhodov, Vladimir

doi:10.1109/joe.2020.2998930

Cited by 11 publications

(7 citation statements)

References 54 publications

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“…Note that the optimal control depends on the derivative of the value function; hence, it may suffer from noise if the value function estimate is not good. We observe several interesting points in this figure: First, note how the optimal control obtained is very similar to the baseline control (17), as the control tends to be pointing towards the origin. Second, note how there are variations in the control depending on the disturbance; this is expected, as the DGM is able to obtain optimal control functions for each concrete disturbance.…”

Section: Training Resultsmentioning

confidence: 62%

“…In [ 15 ], the authors use optimal control tools for underwater navigation, but they have to use a restricted mesh of discrete states to avoid the curse of dimensionality (as in [ 16 ] for surface vehicles). A recent work close to ours is [ 17 ], where the authors use optimal control tools for mine detection missions. They use autonomous vehicles that move in the ocean surface, and seek to obtain optimal trajectories in the sense that they maximize the mine detection probability using different detection sensor models.…”

Section: Introductionmentioning

confidence: 99%

“…Example trajectories for each control law and disturbance, where the horizontal axis denotes the x coordinate, and the vertical axis is y. The vector field represents the disturbance, the red trajectories are obtained using DGM and the blue trajectories are obtained with the baseline control(17). Observe how DGM trajectories differ the most as the disturbance effect increases, thus giving a strong advantage to DGM over the baseline when the disturbance effect is not negligible.…”

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confidence: 99%

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Deep Learning for Efficient and Optimal Motion Planning for AUVs with Disturbances

Parras

Apellániz

Zazo

2021

Sensors

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We use the recent advances in Deep Learning to solve an underwater motion planning problem by making use of optimal control tools—namely, we propose using the Deep Galerkin Method (DGM) to approximate the Hamilton–Jacobi–Bellman PDE that can be used to solve continuous time and state optimal control problems. In order to make our approach more realistic, we consider that there are disturbances in the underwater medium that affect the trajectory of the autonomous vehicle. After adapting DGM by making use of a surrogate approach, our results show that our method is able to efficiently solve the proposed problem, providing large improvements over a baseline control in terms of costs, especially in the case in which the disturbances effects are more significant.

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Section: Training Resultsmentioning

confidence: 62%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Deep Learning for Efficient and Optimal Motion Planning for AUVs with Disturbances

Parras

Apellániz

Zazo

2021

Sensors

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“…Relevant scholars pointed out that the research of automated logistics system is mainly divided into two aspects: the development of traffic planning and design, and the research and development of components of paths and processing equipment [14]. In recent years, automated guided vehicles (AGVs) are important equipment for realizing flexible and fast material handling, due to their high flexibility, automation, easy-to-control integration, and high reliability pursued by many enterprises [15].…”

Section: Related Workmentioning

confidence: 99%

Optimal Control of the Logistics Automation Transmission System Based on Partial Differential Equation

2022

Mathematical Problems in Engineering

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In this paper, the finite difference scheme of the spatiotemporal fractional convection-diffusion equation is established, and its stability and convergence are proved. Furthermore, this discrete technique is extended to solve nonlinear spatiotemporal fractional convection-diffusion equations. By using the Krylov subspace method to solve the discrete system, the numerical solution of the spatiotemporal fractional convection-diffusion equation can be obtained quickly. In this paper, an efficient optimal control algorithm is proposed to solve the free control problem of a class of nonlinear time-delay systems. We obtained the optimal control law of the system through the Bellman optimality principle, obtained the asymptotic stability criterion of the system in the form of LMI under the optimal control input by using the Lyapunov stability theory, and discussed the effect of the delay parameter on the system stability. Using the principle of intelligent neural network approximation function, the evaluation neural network and the execution neural network are used to approximate the optimal performance index function and optimal control input, respectively, the optimal control strategy of the system is obtained, and the convergence of the weight estimation error is proved to be optimal. On the basis of optimal state adjustment, the optimal tracking control problem is further solved. Numerical example results verify the effectiveness of the proposed method in terms of stability analysis, optimal state control, and optimal tracking control for the nonlinear time-delay system proposed in this paper. We calculate the parameters of the conveyor and select a reasonable transmission and sorting mechanism to realize the speed regulation of the driving motor of each mechanism. Through the work of each part, the design scheme of the automatic transmission system is formed, and the reliability, practicability, and economy of the system are guaranteed.

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“…To achieve these motion scenarios, it is crucial to develop a high‐fidelity speed tracking controller. Motivated by the rapid developments in control technologies, 27‐43 a variety of speed control methods are proposed including adaptive control, 6,7,9,11,20,21,44 sliding mode control, 3 and so forth.…”

Section: Introductionmentioning

confidence: 99%

Adaptive anti‐disturbance sampling control of autonomous surface vehicles based on discrete‐time concurrent learning extended state observers

Cui

et al. 2023

Intl J Robust & Nonlinear

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Summary This paper investigates the surge speed tracking control of an autonomous surface vehicle (ASV) subject to fully unknown internal dynamic, external disturbance, and unknown control input gain. An adaptive anti‐disturbance sampling control method is proposed for an ASV without using any model parameters. Specifically, by utilizing real‐time and historical data, discrete‐time reduced‐order and full‐order concurrent learning extended state observers (CLESOs) are designed to estimate the unknown ASV model parameters and ensure estimation convergence without requiring persistent excitation. Then, a surge speed tracking control law is designed based on the discrete‐time full‐order CLESO. Through Lyapunov stability analysis, the closed‐loop system is proven to be stable, and the estimation and tracking errors are bounded. Simulation and hardware‐in‐loop experimental results validate the effectiveness of the proposed discrete‐time CLESOs for the surge speed tracking of an ASV with fully unknown dynamic model.

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Generalized Optimal Control for Autonomous Mine Countermeasures Missions

Cited by 11 publications

References 54 publications

Deep Learning for Efficient and Optimal Motion Planning for AUVs with Disturbances

Deep Learning for Efficient and Optimal Motion Planning for AUVs with Disturbances

Optimal Control of the Logistics Automation Transmission System Based on Partial Differential Equation

Adaptive anti‐disturbance sampling control of autonomous surface vehicles based on discrete‐time concurrent learning extended state observers

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