Adaptive learning of human motor behaviors: An evolving inverse optimal control approach

El-Hussieny, Haitham; Abouelsoud, A. A.; Assal, Samy F. M.; Megahed, Saïd M.

doi:10.1016/j.engappai.2016.01.024

Cited by 37 publications

(15 citation statements)

References 22 publications

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“…Due to the close relationship between the problems of inverse differential games and inverse optimal control, existing methods of inverse differential games and inverse optimal control share the same limitations. For example, the bilevel methods of inverse optimal control of [28] and [29] have computationally expensive implementations that involve the solution of optimal control problems inside numeric optimisation routines. The bilevel and nested optimisation methods of inverse differential games proposed in in [7], [9] and derived from these bilevel methods of inverse optimal control thus also have computationally implementations that scale poorly with the number of players, and state and control dimensions.…”

Section: Introductionmentioning

confidence: 99%

Inverse Open-Loop Noncooperative Differential Games and Inverse Optimal Control

Molloy

Inga

Flad

et al. 2020

IEEE Trans. Automat. Contr.

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We consider the problem of computing parameters of player cost functionals such that given state and control trajectories constitute an open-loop Nash equilibrium for a noncooperative differential game. We propose two methods for solving this inverse differential game problem and novel conditions under which our methods compute unique cost-functional parameters. Our conditions are analogous to persistence of excitation conditions in adaptive control and parameter estimation. The efficacy of our methods is illustrated in simulations. Index Terms-Game theory, inverse differential games, inverse optimal control, optimal control.

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Section: Introductionmentioning

confidence: 99%

Inverse Open-Loop Noncooperative Differential Games and Inverse Optimal Control

Molloy

Inga

Flad

et al. 2020

IEEE Trans. Automat. Contr.

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“…This control strategy is based on a linear approximation of the model such that asymptotically stability in a neighborhood of the operating point is guaranteed if the closed loop system is Hurwitz and the dynamic states are bounded. Optimal control strategies based on the nonlinear model have been proposed for biological processes . The inverse optimal control synthesizes a stabilizing optimal controller based on a nonlinear control Lyapunov function minimizing a cost functional.…”

Section: Introductionmentioning

confidence: 99%

“…Optimal control strategies based on the nonlinear model have been proposed for biological processes. 14,15 The inverse optimal control synthesizes a stabilizing optimal controller based on a nonlinear control Lyapunov function minimizing a cost functional. Then, the proposed approach is extended to disturbed nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Inverse optimal neural control via passivity approach for nonlinear anaerobic bioprocesses with biofuels production

Gurubel

Sánchez²,

Coronado-Mendoza

et al. 2019

Optim Control Appl Methods

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Summary This paper proposes an inverse optimal neural control method of a nonlinear anaerobic bioprocesses model for simultaneous hydrogen and methane production in presence of disturbances. Based on the fundamental properties of the system, a passivity approach is designed such that asymptotic stability is guaranteed. A recurrent high‐order neural network for unknown nonlinear systems in presence of unknown bounded disturbances and parameter uncertainties is proposed to identify nonmeasurable state variables of the system, which are directly related to biofuels production. Optimal control laws based on the neural model are proposed so that the passivation of the entire plant is preserved. The neural control strategy performance for trajectory tracking in presence of disturbances is proven. Results via simulation show the optimal control methodology efficiency to stabilize the H2 and CH4 productions along desired trajectories even in presence of disturbances.

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“…In the optimal control aspect, Ornelas-Tellez, Sanchez, and Loukianov presented an inverse optimal control approach to prevent solving the associated Hamilton-Jacobi-Bellman equation and minimize the cost function in stabilizing discrete-time nonlinear systems [14]. El-Hussieny, Abouelsoud, and Assal used particle swarm optimization to retrieve the unknown cost in their proposed ILQR problem; they proposed an evolving ILQR algorithm in refining the learned cost once new unseen demonstrations exist to overcome the overfitting problem [15]. In the fuzzy control aspect, Boukezzoula et al applied the Takagi-Sugeno fuzzy model and the fuzzy mathematical model to identify the inverse model of nonlinear systems [16].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Inverse Control Based on Kriging Algorithm and Lyapunov Theory of Crawler Electromechanical System

et al. 2018

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The electromechanical system of a crawler is a multi-input, multioutput strongly coupled nonlinear system. In this study, an adaptive inverse control method based on kriging algorithm and Lyapunov theory is proposed to improve control accuracy during adaptive driving. The electromechanical coupling model of the electromechanical system is established on the basis of the dynamic analysis of the crawler. In accordance with the kriging algorithm, the inverse model of the electromechanical system of the crawler is established by offline data. The adaptive travel control law of the crawler is obtained on the basis of Lyapunov theory. Combined with the kriging algorithm, the adaptive driving reverse control method is designed, and the online system is used to update and perfect the inverse system model in real time. Finally, the virtual prototype model of the crawler is established, and the control effect of the adaptive inverse control method is verified by theoretical analysis and virtual prototype simulation.

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Adaptive learning of human motor behaviors: An evolving inverse optimal control approach

Cited by 37 publications

References 22 publications

Inverse Open-Loop Noncooperative Differential Games and Inverse Optimal Control

Inverse Open-Loop Noncooperative Differential Games and Inverse Optimal Control

Inverse optimal neural control via passivity approach for nonlinear anaerobic bioprocesses with biofuels production

Adaptive Inverse Control Based on Kriging Algorithm and Lyapunov Theory of Crawler Electromechanical System

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