Decentralized model reference adaptive control for interconnected robotic systems

Amor, Rabeb Ben; Elloumi, Salwa

doi:10.1109/sta.2017.8314907

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…In cases where a dynamic model is available, certain model-reference adaptive control approaches may be considered. These involve backstepping, sliding mode control, and Lyapunov methods, for example [47][48][49][50][51][52][53][54]. Given the dependency of such methods on the dynamics the process, the control strategies inherit such limitations.…”

Section: Introductionmentioning

confidence: 99%

Real-time measurement-driven reinforcement learning control approach for uncertain nonlinear systems

Abouheaf

Boase

Gueaieb

et al. 2023

Engineering Applications of Artificial Intelligence

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Section: Introductionmentioning

confidence: 99%

Real-time measurement-driven reinforcement learning control approach for uncertain nonlinear systems

Abouheaf

Boase

Gueaieb

et al. 2023

Engineering Applications of Artificial Intelligence

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“…This approach tackled the high oscillations in the actuator response due to the tuning with high gain adaptive rates. A decentralized MRAS is developed in [9] for large-scale interconnected systems, such as robotic manipulators. An adaptive control approach combined with anti-windup structure is developed for autonomous underwater vehicles (AUVs) in [10].…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Model-Reference Adaptive Control Approach Based on Reinforcement Learning

Abouheaf

Gueaieb²,

Spinello

et al. 2021

2021 IEEE International Symposium on Robotic and Sensors Environments (ROSE)

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Model-reference adaptive systems refer to a consortium of techniques that guide plants to track desired reference trajectories. Approaches based on theories like Lyapunov, sliding surfaces, and backstepping are typically employed to advise adaptive control strategies. The resulting solutions are often challenged by the complexity of the reference model and those of the derived control strategies. Additionally, the explicit dependence of the control strategies on the process dynamics and reference dynamical models may contribute in degrading their efficiency in the face of uncertain or unknown dynamics. A model-reference adaptive solution is developed here for autonomous systems where it solves the Hamilton-Jacobi-Bellman equation of an error-based structure. The proposed approach describes the process with an integral temporal difference equation and solves it using an integral reinforcement learning mechanism. This is done in real-time without knowing or employing the dynamics of either the process or reference model in the control strategies. A class of aircraft is adopted to validate the proposed technique.

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Decentralized Control Approach for Multiple Cooperative Robots manipulating a Common Object

Amor

Elloumi

2020

2020 4th International Conference on Advanced Systems and Emergent Technologies (IC_ASET)

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Decentralized model reference adaptive control for interconnected robotic systems

Cited by 3 publications

References 6 publications

Real-time measurement-driven reinforcement learning control approach for uncertain nonlinear systems

Real-time measurement-driven reinforcement learning control approach for uncertain nonlinear systems

A Data-Driven Model-Reference Adaptive Control Approach Based on Reinforcement Learning

Decentralized Control Approach for Multiple Cooperative Robots manipulating a Common Object

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