Adaptive Prescribed-Time Optimal Control for Flexible-Joint Robots via Reinforcement Learning

This article proposes a prescribed-time fuzzy optimal control approach for flexible-joint (FJ) robot systems utilizing the reinforcement learning (RL) strategy. The uniqueness of this method lies in its ability to ensure optimal tracking performance for n-link flexible joint robots within the prescr...

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Vydané v:IEEE transactions on systems, man, and cybernetics. Systems Ročník 55; číslo 4; s. 2633 - 2643
Hlavní autori: Xie, Shiyu, Sun, Wei, Sun, Yougang, Su, Shun-Feng
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: IEEE 01.04.2025
Predmet:
Actor-critic structure
Backstepping
flexible-joint (FJ) robots
fuzzy logic systems (FLSs)
MIMO
Optimal control
Performance analysis
prescribed-time optimal control
Process control
Program processors
Reinforcement learning
reinforcement learning (RL)
Robots
ISSN:2168-2216, 2168-2232
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Abstract This article proposes a prescribed-time fuzzy optimal control approach for flexible-joint (FJ) robot systems utilizing the reinforcement learning (RL) strategy. The uniqueness of this method lies in its ability to ensure optimal tracking performance for n-link flexible joint robots within the prescribed-time frame, while the actor and critic fuzzy logic system effectively approximate the optimal cost and evaluates system performance. First, the optimal controllers with the auxiliary compensation term are constructed by utilizing the online approximation of the modified performance index function and RL actor-critic structure. The designed controller can deal with unknown structure impacts and avoid model identification. Besides, in designing the prescribed-time scale function, the introduced constant term not only prevents singularity but also allows flexible setting of constraint regions. The proposed scheme is theoretically verified to satisfy the Bellman optimality principle and ensure the tracking error converges to the desired zone within the prescribed time. Finally, the practicability of the designed control scheme is further demonstrated by the 2-link FJ robot simulation example.
AbstractList This article proposes a prescribed-time fuzzy optimal control approach for flexible-joint (FJ) robot systems utilizing the reinforcement learning (RL) strategy. The uniqueness of this method lies in its ability to ensure optimal tracking performance for n-link flexible joint robots within the prescribed-time frame, while the actor and critic fuzzy logic system effectively approximate the optimal cost and evaluates system performance. First, the optimal controllers with the auxiliary compensation term are constructed by utilizing the online approximation of the modified performance index function and RL actor-critic structure. The designed controller can deal with unknown structure impacts and avoid model identification. Besides, in designing the prescribed-time scale function, the introduced constant term not only prevents singularity but also allows flexible setting of constraint regions. The proposed scheme is theoretically verified to satisfy the Bellman optimality principle and ensure the tracking error converges to the desired zone within the prescribed time. Finally, the practicability of the designed control scheme is further demonstrated by the 2-link FJ robot simulation example.
Author Sun, Wei
Sun, Yougang
Su, Shun-Feng
Xie, Shiyu
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Snippet This article proposes a prescribed-time fuzzy optimal control approach for flexible-joint (FJ) robot systems utilizing the reinforcement learning (RL)...
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SubjectTerms Actor-critic structure
Backstepping
flexible-joint (FJ) robots
fuzzy logic systems (FLSs)
MIMO
Optimal control
Performance analysis
prescribed-time optimal control
Process control
Program processors
Reinforcement learning
reinforcement learning (RL)
Robots
Title Adaptive Prescribed-Time Optimal Control for Flexible-Joint Robots via Reinforcement Learning
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Volume 55
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