H&lt;inf&gt;&amp;#x221E;&lt;/inf&gt; output feedback stabilization for continuous-time switched linear systems

Shi, Shuhui

doi:10.1109/icmc.2014.7231809

Cited by 2 publications

(9 citation statements)

References 16 publications

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“…Similar to [33], the terms A i q i and B i z i in (10) are to guarantee the angular position of each joint of manipulator to not exceed the preset constraint ±ε i . While the term 1 1+k yi C i in (10) is to guarantee the angular velocity of each joint to not exceed the preset constraint ±Λ i , which was not achieved in [33]. The proof will be detailed later.…”

Section: 𝐴 =mentioning

confidence: 99%

“…e virtual control law. follows: 𝑞 + 𝑘 𝐵 𝑧 + 𝜆 𝑞 + 𝜆 𝛬 𝑡𝑎𝑛ℎ 𝑒 ₵ (10) onstants determined by users. 𝜆 > 0 is a rcement learning mechanism.…”

Section: Controller Designmentioning

confidence: 99%

“…In order to successfully perform such industrial tasks, it is essential to well control the motion of robotic manipulators. However, the highly nonlinear and uncertain dynamics of robotic manipulators result in the inapplicability of many traditional controllers designed for linear systems such as linear quadratic control (LQC) [8,9] and linear H∞ control [10,11]. Hence, many researchers have been motivated to place efforts on designing advanced controllers for robotic manipulators.…”

Section: Introductionmentioning

confidence: 99%

“…The novel adaptive gain in (13) results in the smooth control torques to reduce the chattering effect caused by switching term in (10). Meanwhile, an RL-based term can effectively improve the tracking accuracy, which thereby reduces the possible steady-state tracking errors caused by the lack of asymptotic convergence;…”

mentioning

confidence: 99%

“…. , 𝑛 (10) where 𝑘 , 𝑘 , 𝑘 , ₵ and 𝑘 are the positive constants determined by users. 𝜆 > 0 is a positive variable determined by the fuzzy reinforcement learning mechanism.…”

mentioning

confidence: 99%

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Reinforcement Learning-Based Adaptive Position Control Scheme for Uncertain Robotic Manipulators with Constrained Angular Position and Angular Velocity

Xie

Lin

2023

Applied Sciences

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Aiming at robotic manipulators subject to system uncertainty and external disturbance, this paper presents a novel adaptive control scheme that uses the time delay estimation (TED) technique and reinforcement learning (RL) technique to achieve a good tracking performance for each joint of a manipulator. Compared to conventional controllers, the proposed control scheme can not only handle the system parametric uncertainty and external disturbance but also guarantee both the angular positions and angular velocities of each joint without exceeding their preset constraints. Moreover, it has been proved by using Lyapunov theory that the tracking errors are uniformly ultimately bounded (UUB) with a small bound related to the parameters of the controller. Additionally, an innovative RL-based auxiliary term in the proposed controller further minimizes the steady state tracking errors, and thereby the tracking accuracy is not compromised by the lack of asymptotic convergence of tracking errors. Finally, the simulation results validate the effectiveness of the proposed control scheme.

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