Neural network-based adaptive optimal containment control for non-affine nonlinear multi-agent systems within an identifier-actor-critic framework

Zhao, Yanwei; Niu, Ben; Zong, Guangdeng; Zhao, Xudong; Alharbi, Khalid

doi:10.1016/j.jfranklin.2023.06.014

Cited by 52 publications

(9 citation statements)

References 37 publications

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“…Substituting equations ( 30), ( 36), (37), and (38) into equation (33) yields Step j(2 ≤ 𝑗 ≤ 𝑛 − 1): From equations ( 9) and (10), it holds that…”

Section: By Calculating 𝜕𝐻mentioning

confidence: 99%

“…In recent times, there has been a surge of interest in incorporating RL into MASs. It is an interesting and challenging problem and has produced some excellent results [31][32][33][34][35][36][37][38] . For instance, in [33] , an optimal backstepping consensus control protocol based on RL was introduced for nonlinear strict-feedback MASs, which not only exhibits algorithmic simplicity but also relaxes the need for two general conditions: known dynamics and persistence excitation.…”

Section: Introductionmentioning

confidence: 99%

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Reinforcement learning-based optimal adaptive fuzzy control for nonlinear multi-agent systems with prescribed performance

Yue,

Xia

2023

Complex Engineering Systems

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In this paper, the problem of optimal adaptive consensus tracking control for nonlinear multi-agent systems with prescribed performance is investigated. To address the issue of satisfying the initial value conditions in existing results, an improved performance function is employed as the prescribed performance boundary, effectively resolving this problem. Then, by employing the error transformation function, the constrained system is converted into an unconstrained one. Furthermore, fuzzy logic systems are employed to identify unknown system parts. By applying the dynamic surface technique, the problem of "differential explosion", which often occurs in backstepping, is solved. Moreover, a distributed optimal adaptive fuzzy control protocol based on the reinforcement learning actor-critic algorithm is proposed. Under the proposed control scheme, it is proved that all the signals within the closed-loop system are bounded, and the consensus tracking errors have remained within the predefined bounds. Finally, the numerical simulation results demonstrate the effectiveness of the proposed scheme.

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“…Substituting equations ( 30), ( 36), (37), and (38) into equation (33) yields Step j(2 ≤ 𝑗 ≤ 𝑛 − 1): From equations ( 9) and (10), it holds that…”

Section: By Calculating 𝜕𝐻mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reinforcement learning-based optimal adaptive fuzzy control for nonlinear multi-agent systems with prescribed performance

Yue,

Xia

2023

Complex Engineering Systems

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“…Kernel LMS is a method that can adaptively identify nonlinear functions by approximating them using the kernel method, and it is widely used in the field of signal processing [38][39][40][41]. There are other methods for identifying nonlinear functions, such as Neural Networks (NNs) [42][43][44] and Support Vector Machines (SVMs) [45][46][47], but they have high learning costs and cannot easily achieve adaptive identification. On the other hand, Kernel LMS has the advantage of low learning costs and the ability to achieve adaptive identification.…”

Section: Introductionmentioning

confidence: 99%

Perfect Tracking Control of Linear Sliders Using Sliding Mode Control with Uncertainty Estimation Mechanism

Hoshina,

Yamada,

Deng

2024

Machines

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This paper aims to achieve precise position control of a stage used in semiconductor exposure apparatus. The demand for smart devices, such as smartphones, is rapidly expanding, and their performance is expected to continue to improve. To manufacture these devices, it is necessary to miniaturize semiconductor devices and improve productivity. The precise control of semiconductor exposure apparatus is important for the manufacture of ultra-small semiconductor devices. The stage of semiconductor exposure apparatus uses a linear motor, and this paper performs high-precision perfect tracking control of this stage. Perfect tracking control is a control method that always follows the command value while the command value changes moment by moment, and requires high accuracy. In high-precision positioning, uncertainty in the stage model has a significant impact. Therefore, this paper proposes a method to reduce tracking errors due to the influence of uncertainty by performing uncertainty compensation using sliding mode control with the estimated value of uncertainty. The estimation of uncertainty uses a method that combines Kernel LMS with an observer. Instead of the widely used Gaussian kernel, this paper uses a generalized Gaussian kernel that allows for finer parameter settings. Furthermore, this paper proposes a method to adaptively optimize the shape parameter of the generalized Gaussian kernel. Our simulations and experiments confirm that the proposed method improves tracking performance compared to conventional sliding mode control.

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“…Unlike affine systems, non-affine systems have a non-linear relation with the system output regarding the control input [27][28][29]. This property of the non-affine systems makes it very difficult to find an exact solution for them.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Adaptive Controller Based on Hyperbolic Cost Function for Non-Affine Discrete-Time Systems with Variant Control Direction

Flores-Padilla,

Treesatayapun

2024

ASI

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As technology evolves, more complex non-affine systems are created. These complex systems are hard to model, whereas most controllers require information on systems to be designed. This information is hard to obtain for systems with varying control directions. Therefore, this study introduces a novel data-driven estimator and controller tailored for single-input single-output non-affine discrete-time systems. This approach focuses on cases when the control direction varies over time and the mathematical model of the system is completely unknown. The estimator and controller are constructed using a Multiple-input Fuzzy Rules Emulated Network framework. The weight vectors are updated through the gradient descent optimization method, which employs a unique cost function that multiplies the error by a hyperbolic tangent. The stability analyses demonstrate that both the estimator and controller converge to uniformly ultimately bounded (UUB) functions of Lyapunov. To validate the results, we show experimental tests of force control that were executed on the z-axis of a drive-controlled 3D scanning robot. This system has a varying control direction, and we also provide comparison results with a state-of-the-art controller. The results show a mean absolute percentage tracking error smaller than one percent on the steady state and the expected variation in the system’s control direction.

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Neural network-based adaptive optimal containment control for non-affine nonlinear multi-agent systems within an identifier-actor-critic framework

Cited by 52 publications

References 37 publications

Reinforcement learning-based optimal adaptive fuzzy control for nonlinear multi-agent systems with prescribed performance

Reinforcement learning-based optimal adaptive fuzzy control for nonlinear multi-agent systems with prescribed performance

Perfect Tracking Control of Linear Sliders Using Sliding Mode Control with Uncertainty Estimation Mechanism

Data-Driven Adaptive Controller Based on Hyperbolic Cost Function for Non-Affine Discrete-Time Systems with Variant Control Direction

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