Neural minimal learning backstepping control of stochastic active suspension systems with hydraulic actuator saturation

Homayoun, Behrouz; Arefi, Mohammad Mehdi; Vafamand, Navid; Yin, Shen

doi:10.1016/j.jfranklin.2020.10.020

Cited by 19 publications

(7 citation statements)

References 41 publications

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“…All authors demonstrated the advantages of the proposed approach compared to standard regulators. In turn, Homayoun [34] utilized the RBF network to control an active suspension system with hydraulic actuators. Proposals for adaptive neural networks to control several various hydraulic components were presented by Yang.…”

Section: Neural Controlmentioning

confidence: 99%

Artificial Intelligence Methods in Hydraulic System Design

Filo

2023

Energies

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Reducing energy consumption and increasing operational efficiency are currently among the leading research topics in the design of hydraulic systems. In recent years, hydraulic system modeling and design techniques have rapidly expanded, especially using artificial intelligence methods. Due to the variety of algorithms, methods, and tools of artificial intelligence, it is possible to consider the prospects and directions of their further development. The analysis of the most recent publications allowed three leading technologies to be indicated, including artificial neural networks, evolutionary algorithms, and fuzzy logic. This article summarizes their current applications in the research, main advantages, and limitations, as well as expected directions for further development.

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Section: Neural Controlmentioning

confidence: 99%

Artificial Intelligence Methods in Hydraulic System Design

Filo

2023

Energies

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“…Considering Young inequality [14], one has:

\begin{equation}\frac{{\left( {3{Q}^4 + \bar{z}_1^4} \right)\bar{z}_1^2}}{{2{{\left( {{Q}^4 - \bar{z}_1^4} \right)}}^2}}B_1^T{B}_1 \le \frac{{{{\left( {3{Q}^4 + \bar{z}_1^4} \right)}}^2\bar{z}_1^4}}{{4r_1^2{{\left( {{Q}^4 - \bar{z}_1^4} \right)}}^4}}{B}_1^4 + \frac{1}{4}r_1^2\end{equation}

\begin{equation}\frac{{\bar{z}_1^3}}{{{Q}^4 - \bar{z}_1^4}}{\bar{z}}_2 \le \frac{3}{4}\frac{{\bar{z}_1^4}}{{{{\left( {{Q}^4 - \bar{z}_1^4} \right)}}^{\frac{4}{3}}}} + \frac{1}{4}\bar{z}_2^4\end{equation}

\frac{{\overset{z}{true}}_{1}^{3}}{Q^{4} - {\bar{z}}_{1}^{4}} d_{1} badbreak\leq \frac{{\overset{z}{true}}_{1}^{3}}{Q^{4} - {\bar{z}}_{1}^{4}} d

…”

Section: Adaptive Robust Controller Designmentioning

confidence: 99%

“…Recent advances in the stochastic stability analysis of nonlinear complex systems provide an effective platform to design a controller for stochastic systems [11][12][13][14]. In this regard, by introducing the so-called Ito operator, the concept of bounded in probability stability emerged to handle the stochastic systems.…”

Section: Introductionmentioning

confidence: 99%

Command filtered backstepping control of constrained flexible joint robotic manipulator

Arefi

Vafamand

Homayoun

et al. 2023

IET Control Theory & Appl

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Here, an adaptive radial basis function (RBF) neural network (NN) backstepping controller is proposed for a class of input‐constrained flexible joint robotic manipulators represented by strict‐feedback form with unknown terms, external stochastic disturbance, and output disturbance. The proposed approach is robust against both deterministic and stochastic uncertainties and disturbances and copes with the control input amplitude saturation. Moreover, by deploying the minimal learning parameter method and command filter technique, the computational burden of derivative terms and adaptive terms greatly decreases. Considering the mean‐value theorem assists us to avoid the need for having the input saturation bounds in prior. The suggested tracking control scheme mandates the closed‐loop system states to be semi‐globally bounded‐in‐probability. Also, a quartic Barrier Lyapunov function is utilized to force the tracking error to be confined within a pre‐chosen small region around the origin. Eventually, a numerical simulation of a flexible joint robot manipulator with a single link is performed to show the effectiveness and performance of the developed control method.

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“…The hydraulic actuators commonly were employed in active vibration control of vehicle active suspension and helicopter cabin (Behrouz et al, 2020). For example, they were installed between the fuselage and main gearbox for active vibration control of the EH-101 and S-76B helicopters.…”

Section: Introductionmentioning

confidence: 99%

Active vibration control of heavy platform-struts structure

Yang

Liu³

et al. 2022

Journal of Vibration and Control

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To tackle the active control problem of the heavy platform-struts structure, this paper designs a kind of hydraulic servo actuator that can withstand large static load and generate high-precision, small-amplitude dynamic forces as well, and develops a multi-channel cross-coupling suppression control algorithm that can consider the strong coupling effect in structure. Subsequently, combining the designed actuators with the control algorithm, this paper puts forward an active heavy platform-struts structure vibration control method. By embedding the designed actuators into the support struts, a platform-struts structure experimental system is constructed to assess the vibration suppression effect of the proposed method. Excited by the amplitude-varying disturbing force, four different kinds of experiments are conducted. The experimental results show that the distributed multi-channel adaptive active control method without considering the coupling effect will lead to the instability, and the vibration attenuation of the proposed method in this paper exceeds 90%. The proposed method therefore has an important potential in addressing the problem of vibration suppression in the practical engineering. In addition, it has strong convergence and robustness.

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Neural minimal learning backstepping control of stochastic active suspension systems with hydraulic actuator saturation

Cited by 19 publications

References 41 publications

Artificial Intelligence Methods in Hydraulic System Design

Artificial Intelligence Methods in Hydraulic System Design

Command filtered backstepping control of constrained flexible joint robotic manipulator

Active vibration control of heavy platform-struts structure

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