RBF Neural Network Sliding Mode Control Method Based on Backstepping for an Electro-hydraulic Actuator

Li, Wending; Shi, Guanglin; Zhao, Chun; Liu, Hongyu; Fu, Junyong

doi:10.5545/sv-jme.2020.6866

Cited by 3 publications

(2 citation statements)

References 24 publications

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“…Numerous studies have explored sophisticated adaptive parameter tuning methods to enhance the performance of hydraulic control systems in the presence of uncertainty. Reference [7] describes a neural networkbased sliding mode control strategy for positioning control in the face of indeterminate disturbances. Reference [8] presents an active disturbance rejection control strategy that employs neural networks to overcome difficulties in tracking step signals in the presence of external perturbations.…”

Section: Introductionmentioning

confidence: 99%

Control Strategy of Hydraulic Servo Control Systems Based on the Integration of Soft Actor-Critic and Adaptive Robust Control

He,

Zhou,

et al. 2024

IEEE Access

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High-flow hydraulic servo systems are extensively employed in contemporary industrial applications due to their considerable flow capacity and cost-effectiveness. Nonetheless, hydraulic servo systems frequently encounter unpredictable internal and external disturbances, and high-flow proportional directional valves always have unsatisfactory hydraulic characteristics, compromising the precision and robustness of high-flow hydraulic servo systems. This study proposes a novel control strategy integrating the Soft Actor-Critic (SAC) reinforcement learning algorithm with Adaptive Robust Control (ARC) to enhance system performance. This approach features a two-tiered controller: the upper controller utilizes the SAC algorithm to learn and adapt to the dynamics of the hydraulic servo system, iteratively refining the lower controller's hyperparameters. Meanwhile, grounded in the ARC strategy, the lower controller executes realtime control of the hydraulic servo system. The simulation and experimental results demonstrate that the proposed control strategy can effectively adjust the control hyperparameters according to the learned system dynamic and tracking errors. Consequently, this approach enhances control precision amidst varying external and internal disturbances. Moreover, this control strategy is anticipated to realize high-flow, high-precision, and high-robust hydraulic servo systems, which can be used in various fields such as marine and offshore engineering.

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Section: Introductionmentioning

confidence: 99%

Control Strategy of Hydraulic Servo Control Systems Based on the Integration of Soft Actor-Critic and Adaptive Robust Control

He,

Zhou,

et al. 2024

IEEE Access

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“…However, it cannot solve this problem well for HTSs with complex nonlinearity. Instead, a combination of RBF neural network theory and adaptive control can solve this problem effectively [27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Maximum Power Point Tracking Control of Offshore Hydraulic Wind Turbine Based on Radial Basis Function Neural Network

Wang,

Du,

Chen

et al. 2024

Energies

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A maximum power point tracking control strategy for an affine nonlinear constant displacement pump-variable hydraulic motor actuation system with parameter uncertainty, used within an offshore hydraulic wind turbine, is studied in this paper. First, we used the feedback linearization method to solve the affine nonlinear problem in the system. However, offshore hydraulic wind turbines have strong parameter uncertainty characteristics. This conflict was resolved through the further application of RBF neural network adaptive control theory. So, we combined feedback linearization with RBF adaptive control as the control theory, and then two control laws were compared by setting the pump rate and rating as outputs, respectively. It is shown by the MATLABR2016a/Simulink emulation results that power control is smoother than speed and friendlier for electric networks. It is also shown by the emulation results, in terms of the undulatory wind speed condition, that the feedback linearization–RBF neural network adaptive control strategy has perfect robustness. According to the simulation results, the feedback linearization–RBF neural network adaptive control strategy adopts the RBF neural network to approach complex nonlinear models and solve the parameter uncertainty problem. This control law also avoids the use of feedback linearization control alone, which can result in the system becoming out of control.

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Cyber-Attack detection of Bias Injection Attacks using Extended State Kalman Filters

Altius,

Liang

2023

2023 International Automatic Control Conference (CACS)

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RBF Neural Network Sliding Mode Control Method Based on Backstepping for an Electro-hydraulic Actuator

Cited by 3 publications

References 24 publications

Control Strategy of Hydraulic Servo Control Systems Based on the Integration of Soft Actor-Critic and Adaptive Robust Control

Control Strategy of Hydraulic Servo Control Systems Based on the Integration of Soft Actor-Critic and Adaptive Robust Control

Maximum Power Point Tracking Control of Offshore Hydraulic Wind Turbine Based on Radial Basis Function Neural Network

Cyber-Attack detection of Bias Injection Attacks using Extended State Kalman Filters

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