RBF Neural Network Based Backstepping Control for an Electrohydraulic Elastic Manipulator

Tran, Duc Thien; Nguyen, Minh-Nhat; Ahn, Kyoung Kwan

doi:10.3390/app9112237

Cited by 11 publications

(3 citation statements)

References 28 publications

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“…In control systems, neurons with transfer functions of the RBF type are often used. Tran in [30] presented an adaptive robust control system for the equilibrium position of an electrohydraulic elastic manipulator under the presence of variable stiffness. The proposed control strategy (Figure 9) includes sliding mode controls (SMCs), radial basis function neural network (RBFNN), and a backstepping technique.…”

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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“…A virtual angle-based control method was used to design a single-loop control system to achieve trajectory tracking and balancing of the robot. Unlike in [14], the radial basis function networks (RBFNs) [18], a kind of ANN, were used to compensate the uncertainties without knowing their upper bounds, and the dynamic surface control (DSC) method [19] was employed to design the controller. From the Lyapunov stability theory, it was proven that all errors of the closed-loop control system were uniformly ultimately bounded.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Control for Trajectory Tracking and Balancing of a Ball-Balancing Robot with Uncertainty

2021

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In this paper, a neural-network-based control method to achieve trajectory tracking and balancing of a ball-balancing robot with uncertainty is presented. Because the ball-balancing robot is an underactuated system and has nonlinear couplings in the dynamic model, it is challenging to design a controller for trajectory tracking and balancing. Thus, various approaches have been proposed to solve these problems. However, there are still problems such as the complex control system and instability. Therefore, the objective of this paper was to propose a solution to these problems. To this end, we developed a virtual angle-based control scheme. Because the virtual angle was used as the reference angle to achieve trajectory tracking while keeping the balance of the ball-balancing robot, we could solve the underactuation problem using a single-loop controller. The radial basis function networks (RBFNs) were employed to compensate uncertainties, and the controller was designed using the dynamic surface control (DSC) method. From the Lyapunov stability theory, it was proven that all errors of the closed-loop control system were uniformly ultimately bounded. Therefore, the control system structure was simple and ensured stability in achieving simultaneous trajectory tracking and balancing of the ball-balancing robot with uncertainty. Finally, the simulation results are given to verify the performance of the proposed controller through comparison results. As a result, the proposed method showed a 19.2% improved tracking error rate compared to the existing method.

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“…Advanced nonlinear control methods are required to adapt to the rapid technical developments of several industrial fields. As a representative and outstanding nonlinear control method, the backstepping control (BSC) has been developed [1,2] and applied to various industrial systems by adopting a recursive controller design technique with a combination of a fuzzy logic system (FLS) [3] and neural networks (NNs) [4,5]. However, BSC is known to have a fatal issue that relates to the coupling order of the controller due to repeated differentiation of the virtual controls, which causes the phenomenon of "exploration of complexity".…”

Section: Introductionmentioning

confidence: 99%

Grey Wolf and Weighted Whale Algorithm Optimized IT2 Fuzzy Sliding Mode Backstepping Control with Fractional-Order Command Filter for a Nonlinear Dynamic System

Han

2021

Applied Sciences

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In this study, a fractional-order sliding mode backstepping control method was proposed, which involved the use of a fractional-order command filter, an interval type-2 fuzzy logic system approximation method, and a grey wolf and weighted whale optimization algorithm for multi-input multi-output nonlinear dynamic systems. For designing the stabilizing controls of the backstepping control, a novel fractional-order sliding mode surface was suggested. Further, the transformed errors that occurred during the recursive design steps were easily compensated by the controllers constructed using a new fractional-order command filter. Thus, the differentiation issue of the virtual control in the conventional backstepping control design could be bypassed with a simpler controller structure. Subsequently, the unknown plant dynamics were approximated by an interval type-2 fuzzy logic system. The uncertainties, such as the approximation error and the external disturbance, were compensated by the fractional-order sliding mode control that was added in the backstepping controller. Furthermore, the controller parameters and the fuzzy logic system were optimized via a grey wolf and weighted whale optimization algorithm to obtain a faster tuning process and an improved control performance. Simulation results demonstrated that the fractional-order sliding mode backstepping control scheme provides enhanced control performance over the conventional backstepping control system. Thus, in this paper, a fractional-order sliding mode surface and fractional-order backstepping control are studied, which provide more rapid convergence and enhanced robustness. Furthermore, a hybrid grey wolf and weighted whale optimization algorithm are proposed to provide an improved learning performance than those of conventional grey wolf optimization and weighted whale optimization methods.

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RBF Neural Network Based Backstepping Control for an Electrohydraulic Elastic Manipulator

Cited by 11 publications

References 28 publications

Artificial Intelligence Methods in Hydraulic System Design

Artificial Intelligence Methods in Hydraulic System Design

Neural Network Control for Trajectory Tracking and Balancing of a Ball-Balancing Robot with Uncertainty

Grey Wolf and Weighted Whale Algorithm Optimized IT2 Fuzzy Sliding Mode Backstepping Control with Fractional-Order Command Filter for a Nonlinear Dynamic System

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