RBF Neural Network-Based Adaptive Robust Synchronization Control of Dual Drive Gantry Stage With Rotational Coupling Dynamics

Shi, Pengwei; Sun, Weichao; Yang, Xuebo

doi:10.1109/tase.2022.3177540

Cited by 21 publications

(4 citation statements)

References 35 publications

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“…where B i and A i represent viscous friction and Coulomb friction coefficient, respectively. The ball support is squeezed and deformed when the crossbeam rotates, and the equivalent work generated at the guide rail can be expressed as [26]:…”

Section: B Dynamic Representationmentioning

confidence: 99%

Composite Adaptive Synchronous Control of Dual-Drive Gantry Stage With Load Movement

Shi

Yu²,

Yang

et al. 2023

IEEE Open J. Ind. Electron. Soc.

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In gantry systems, load usually moves according to different production tasks. The continuous change of the beam centroid position is a non-negligible factor for achieving high-performance synchronous control. Therefore, it cannot be simply treated as an external disturbance. In addition, advanced controller design and model parameter identification are very important for precision motion control systems. This paper addresses the synchronous control of dual-drive gantry platforms with load movement to improve their steady-state and transient performance. Based on the analysis of the rigid and flexible characteristics of gantry mechanical components, a rigid-flexible coupling dynamic model is proposed, which provides a theoretical basis for the control system design. To ensure zero steady-state tracking error and parameter convergence in the presence of parameter uncertainties and unknown disturbances, a composite adaptive control method integrating excellent output tracking performance and parameter estimation is presented. Furthermore, the bridge between load movement and centroid position is established through frequency domain identification, which facilitates reasonable thrust distribution. Comparative experimental results are presented that confirm the effectiveness and advantages of the proposed synchronous control scheme.INDEX TERMS Synchronization control, composite adaptive algorithm, system identification, load movement I. INTRODUCTION

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Section: B Dynamic Representationmentioning

confidence: 99%

Composite Adaptive Synchronous Control of Dual-Drive Gantry Stage With Load Movement

Shi

Yu²,

Yang

et al. 2023

IEEE Open J. Ind. Electron. Soc.

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show abstract

“…The cross-beam serves also as support to a third linear motor carrying a load [35][36][37][38]. There is need for precise synchronization between the two parallel motors and for minimization of internal forces [39][40][41][42]. When the two linear motors are not well synchronized the crossbeam will mis-align from the horizontal axis and will rotate by a small angle around its center of gravity [43][44][45].…”

Section: Introductionmentioning

confidence: 99%

Flatness-Based Control in Successive Loops of an H-Type Gantry Crane with Dual PMLSM

Rigatos,

Siano,

AL-Numay

et al. 2024

J. Vib. Eng. Technol.

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Gantry cranes of the H-type with dual electric-motor actuation are widely used in industry. In this article the control problem of an H-type gantry crane which is driven by a pair of linear permanent magnet synchronous motors is considered. The integrated system that comprises the H-type gantry crane and its two PMLSMs is shown to be differentially flat. The control problem for this robotic system is solved with the use of a flatness-based control approach which is implemented in successive loops. To apply flatness-based control in successive loops, the state-space model of the H-type gantry crane with dual PMLSM is separated into subsystems, which are connected in cascading loops. Each one of these subsystems can be viewed independently as a differentially flat system and control about it can be performed with inversion of its dynamics as in the case of input-output linearized flat systems. The state variables of the preceding (i-th) subsystem become virtual control inputs for the subsequent (i+1-th) subsystem. In turn, exogenous control inputs are applied to the last subsystem. The whole control method is implemented in successive loops and its global stability properties are also proven through Lyapunov stability analysis. The proposed method achieves stabilization of the H-type gantry crane with dual PMLSM without the need of diffeomorphisms and complicated state-space model transformations.

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“…Since the 1980s, the intelligent control theory has been developed greatly, and has rapidly moved to the application level. Neural network control as an important branch of intelligent control field, the same to the rapid development [9,10]. Taking the strict feedback triangle model of the third-order non-linear system with modulo feedback uncertainty as an example, the whole design process of the algorithm is expounded, and the stability of the system is proved by the Lyapunov method.…”

Section: Introductionmentioning

confidence: 99%

Dynamic surface adaptive RBF neural network control for a class of non‐linear uncertain systems

Zhang,

Wang,

Shan

et al. 2023

Electronics Letters

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An adaptive neural network dynamic surface control method for strict feedback non‐linear systems with model uncertainty is proposed in this paper. The uncertain parts are approached by RBF neural network; meanwhile, the virtual controller is designed to make system stable according to dynamic surface control. The method of Lyapunov is used to prove the stability and convergence of the system. The simulation results prove the feasibility of this controller and prove that the controller has an advantage to approach the uncertain non‐linear system and make system have well convergence and traceability.

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RBF Neural Network-Based Adaptive Robust Synchronization Control of Dual Drive Gantry Stage With Rotational Coupling Dynamics

Cited by 21 publications

References 35 publications

Composite Adaptive Synchronous Control of Dual-Drive Gantry Stage With Load Movement

Composite Adaptive Synchronous Control of Dual-Drive Gantry Stage With Load Movement

Flatness-Based Control in Successive Loops of an H-Type Gantry Crane with Dual PMLSM

Dynamic surface adaptive RBF neural network control for a class of non‐linear uncertain systems

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