Hierarchical Multiobjective Heuristic for PCB Assembly Optimization in a Beam-Head Surface Mounter

Gao, Huijun; Li, Zhengkai; Yu, Xinghu; Qiu, Jianbin

doi:10.1109/tcyb.2020.3040788

Cited by 164 publications

(47 citation statements)

References 36 publications

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“…C2: Neural network based ARC (NNARC) algorithm: The neural network part selects the Gaussian function, and its center value and width are designed as c nj = [−0.21, −0.14, −0.07, 0, 0.07, 0.14, 0.21] 5×7 and b j = [10,10,10,10,10,10,10]. The learning rate matrix and robust gain coefficient are selected as = diag [5,5,5,5,5,5,5] and κ = 0.05. The adaptive rate in the ARC part is Γ = diag [10,0,200,30,0,0,80,50,50,50].…”

Section: B Parameter Settingmentioning

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 Parameter Settingmentioning

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

“…Step n: In this step, we would establish an event-triggered controller for the considered system (1). Owing to the existence of the nonsymmetric dead-zone nonlinearity, to facilitate later analysis, we first rewrite the dead-zone model described by (2) as…”

Section: Event-triggered Adaptive Neural Tracking Controller Designmentioning

confidence: 99%

“…Fractional-order calculus, generalizing definitions of integrals and derivatives from integer-order to real-order, is deemed as a general extension of integer-order calculus. With the fractional-order calculus introduced into engineering fields, fractional-order nonlinear systems provide an accurate description for mathematical modeling of various physical systems including fractional-order rub impact rotor systems, 1 fractional-order chaotic systems, 2,3 and so forth. Accordingly, system analysis and control synthesis of fractional-order nonlinear systems have captured increasing attention due to its potential applications, [4][5][6][7] and the adaptive backstepping technique as an essential method in nonlinear control theory has gradually been applied to solve control problems of such a type of nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Event‐triggered adaptive tracking control for uncertain fractional‐order nonstrict‐feedback nonlinear systems via command filtering

Wang

Zhao

et al. 2022

Intl J Robust & Nonlinear

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This article puts forward an event-triggered adaptive neural tracking control strategy for a class of uncertain fractional-order nonstrict-feedback nonlinear systems with full-state constraints and dead-zone inputs. The proposed control strategy is capable of surmounting an inherent shortcoming in most traditional backstepping control approaches which results from the repeated differentiation of virtual control functions, and enhancing the transmission efficiency of control signals by introducing an event-triggered mechanism with a dynamic threshold condition. Furthermore, by constructing a novel barrier Lyapunov function in each step of the backstepping procedure, the design difficulty arising from the system state constraints issue under fractional-order cases is resolved. It is strictly proved that the tracking error can converge into a desired small neighborhood of the origin and all signals in the closed-loop system are bounded. Finally, a simulation example is given to verify the effectiveness of our proposed control strategy.

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“…On the other hand, the unconstrained states of systems may affect stability of systems, even cause the safety problem in practical engineering, such as power systems, 26 motors-driven systems, 27 and beam-head surface mounters. 28 Therefore, the state-constrained issue is an important topic in control theories and engineering. Recently, many effective results have been proposed to solve the state-constrained problem.…”

Section: Introductionmentioning

confidence: 99%

Optimized adaptive event‐triggered tracking control for multi‐agent systems with full‐state constraints

Yang

Pan

Sun

et al. 2022

Intl J Robust & Nonlinear

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In this article, the event‐triggered optimized adaptive tracking control problem is investigated for a class of multi‐agent systems subject to full‐state constraints. To address the full‐state constraints problem, a nonlinear mapping technique is applied, which can release the feasibility conditions for virtual controllers. Based on the mean‐value theorem, the nonaffine nonlinear terms generated by transformed system are separated, which overcomes the obstacle of solving optimized solution. The neural network based reinforcement learning (RL) algorithm with the identifier‐critic‐actor architecture is introduced to obtain the optimal solution of systems with unknown dynamics. It is worth noting that the RL algorithm in this article is simplified, which can reduce the computational burden. To reducing the communication burden, an event‐triggered mechanism with time‐varying threshold related to the optimized control signal is developed. By applying the Lyapunov stability method, it is proved that the desired optimized tracking performance and the stability of the closed‐loop systems can be guaranteed. Finally, a simulation example demonstrates that the proposed control strategy is effective.

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Hierarchical Multiobjective Heuristic for PCB Assembly Optimization in a Beam-Head Surface Mounter

Cited by 164 publications

References 36 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

Event‐triggered adaptive tracking control for uncertain fractional‐order nonstrict‐feedback nonlinear systems via command filtering

Optimized adaptive event‐triggered tracking control for multi‐agent systems with full‐state constraints

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