Modeling and synchronous control of a single-axis stage driven by dual mechanically-coupled parallel ball screws

Hsieh, Min‐Fu; Yao, Wu-Sung; Chiang, Chia-Rong

doi:10.1007/s00170-007-1135-4

Cited by 45 publications

(32 citation statements)

References 7 publications

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“…The design of the controllers for the position loop and velocity loop in Sdomain is expressed as In position loop analysis, the velocity loop above can be treated as unity gain to reduce the order of the position loop to first-order system. 7,17,18 It is assumed that the bandwidth of the velocity loop is sufficiently larger than that of the position loop one. For a P control structure, the equivalent transfer function of position loop T P (s) becomes…”

Section: Adaptive Neuron Controller P/pi Controller and Stability Anamentioning

confidence: 99%

Adaptive neural network–based synchronized control of dual-axis linear actuators

Mao

Suprapto

Hung

2016

Advances in Mechanical Engineering

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Synchronized motion control with high accuracy becomes very essential part in industry. Due to some possible effect such as unknown disturbance or unmatched system model, it is difficult to obtain the precision of synchronous control using the conventional proportional-integral control method with parallel architecture. The adaptive compensator must be employed to eliminate tracking errors. The objective of this research is to propose the modified cross-coupling architecture using single-neuron proportional-integral controller and a synchronous compensator for dual-axis linear actuator. The single-neuron proportional-integral control strategy with delta learning algorithm can adjust the weighting coefficients of controllers to provide the robustness for each single-axis DC linear actuator system. A back-propagation neural network compensator is designed to adaptively reduce position and velocity errors between the two-axis servo systems. Both simulation and experimental results are developed to demonstrate that the synchronous position tracking performances in terms of root mean square error and sum of absolute error can be substantially improved, and the robustness to linear actuator uncertainties can be obtained as well. The proposed coupling strategy which uses the microchip platform and pulse-width modulation control technique is realized and implemented, and the synchronization performances to external disturbance load are illustrated by several experimental results.

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Section: Adaptive Neuron Controller P/pi Controller and Stability Anamentioning

confidence: 99%

Adaptive neural network–based synchronized control of dual-axis linear actuators

Mao

Suprapto

Hung

2016

Advances in Mechanical Engineering

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“…A motion stage is a linear motion of a table [5][6][7]. Figure 2 shows a model of a linear stage in which the table is driven by a linear servomotor.…”

Section: Modelingmentioning

confidence: 99%

A method to improve the accuracy of synchronous control systems

Hoang

Jeon

2013

2013 11th IEEE International Conference on Industrial Informatics (INDIN)

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This paper presents a method to improve the accuracy of synchronous control systems for dual linear stages driven by linear servomotors in order to quickly eliminate the velocity and position synchronous errors of the dual linear stages. Two velocity controllers and a synchronous controller are designed for the control system. To decrease the velocity tracking errors, two velocity controllers are designed to follow the velocity tracking commands which are also the desired velocity profiles specified by the given tasks and have optimized acceleration and deceleration characteristics. Moreover, the synchronous controller is designed to quickly and stably eliminate the position and velocity synchronous errors of the two dual linear stages by improving the damping characteristics of the synchronous control system. The experimental results are presented to demonstrate the effectiveness of the proposed synchronous control system.

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“…In fact, the performance of DDFS suffered from the diversity of mechanism characteristics, deflection of joint thrust, and mechanical coupling [9][10][11][12]. To improve the synchronous performance, some scholars established the mechanical coupling models by identification technique [13][14][15], yet the models are established as first-order inertia links, and the parameters are identified without actual physical meaning [16].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Characteristics Analysis and Test of Dual-Driving Feed System Driven by Center of Gravity

Lü

Fan

Zhang

et al. 2018

Mathematical Problems in Engineering

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Dual-driving feed system (DDFS) driven by center of gravity (DCG) has been widely used in advanced manufacturing machine for its high rigidity and precision. However, the DCG technology requires that the joint force coincides with the center of gravity of the sliding stage. The dual-driving synchronization and tracking performance will be affected by the change of center of gravity of the sliding stage. Therefore, this paper proposes dynamic characteristics modeling, identification, and control scheme for DDFS driven by center of gravity (DCG). Firstly, a redundancy dynamic model including rotation and pitch vibration caused by the change of the position of center of gravity is presented for DDFS DCG based on the Lagrange method. The model parameters are identified by system identification experiment, and the predictive natural frequencies and vibration modes by the proposed dynamic model are compared by modal experiment. Moreover, the dynamic model-based cross-coupled sliding mode control (CCSMC) is proposed for DDFS DCG. Then, the proposed dynamic model-based CCSMC has been compared with normal cross-coupled sliding mode control (NCCSMC). Both the simulation and experimental results show that the proposed dynamic characteristics analysis and test scheme of DDFS DCG are validated effectively by comparisons.

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Modeling and synchronous control of a single-axis stage driven by dual mechanically-coupled parallel ball screws

Cited by 45 publications

References 7 publications

Adaptive neural network–based synchronized control of dual-axis linear actuators

Adaptive neural network–based synchronized control of dual-axis linear actuators

A method to improve the accuracy of synchronous control systems

Dynamic Characteristics Analysis and Test of Dual-Driving Feed System Driven by Center of Gravity

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