Task-Space Measurement and Control for a 6DOF Stewart Nanoscale Platform

Ting, Yung; Li, Chun-Chung; Jar, Ho-Chin; Yuan, Kun

doi:10.1109/icma.2007.4303536

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…Stewart platforms can be found in different domains, such as machinery [1,6], test beds [7], realtime simulators for vehicles [2,8], and antenna/solar orientation platforms [9,10]. Typically, every arm is controlled by an electro mechanical actuator (EMA), but hydraulic actuators are more suitable for large loads [2,11], and piezoelectric actuators are commonly used in micro-or nano-scale platforms (found in biomedical science, optics, and microscopical devices) [12]. Stewart platforms are frequently used as camera levelling bases in a wide range of applications.…”

Section: Introductionmentioning

confidence: 99%

Experimental Validation of a Sliding Mode Control for a Stewart Platform Used in Aerospace Inspection Applications

et al. 2020

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The authors introduce a new controller, aimed at industrial domains, that improves the performance and accuracy of positioning systems based on Stewart platforms. More specifically, this paper presents, and validates experimentally, a sliding mode control for precisely positioning a Stewart platform used as a mobile platform in non-destructive inspection (NDI) applications. The NDI application involves exploring the specimen surface of aeronautical coupons at different heights. In order to avoid defocusing and blurred images, the platform must be positioned accurately to keep a uniform distance between the camera and the surface of the specimen. This operation requires the coordinated control of the six electro mechanic actuators (EMAs). The platform trajectory and the EMA lengths can be calculated by means of the forward and inverse kinematics of the Stewart platform. Typically, a proportional integral (PI) control approach is used for this purpose but unfortunately this control scheme is unable to position the platform accurately enough. For this reason, a sliding mode control (SMC) strategy is proposed. The SMC requires: (1) a priori knowledge of the bounds on system uncertainties, and (2) the analysis of the system stability in order to ensure that the strategy executes adequately. The results of this work show a higher performance of the SMC when compared with the PI control strategy: the average absolute error is reduced from 3.45 mm in PI to 0.78 mm in the SMC. Additionally, the duty cycle analysis shows that although PI control demands a smoother actuator response, the power consumption is similar.

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