Sliding Mode and Continuous Estimation Techniques for the Realization of Advanced Control Strategies for Parallel Kinematics

Flottmeier, Sarah; Olma, Simon; Trächtler, Ansgar

doi:10.3182/20140824-6-za-1003.01178

Cited by 6 publications

(1 citation statement)

References 14 publications

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“…However, in conventional SMC design, a priori knowledge of the bounds on system uncertainties must be acquired [23][24][25]. Several SMC-based strategies to control Stewart platforms are proposed and verified by simulations: SMC with perturbation estimation [26], integral SMC [5], continuous higher order SMC [27], and SMC with fuzzy tuning design [28]. The combination of SMC with estimation techniques, such as state observers [29], allows the conditioning of high frequency input signals [13], and estimating variables when not all states are measured directly, by using super-twisting algorithms [2] and adaptive super-twisting algorithms [10].…”

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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