Precision control of miniature SCARA robots for multi-object spectrographs

Kronig, Luzius; Hörler, Philipp; Caseiro, Stefane; Grossen, Loïc; Araújo, Ricardo; Kneib, Jean-Paul; Bouri, Mohamed

doi:10.1080/15599612.2020.1829218

Cited by 5 publications

(9 citation statements)

References 28 publications

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“…In different applications, due to the different control purposes, the controller's choice of performance indicators is often different [17,18]. Different control parameters are formulated for different performance indexes, which is of great significance in practical applications.…”

Section: Iste Optimization Parameter Tuning Methodmentioning

confidence: 99%

Iterative Learning-Based PID Precision Control for Sports Performance Analysis

Shen

et al. 2021

Wireless Communications and Mobile Computing

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Traditional function algorithms are contradictory to accuracy and performance. Therefore, taking into account the balance of accuracy and performance, the research of accuracy control-oriented mathematical function algorithms is of great significance to the design of high-precision and high-performance mathematical function algorithms. This paper is aimed at the design of mathematical function algorithm for precision control and has conducted indepth research on traditional PID control algorithm and fuzzy logic control theory. By analyzing the advantages and disadvantages of the two in practical applications, a parameter fuzzy cascade PID control is designed. The algorithm and its performance simulation and comparative analysis provide a theoretical basis for the follow-up accuracy control algorithm research and realization process. The accuracy control algorithm is then used to calculate and statistically analyze the sports performance including speed, strength (comprehensiveness and explosiveness), endurance, sensitivity, and coordination. The results show that the optimized function random point (nonextreme) test calculation accuracy is 99.5%, and the control accuracy improvement rate of the parameter fuzzy cascade PID control algorithm is about 18.24%. It has better control effect, stronger stability, and higher control accuracy. In the test of extreme points, the optimized test results are obviously better than those before optimization, which can effectively calculate sports results with high accuracy.

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Section: Iste Optimization Parameter Tuning Methodmentioning

confidence: 99%

Iterative Learning-Based PID Precision Control for Sports Performance Analysis

Shen

et al. 2021

Wireless Communications and Mobile Computing

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“…Here, different motors are used for theta-phi positioners. For example, the classic stepper model is adopted by the LAMOST and MOONS, whereas a brushless direct current (BLDC) motor is used by the SDSS-V and LAMOST second stage (Kronig et al 2020c;Guo et al 2022). The motors are controlled using an electronic board with pulse-width modulation through an open loop (LAMOST second stage; Guo et al 2022) or a closed loop (SDSS-V with Hall sensors; Kronig et al 2020b).…”

Section: Description and Distribution Of Theta-phi Positionersmentioning

confidence: 99%

“…The motors are controlled using an electronic board with pulse-width modulation through an open loop (LAMOST second stage; Guo et al 2022) or a closed loop (SDSS-V with Hall sensors; Kronig et al 2020b). One of the main challenges for the positioners is the tiny maximal allowable positioning error, which is telescopespecific and related to the fiber core diameter and size of the objects (Horler et al 2018;Kronig et al 2020c). Typically, the positioning requirement is proportional to the fiber core diameter (approximately 4% of the actual core diameter; Horler et al 2018).…”

Section: Description and Distribution Of Theta-phi Positionersmentioning

confidence: 99%

“…To calibrate the RFP of DESI, the positions of 16 calibration points per arc and that of a rectilinear grid of 24 calibration points are measured and the six geometric parameters are calibrated using LSM (Silber et al 2023). The LSM method is also adopted by SDSS-V to calibrate parameters such as arm length (Kronig et al 2020c). However, LSM has disadvantages, such as sensitivity to the initial values and calculation complexity.…”

Section: Introductionmentioning

confidence: 99%

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The Calibration of theta-phi Fiber Positioners Based on the Differential Evolution Algorithm

Zhang,

Huang,

Chen

et al. 2024

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Robotic fiber positioner (RFP) arrays are commonly adopted in multiobject spectroscopic instruments. The positioning accuracy is a common but vital issue for RFP as inaccurate fiber placement may heavily affect the observation performance. The calibration of RFP can effectively improve the positioning accuracy. Least-square is a widely used calibration method. However, it has disadvantages, such as sensitivity to the initial values and calculation complexity. To improve the positioning accuracy and reduce the iteration moves, we propose a new calibration method based on the differential evolution algorithm and verify it by calibrating the RFP of the Large Sky Area Multi-Object Fiber Spectroscopy Telescope. We first build the kinematic models of the RFP based on the Denavit–Hartenberg matrix and geometry relationship. Then, we analyze the error components and present the proposed calibration algorithms. The experiments are done with the digital universal tool microscope 19JC and the errors are calculated using the distance between the positions of achieved and target. Results show that the proposed algorithm can achieve higher accuracy than the least-square method and the average positioning accuracy is improved by 78.94% after calibration. Combined with the “pulse reduction” strategy and close-loop compensation, after two moves, the positioners can place the fiber ends within 40 μm of the intended location. The proposed calibration method is also suitable for other similar theta-phi positioners.

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“…Selective compliance articulated robot arms (SCARA) robots are widely used in industrial tasks as well as in the teaching of robotics and in the related testing of new robot control algorithms [1][2][3][4][5]. The rapid development of the Computer, Communication and Consumer Electronics Industry (3C industry) has led also to the spread of the use of SCARA robots [6][7][8][9][10]. SCARA robots exhibit agility in assembly tasks for the 3C industry and particularly in the fabrication of electronic devices, as well as in welding, handling of objects, and pick and place tasks with high speed, short time-cycle, accurate path following, and in general much flexible operation [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear optimal control for a 4-DOF SCARA robotic manipulator

et al. 2023

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Selective compliance articulated robot arms (SCARA) robotic manipulators find wide use in industry. A nonlinear optimal control approach is proposed for the dynamic model of the 4-degrees of freedom (DOF) SCARA robotic manipulator. The dynamic model of the SCARA robot undergoes approximate linearization around a temporary operating point that is recomputed at each time-step of the control method. The linearization relies on Taylor series expansion and on the associated Jacobian matrices. For the linearized state-space model of the system, a stabilizing optimal (H-infinity) feedback controller is designed. To compute the controller’s feedback gains, an algebraic Riccati equation is repetitively solved at each iteration of the control algorithm. The stability properties of the control method are proven through Lyapunov analysis. The proposed control method is advantageous because: (i) unlike the popular computed torque method for robotic manipulators, it is characterized by optimality and is also applicable when the number of control inputs is not equal to the robot’s number of DOFs and (ii) it achieves fast and accurate tracking of reference setpoints under minimal energy consumption by the robot’s actuators. The nonlinear optimal controller for the 4-DOF SCARA robot is finally compared against a flatness-based controller implemented in successive loops.

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Precision control of miniature SCARA robots for multi-object spectrographs

Cited by 5 publications

References 28 publications

Iterative Learning-Based PID Precision Control for Sports Performance Analysis

Iterative Learning-Based PID Precision Control for Sports Performance Analysis

The Calibration of theta-phi Fiber Positioners Based on the Differential Evolution Algorithm

Nonlinear optimal control for a 4-DOF SCARA robotic manipulator

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