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Renjie Ma

Publications and source records attributed to Renjie Ma.

4 recordsLinked to original sources

Measuring high-precision luminosity at the CEPC

Purpose: Luminosity measurement at the Circular Electron-Positron Collider (CEPC) is required to achieve 10^{-4} precision when operating at the center-of-mass energy of the Z-pole. Approximately 10^{12} Z-bosons will be collected to refine measurements of Standard Model processes. The design of the luminosity calorimeter (LumiCal) takes into account the geometry of the Machine-Detector-Interface (MDI) for detection of Bhabha events. The detector simulation with GEANT predicts measurements of scattered electrons, positrons, and radiation photons. Results: The luminosity measurement by counting Bhabha events depends on the accuracy of the lower acceptance angle (θ_{acc}) at the detector's fiducial edge. The beam-pipe design incorporates lowmass windows of 1 mm thick beryllium (Be) layers to reduce multiple scattering effects. The LumiCal has pixelated silicon detectors with better than 5 um resolution and LYSO crystals segmented into 3x3 mm2, which enhances the capability for detecting radiative Bhabha events. To achieve a precision level of 10^{-4}, it is crucial to monitor the interaction point (IP) of colliding beams and the positions of detectors with the error on mean of better than 1 urad. Conclusion: The LumiCal measures Bhabha scattering events using Si-detectors and finely segmented LYSO arrays. Its design is optimized for detecting radiative photons that are separated from electrons by a sufficiently large opening angle. This measurement aims to detect higher order corrections to the Bhabha interaction. Emphasis is placed on steering the beams for IP distribution and survey monitoring of detector positions to achieve high-precision luminosity measurements.lation with GEANT predicts measurements of scattered electrons, positrons, and radiation photons.

hep-ex

Adaptive event-triggered robust tracking control of soft robots

Soft robots manufactured with flexible materials can be highly compliant and adaptive to their surroundings, which facilitates their application in areas such as dexterous manipulation and environmental exploration. This paper aims at investigating the tracking control problem for soft robots under uncertainty such as unmodeled dynamics and external disturbance. First, we establish a novel switching function and design the compensated tracking error dynamics by virtue of the command filter. Then, based on the backstepping methodology, the virtual controllers and the adaptive logic estimating the supremum of uncertainty impacts are developed for synthesizing an event-triggered control strategy. In addition, the uniformed finite-time stability certification is derived for different scenarios of the switching function. Finally, we perform a case study of a soft robot to illustrate the effectiveness of the proposed control algorithm.

eess.SY

Aperiodic-sampled neural network controllers with closed-loop stability verifications (extended version)

In this paper, we synthesize two aperiodic-sampled deep neural network (DNN) control schemes, based on the closed-loop tracking stability guarantees. By means of the integral quadratic constraint coping with the input-output behaviour of system uncertainties/nonlinearities and the convex relaxations of nonlinear DNN activations leveraging their local sector-bounded attributes, we establish conditions to design the event- and self-triggered logics and to compute the ellipsoidal inner approximations of region of attraction, respectively. Finally, we perform a numerical example of an inverted pendulum to illustrate the effectiveness of the proposed aperiodic-sampled DNN control schemes.

eess.SY

Learning event-triggered controllers for linear parameter-varying systems from data

Nonlinear dynamical behaviours in engineering applications can be approximated by linear-parameter varying (LPV) representations, but obtaining precise model knowledge to develop a control algorithm is difficult in practice. In this paper, we develop the data-driven control strategies for event-triggered LPV systems with stability verifications. First, we provide the theoretical analysis of $θ$-persistence of excitation for LPV systems, which leads to the feasible data-based representations. Then, in terms of the available perturbed data, we derive the stability certificates for event-triggered LPV systems with the aid of Petersen's lemma in the sense of robust control, resulting in the computationally tractable semidefinite programmings, the feasible solutions of which yields the optimal gain schedulings. Besides, we generalize the data-driven eventtriggered LPV control methods to the scenario of reference trajectory tracking, and discuss the robust tracking stability accordingly. Finally, we verify the effectiveness of our theoretical derivations by numerical simulations.

eess.SY