Active magnetic bearing (AMB) system is a typical electromechanical system which requires a controller for stable levitation. Active disturbance rejection controller (ADRC) is considered as an effective way to stabilize a system under external disturbance. And a classical proportion-integration-differentiation (PID) controller is still a simple yet effective method for most engineering applications for its robustness. In this paper, ADRC and PID controller are implemented for the stable levitation of AMB system. By the mathematical analysis, the connecting relationships of control parameters between those two methods are researched. And in view of the unbalance effect while AMB-rotor is rotating, the output responses and control performance comparison are analyzed for the closed-loop system models of AMBs using ADRC and PID. The derivations reveal the advantages of ADRC than PID clearly, which means that ADRC is a great substitute for PID to suppress unbalance force. Finally, the simulations are conducted to verify the analytical conclusions of this paper. © 2021 Technical Committee on Control Theory, Chinese Association of Automation.

Topology and line parameter information is essential to the operation and control of electric grids. However, the gird topology may frequently change with the wide adoption of distributed energy resources, and the line parameters may be missing or inaccurate. The widespread deployment of advanced metering infrastructure enables the line parameters estimation and topology identification from a data-driven perspective. In this paper, we propose a reweighted ℓ1-minimization algorithm to deal with the joint topology and line parameter estimation problem by leveraging the sparsity of topology structure. A modified alternating descent sub-optimal method is proposed to estimate the admittance matrix of the power system using nodal voltage and current phasor measurements. Extensive simulations performed on IEEE 14 and 57-bus benchmark systems are presented to substantiate the effectiveness of the proposed algorithm. © 2021 Technical Committee on Control Theory, Chinese Association of Automation.

Though action recognition in videos has achieved great success recently, it remains a challenging task due to the massive computational cost. Designing lightweight networks is a possible solution, but it may degrade the recognition performance. In this paper, we innovatively propose a general dynamic inference idea to improve inference efficiency by leveraging the variation in the distinguishability of different videos. The dynamic inference approach can be achieved from aspects of the network depth and the number of input video frames, or even in a joint input-wise and network depth-wise manner. In a nutshell, we treat input frames and network depth of the computational graph as a 2-dimensional grid, and several checkpoints are placed on this grid in advance with a prediction module. The inference is carried out progressively on the grid by following some predefined route, whenever the inference process comes across a checkpoint, an early prediction can be made depending on whether the early stop criteria meets. For the proof-of-concept purpose, we instantiate several dynamic inference frameworks. In these instances, we overcome the drawback of limited temporal coverage resulted from an early prediction by a novel frame permutation scheme, and alleviate the conflict between progressive computation and video temporal relation modeling by introducing the online temporal shift module. Extensive experiments are conducted to thoroughly analyze the effectiveness of our ideas and to inspire future research efforts. Results on various datasets also evident the superiority of our approach. © 2020 IEEE.

The purpose of this paper is to evaluate the performance and computation time of Laguerre based model predictive control in the longitudinal motion of an aircraft. A Laguerre based model predictive algorithm is developed to control the longitudinal dynamics of an aircraft subject to state and input constraints. Then, the performance and the computation time of the designed controller are compared to the ones of the standard version of model predictive control. The results show that the Laguerre based model predictive control improves the computation time as much as 7.2% with a negligible trade-off in performance degradation. Because performing aircraft maneuvers needs to solve constrained optimization problems in short time samples, this study suggests the application of Laguerre based model predictive control for such purpose. Furthermore, by allowing to use shorter sampling times, the method could be used in real-time implementation of agile aircraft with fast dynamics. © 2020 EUCA.