Shuai Li
Lanzhou University
Stability (learning theory)AlgorithmMathematical optimizationControl engineeringMatrix (mathematics)Recurrent neural networkArtificial intelligenceNonlinear systemControl (management)MathematicsKinematicsComputer scienceArtificial neural networkControl theoryConvergence (routing)Control theoryMotion planningQuadratic programmingRobotRobustness (computer science)
375Publications
53H-index
7,253Citations
Publications 380
Newest
#1Vasilios N. Katsikis (UoA: National and Kapodistrian University of Athens)H-Index: 16
#2Spyridon D. Mourtas (UoA: National and Kapodistrian University of Athens)H-Index: 3
Last. Xinwei Cao (SHU: Shanghai University)H-Index: 6
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Abstract null null It is widely acclaimed that the Markowitz mean–variance portfolio selection is a very important investment strategy. One approach to solving the static mean–variance portfolio selection (MVPS) problem is based on the usage of quadratic programming (QP) methods. In this article, we define and study the time-varying mean–variance portfolio selection (TV-MVPS) problem both in the cases of a fixed target portfolio’s expected return and for all possible portfolio’s expected returns...
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#1Ameer Tamoor Khan (PolyU: Hong Kong Polytechnic University)H-Index: 3
#2Abdul Rehman Khan (PIEAS: Pakistan Institute of Engineering and Applied Sciences)H-Index: 2
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Abstract null null The Photovoltaic generation inherits the instability due to the variability and non-availability of solar irradiation at times. Such unstable generation will cause grid management, planning, and operation issues. Researchers have proposed several classical and advanced algorithms to forecast the power generation of photovoltaic plants to avoid such unsuitability issues. Artificial Neural Networks advancement has pushed them in power forecasting, ranging from yearly to hourly p...
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#1Hongmin Wu (CAS: Chinese Academy of Sciences)
#2Wu Yan (CAS: Chinese Academy of Sciences)
Last. Xuefeng Zhou (CAS: Chinese Academy of Sciences)H-Index: 11
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Abstract null null Robots are prone to making anomalies when performing manipulation tasks in unstructured environments, it is often desirable to rapidly adapt the robotic behavior to avoid environmental changes by learning from experts’ demonstrations. We propose a framework for learning robot anomaly recovery skills from time-driven demonstrations based on a Gaussian process regression with prior mean derived by Gaussian mixture regression, named as mean-prior GPR (MP-GPR), which allows an end...
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#1Yanmei WangH-Index: 9
#2Shuai Li (CAS: Chinese Academy of Sciences)H-Index: 54
Last. Song ZhangH-Index: 18
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#1Dechao Chen (Hangzhou Dianzi University)H-Index: 18
#2Xinwei Cao (SHU: Shanghai University)H-Index: 6
Last. Shuai Li (Swansea University)H-Index: 53
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Abstract null null Because of the strong dynamic behavior and computing power, zeroing neural networks (ZNNs) have been dee different time-dependent issues. However, due to the high nonlinearity and complexity, the research on finding a feasible ZNN to address time-dependent nonlinear optimization with multiple types of constraints still remains stagnant. To simultaneously handle multiple types of constraints for the time-dependent nonlinear optimization, this paper proposes a novel neural-netwo...
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#3Shuai Li (Lanzhou University)H-Index: 53
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#3Zhihao Xu (CAS: Chinese Academy of Sciences)H-Index: 7
Abstract null null SCARA robot is one of the most popularly used robots in industry. The obstacle avoidance feature of multiple SCARA robot collaboration is essential and prominent, which can be used to support multiple robots to accomplish not only more sophisticated tasks but also more efficient than individual robot. This paper mainly focuses on studying the problem of simultaneous multi-robot coordination and obstacle avoidance. A cooperative kinematic control problem of multiple robot manip...
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#1Yang Shi (YZU: Yangzhou University)H-Index: 6
#2Wenhan Zhao (YZU: Yangzhou University)
Last. Xiaobing Sun (YZU: Yangzhou University)H-Index: 18
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Controlling and processing of time-variant problem is universal in the fields of engineering and science, and the discrete-time recurrent neural network (RNN) model has been proven as an effective method for handling a variety of discrete time-variant problems. However, such model usually originates from the discretization research of continuous time-variant problem, and there is little research on the direct discretization method. To address the aforementioned problem, this article introduces a...
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#1Yufeng ZhangH-Index: 3
#2Shuai LiH-Index: 53
Last. Damiano Zanotto (Stevens Institute of Technology)H-Index: 17
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Assist-as-needed (AAN) control aims at promoting therapeutic outcomes in robot-assisted rehabilitation by encouraging patients' active participation. Impedance control is used by most AAN controllers to create a compliant force field around a target motion to ensure tracking accuracy while allowing moderate kinematic errors. However, since the parameters governing the shape of the force field are often tuned manually or adapted online based on simplistic assumptions about subjects' learning abil...
#1Huiyan LuH-Index: 4
#2Long JinH-Index: 29
Last. Zhijun Zhang (SCUT: South China University of Technology)H-Index: 68
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Joint-drift problems could result in failures in executing task or even damage robots in actual applications and different schemes have been presented to deal with such a knotty problem. However, in these existing schemes, there exists the coupling in coefficients for eliminating the drift in the joint space and the equality constraint for completing the given task in the Cartesian space, thereby, theoretically, leading to a paradox in achieving zero joint drift in the joint space and zero posit...
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