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Jialei Bao

dblp:260/4693 · DBLP profile ↗
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4ranked-venue papers
2as first author
2since 2021 · last 2021
0000-0002-4936-5698ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 3 · 1 first-author · 1 since 2021Systems, architecture and hardware · 1 · 1 first-author · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Motion planning and robot control · 100%

Topics — the 1 heaviest of 1, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control › robot control
tracking control
0.512021
Command Filtered Tracking Control for High-order Systems with Limited Transmission Bandwidth · ICRA 2021

Methods — techniques the papers use, named apart from their topics

event-triggered control · 0.5command filter · 0.5backstepping · 0.5
YearPublicationVenuePosition
2021 Command Filtered Tracking Control for High-order Systems with Limited Transmission Bandwidth
abstract
This paper investigates the tracking control problem of a class of high-order distributed systems subjected to limited communication bandwidth. An event-triggered control method is proposed, where the controller is triggered only when specific events happen. Moreover, the computational complexity is reduced by introducing command filters for virtual control signals. Specifically, the backstepping scheme is adopted as the main design framework, by which the n-th order nonlinear system is divided into n command-cascaded first order subsystems. And virtual control commands are sent through a second-order low-pass filter, by which the time derivatives of the virtual commands can be obtained directly. The theoretical analysis shows the stability of the proposed method. The tracking performance is illustrated by a simulation example.
Jialei Bao, Peter Xiaoping Liu, Huanqing Wang 0001, Minhua Zheng
ICRA1
2021 Fuzzy Finite-Time Tracking Control for a Class of Nonaffine Nonlinear Systems With Unknown Dead Zones
abstract
This paper addresses the finite-time tracking control problem for a class of nonaffine nonlinear systems with unknown dead zones using an adaptive fuzzy control scheme. The unknown nonlinear functions of the system are approximated by the fuzzy logic systems and a finite-time stability theorem is used to construct the control signal. The novelty of the proposed control method is that the tracking system can reach the stable equilibrium within a finite period of time without the knowledge of the boundaries of the dead zone parameters. The simulation results show that the system is semi-global practical finite-time stable and the tracking deviation converges to a small neighborhood of zero in a finite period of time.
Jialei Bao, Huanqing Wang 0001, Peter Xiaoping Liu
IEEE Trans. Syst. Man Cybern. Syst.1
2020 Neural-network-based tracking Control for a Class of time-delay nonlinear systems with unmodeled dynamics
Huanqing Wang 0001, Yuchun Zou, Peter Xiaoping Liu, Xudong Zhao 0001, Jialei Bao, Yucheng Zhou 0002
Neurocomputing5
2020 Adaptive Neural Output-Feedback Decentralized Control for Large-Scale Nonlinear Systems With Stochastic Disturbances
abstract
This paper addresses the problem of adaptive neural output-feedback decentralized control for a class of strongly interconnected nonlinear systems suffering stochastic disturbances. An state observer is designed to approximate the unmeasurable state signals. Using the approximation capability of radial basis function neural networks (NNs) and employing classic adaptive control strategy, an observer-based adaptive backstepping decentralized controller is developed. In the control design process, NNs are applied to model the uncertain nonlinear functions, and adaptive control and backstepping are combined to construct the controller. The developed control scheme can guarantee that all signals in the closed-loop systems are semiglobally uniformly ultimately bounded in fourth-moment. The simulation results demonstrate the effectiveness of the presented control scheme.
Huanqing Wang 0001, Peter Xiaoping Liu, Jialei Bao, Xue-Jun Xie, Shuai Li 0002
IEEE Trans. Neural Networks Learn. Syst.3