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Dive into the research topics where Qingkai Yang is active.

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Featured researches published by Qingkai Yang.


Journal of Systems Engineering and Electronics | 2014

Distributed tracking for networked Euler-Lagrange systems without velocity measurements

Qingkai Yang; Hao Fang; Yutian Mao; Jie Huang

The problem of distributed coordinated tracking control for networked Euler-Lagrange systems without velocity measurements is investigated. Under the condition that only a portion of the followers have access to the leader, sliding mode estimators are developed to estimate the states of the dynamic leader in finite time. To cope with the absence of velocity measurements, the distributed observers which only use position information are designed. Based on the outputs of the estimators and observers, distributed tracking control laws are proposed such that all the followers with parameter uncertainties can track the dynamic leader under a directed graph containing a spanning tree. It is shown that the distributed observer-controller guarantees asymptotical stability of the closed-loop system. Numerical simulations are worked out to illustrate the effectiveness of the control laws.


IEEE Transactions on Automatic Control | 2017

Distributed Global Output-Feedback Control for a Class of Euler–Lagrange Systems

Qingkai Yang; Hao Fang; Jie Chen; Zhong Ping Jiang; Ming Cao

This paper investigates the distributed tracking control problem for a class of Euler–Lagrange multiagent systems when the agents can only measure the positions. In this case, the lack of the separation principle and the strong nonlinearity in unmeasurable states pose severe technical challenges to global output-feedback control design. To overcome these difficulties, a global nonsingular coordinate transformation matrix in the upper triangular form is first proposed such that the nonlinear dynamic model can be partially linearized with respect to the unmeasurable states. And, a new type of velocity observers is designed to estimate the unmeasurable velocities for each system. Then, based on the outputs of the velocity observers, we propose distributed control laws that enable the coordinated tracking control system to achieve uniform global exponential stability. Both theoretical analysis and numerical simulations are presented to validate the effectiveness of the proposed control scheme.


conference on decision and control | 2017

Modulus consensus in discrete-time signed networks and properties of special recurrent inequalities

Qingkai Yang; Ming Cao; Zhiyong Sun; Hao Fang; Jie Chen

Recently the dynamics of signed networks, where the ties among the agents can be both positive (attractive) or negative (repulsive) have attracted substantial attention of the research community. Examples of such networks are models of opinion dynamics over signed graphs. It has been shown that under mild connectivity assumptions these protocols provide the convergence of opinions in absolute value, whereas their signs may differ. This “modulus consensus” may correspond to the bipartite consensus (the opinions split into two clusters, converging to two opposite values) or the asymptotic stability of the system (the opinions always converge to zero). In this paper, we demonstrate that the phenomenon of modulus consensus in a signed network is a manifestation of a more general, regarding the solutions of special recurrent inequalities, associated to conventional first-order consensus algorithms. Although such a recurrent inequality does not provide the uniqueness of a solution, it can be shown that, under some natural assumptions, each of its bounded solutions has a limit and, moreover, converges to consensus. A similar property has previously been established for special continuous-time differential inequalities in [1]. Besides analysis of signed networks, we link the consensus properties of recurrent inequalities to the convergence properties of distributed optimization algorithms and stability properties of substochastic matrices.


IFAC Proceedings Volumes | 2014

Distributed Tracking for Multiple Lagrangian Systems Using Only Position Measurements.

Qingkai Yang; Fengyi Zhou; Jie Chen; Xin Li; Hao Fang

Abstract This paper investigates the distributed tracking control problem for multiple Lagrangian systems under a general directed graph where only a portion of the agents have access to the desired time-varying trajectory. To overcome the problem that only positions are measured, a observer is designed to estimate the velocity for each follower. By employing the estimated states, the distributed observer-based controller is proposed using only position measurements. Furthermore, the condition for the distributed tracking problem on the directed graph is derived, such that the tracking errors and observer errors semi-globally converge to zero. Finally, simulation examples are provided to show the effectiveness of the proposed control algorithms.


Systems & Control Letters | 2018

Distributed formation tracking using local coordinate systems

Qingkai Yang; Ming Cao; Hector Garcia de Marina; Hao Fang; Jie Chen

Abstract This paper studies the formation tracking problem for multi-agent systems, for which a distributed estimator–controller scheme is designed relying only on the agents’ local coordinate systems such that the centroid of the controlled formation tracks a given trajectory. By introducing a gradient descent term into the estimator, the explicit knowledge of the bound of the agents’ speed is not necessary in contrast to existing works, and each agent is able to compute the centroid of the whole formation in finite time. Then, based on the centroid estimation, a distributed control algorithm is proposed to render the formation tracking and stabilization errors to converge to zero, respectively. Finally, numerical simulations are carried to validate our proposed framework for solving the formation tracking problem.


conference on decision and control | 2016

Weighted centroid tracking control for multi-agent systems

Qingkai Yang; Ming Cao; Hao Fang; Jie Chen

This paper investigates the weighted centroid formation tracking control for multi-agent systems. First, a class of novel distributed observers is developed for each agent to infer the formations weighted centroid in finite time. Then, the distance-based control law is proposed based on the estimations, such that the weighted centroid of the formation is driven to track the assigned time-varying reference, meanwhile maintaining the prescribed formation shape. Moreover, the formation stabilization error is shown to converge to zero using the proposed observer-controller scheme utilizing the finite-time Lyapunov stability of the observers. Finally, all the theoretical results are further validated through numerical simulations.


conference on decision and control | 2015

Global output feedback control for multiple robotic manipulators

Qingkai Yang; Hao Fang; Jie Chen; Zhong Ping Jiang; Xiaodan Gu

This paper investigates the global output feedback tracking control problem for multiple robotic manipulators. Firstly, the global nonsingular coordinate transformation to obtain a partially linear system is provided based on the solution of a set of partial differential equations. Then, a new class of velocity observers is designed to estimate the unmeasurable velocities for each system. By employing the estimators, we propose distributed control laws such that the UGAS (uniform global asymptotic stability) is achieved. The theoretical results are further validated by numerical simulations.


Nonlinear Dynamics | 2015

Distributed backstepping-based adaptive fuzzy control of multiple high-order nonlinear dynamics

Jie Huang; Lihua Dou; Hao Fang; Jie Chen; Qingkai Yang


Iet Control Theory and Applications | 2014

Distributed observer-based coordination for multiple Lagrangian systems using only position measurements

Hao Fang; Qingkai Yang; Xueyuan Wang; Jie Chen


IFAC-PapersOnLine | 2016

Distributed trajectory tracking control for multiple nonholonomic mobile robots

Qingkai Yang; Hao Fang; Ming Cao; Jie Chen

Collaboration


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Hao Fang

Beijing Institute of Technology

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Jie Chen

Beijing Institute of Technology

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Ming Cao

University of Groningen

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Jie Huang

University of Groningen

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Lihua Dou

Beijing Institute of Technology

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Yutian Mao

Beijing Institute of Technology

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Jie Huang

University of Groningen

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Zhiyong Sun

Australian National University

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Chu Wu

Beijing Institute of Technology

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