Q. J. Chen
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Q. J. Chen has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Yongqi Chen
College of Science and Technology, Ningbo University, Ningbo 315211, China
1 shared publication Z. P. Wang
College of Electronics and Information Engineering, Tongji University, 4800 Cao'an Road, Shanghai 201804, China
1 shared publicationZ. P. Yuan
College of Electronics and Information Engineering, Tongji University, 4800 Cao'an Road, Shanghai 201804, China
1 shared publicationZhanxin Zhou
Department of Information and Control Engineering, Tongji University, Shanghai, China
1 shared publication
Latest Publications
- 2 November 2009
- Published by Wiley in Asian Journal of Control
- Vol. 11 (6) , 620-632
Stabilization of uncertain chained nonholonomic systems using adaptive output feedback
In this paper, adaptive output feedback control is presented to solve the stabilization problem of nonholonomic systems in chained form with strong nonlinear drifts and uncertain parameters using output signals only. The objective is to design adaptive nonlinear output feedback laws which can steer the closed‐loop systems to globally converge to the origin, while the estimated parameters remain bounded. The proposed systematic strategy combines input‐state scaling with backstepping technique. Motivated from a special case, adaptive output feedback controllers are proposed for a class of uncertain chained systems. The simulation results demonstrate the effectiveness of the proposed controllers. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society
- 2 November 2009
- Published by Wiley in Asian Journal of Control
- Vol. 11 (6) , 620-632
Stabilization of uncertain chained nonholonomic systems using adaptive output feedback
In this paper, adaptive output feedback control is presented to solve the stabilization problem of nonholonomic systems in chained form with strong nonlinear drifts and uncertain parameters using output signals only. The objective is to design adaptive nonlinear output feedback laws which can steer the closed‐loop systems to globally converge to the origin, while the estimated parameters remain bounded. The proposed systematic strategy combines input‐state scaling with backstepping technique. Motivated from a special case, adaptive output feedback controllers are proposed for a class of uncertain chained systems. The simulation results demonstrate the effectiveness of the proposed controllers. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society
- 1 August 2007
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- No. 21618151,p. 1115-1120
The Research and Application of LS_SVM Based on Particle Swarm Optimization
To select parameters is important in the research area of support vector machine. Based on particle swarm optimization, this paper proposes automatic parameters selection for least squares support vector machine (LSSVM). The effect of this proposed method is demonstrated by function regression problem. Besides, an equipment fault classification further illustrates that LSSVM based on particle swarm optimization has better classification ability than LSSVM based genetic algorithm under the same condition.
- 1 August 2007
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- No. 21618151,p. 1115-1120
The Research and Application of LS_SVM Based on Particle Swarm Optimization
To select parameters is important in the research area of support vector machine. Based on particle swarm optimization, this paper proposes automatic parameters selection for least squares support vector machine (LSSVM). The effect of this proposed method is demonstrated by function regression problem. Besides, an equipment fault classification further illustrates that LSSVM based on particle swarm optimization has better classification ability than LSSVM based genetic algorithm under the same condition.