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Chengzhi Yuan

Bio: Chengzhi Yuan is an academic researcher from University of Rhode Island. The author has contributed to research in topics: Control theory & Artificial neural network. The author has an hindex of 17, co-authored 109 publications receiving 1053 citations. Previous affiliations of Chengzhi Yuan include Foshan University & South China University of Technology.

Papers published on a yearly basis

Papers
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Journal ArticleDOI
TL;DR: In this paper, an adaptive neural tracking control of underactuated surface vessels with modeling uncertainties and time-varying external disturbances is presented, where the tracking errors consisting of position and orientation errors are required to keep inside their predefined feasible regions in which the controller singularity problem does not happen.
Abstract: This paper presents adaptive neural tracking control of underactuated surface vessels with modeling uncertainties and time-varying external disturbances, where the tracking errors consisting of position and orientation errors are required to keep inside their predefined feasible regions in which the controller singularity problem does not happen. To provide the preselected specifications on the transient and steady-state performances of the tracking errors, the boundary functions of the predefined regions are taken as exponentially decaying functions of time. The unknown external disturbances are estimated by disturbance observers and then are compensated in the feedforward control loop to improve the robustness against the disturbances. Based on the dynamic surface control technique, backstepping procedure, logarithmic barrier functions, and control Lyapunov synthesis, singularity-free controllers are presented to guarantee the satisfaction of predefined performance requirements. In addition to the nominal case when the accurate model of a marine vessel is known a priori , the modeling uncertainties in the form of unknown nonlinear functions are also discussed. Adaptive neural control with the compensations of modeling uncertainties and external disturbances is developed to achieve the boundedness of the signals in the closed-loop system with guaranteed transient and steady-state tracking performances. Simulation results show the performance of the vessel control systems.

199 citations

Journal ArticleDOI
TL;DR: A new concept of formation learning control is introduced to the field of formation control of multiple autonomous underwater vehicles (AUVs), which specifies a joint objective of distributed formation tracking control and learning/identification of nonlinear uncertain AUV dynamics.
Abstract: In this paper, a new concept of formation learning control is introduced to the field of formation control of multiple autonomous underwater vehicles (AUVs), which specifies a joint objective of distributed formation tracking control and learning/identification of nonlinear uncertain AUV dynamics. A novel two-layer distributed formation learning control scheme is proposed, which consists of an upper-layer distributed adaptive observer and a lower-layer decentralized deterministic learning controller. This new formation learning control scheme advances existing techniques in three important ways: 1) the multi-AUV system under consideration has heterogeneous nonlinear uncertain dynamics; 2) the formation learning control protocol can be designed and implemented by each local AUV agent in a fully distributed fashion without using any global information; and 3) in addition to the formation control performance, the distributed control protocol is also capable of accurately identifying the AUVs’ heterogeneous nonlinear uncertain dynamics and utilizing experiences to improve formation control performance. Extensive simulations have been conducted to demonstrate the effectiveness of the proposed results.

199 citations

Journal ArticleDOI
TL;DR: This hybrid control scheme provides an efficient and systematic way for designing average dwell time switched linear control systems in the sense that the boundary condition can be incorporated into the synthesis problem in a convex formulation.
Abstract: This technical note presents a hybrid control scheme for the output-feedback control of switched linear systems with average dwell time. The proposed hybrid controller consists of a standard switching output-feedback control law and a supervisor enforcing a reset rule for the switching controller states at each switching instant. This hybrid control scheme provides an efficient and systema tic way for designing average dwell time switched linear control systems in the sense that the boundary condition can be incorporated into the synthesis problem in a convex formulation. Specifically, bo th full-order and reduced-order controllers with guaranteed stability and optimal weighted H1 performance will be solved by linear matrix inequality (LMI) optimizations. Simulation studies are included to illustrate the effectiveness of the proposed approach. Index Terms—Switching control; average dwell time; controller state reset; full-order and reduced-order controllers; LMI.

136 citations

Journal ArticleDOI
TL;DR: A cooperative deterministic learning-based adaptive formation control algorithms are proposed for a group of mechanical systems with nonlinear uncertain dynamics under the virtual leader-following framework to enable position-swappable formation control.
Abstract: This paper addresses the formation control problem for a group of mechanical systems with nonlinear uncertain dynamics under the virtual leader-following framework. New cooperative deterministic learning-based adaptive formation control algorithms are proposed. Specifically, the virtual leader dynamics is constructed as a linear system subject to unknown bounded inputs, so as to produce more diverse reference signals for formation tracking control. A cooperative discontinuous nonlinear estimation protocol is first proposed to estimate the leader's state information. Based on this, a cooperative deterministic learning formation control protocol is developed using artificial neural networks, such that formation tracking control and locally-accurate nonlinear identification with learning knowledge consensus can be achieved simultaneously. Finally, by utilizing the learned knowledge represented by constant neural networks, an experience-based distributed control protocol is further proposed to enable position-swappable formation control. Numerical simulations using a group of autonomous underwater vehicles have been conducted to demonstrate the effectiveness and usefulness of the proposed results.

69 citations

Journal ArticleDOI
TL;DR: An experience-based distributed controller is proposed to improve the control performance and reduce the computational burden of an uncertain high-order nonlinear multiagent system with guaranteed transient performance and preserved initial connectivity under an undirected and static communication topology.
Abstract: For an uncertain multiagent system, distributed cooperative learning control exerting the learning capability of the control system in a cooperative way is one of the most important and challenging issues. This article aims to address this issue for an uncertain high-order nonlinear multiagent system with guaranteed transient performance and preserved initial connectivity under an undirected and static communication topology. The considered multiagent system has an identical structure and the uncertain agent dynamics are estimated by localized radial basis function (RBF) neural networks (NNs) in a cooperative way. The NN weight estimates are rigorously proven to converge to small neighborhoods of their common optimal values along the union of all agents’ trajectories by a deterministic learning theory. Consequently, the associated uncertain dynamics can be locally accurately identified and can be stored and represented by constant RBF networks. Using the stored knowledge on identified system dynamics, an experience-based distributed controller is proposed to improve the control performance and reduce the computational burden. The theoretical results are demonstrated on an application to the formation control of a group of unmanned surface vehicles.

55 citations


Cited by
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Book
03 Jan 1991

380 citations

Journal ArticleDOI
TL;DR: In this paper, the authors propose a concrete-concrete approach to the problem of concretization.Concrete-convex, concrete, and concrete-decrease.
Abstract: Concrete

330 citations

01 Aug 2009
TL;DR: PhysioBank是一个大型的逐渐扩增的生理学信号和相关数据的数字化记录文档;目前包含多参数的心肺。
Abstract: PhysioBank是一个大型的逐渐扩增的生理学信号和相关数据的数字化记录文档。目前包含多参数的心肺、神经和其他生物医学信号,尤以心电图(ECG)为主。信号来自健康受试者和各种疾病的患者。涉及的疾病包括心脏猝死、充血性心力衰竭、癫痫、步态不稳、睡眠呼吸暂停和衰老等。

287 citations

Journal ArticleDOI
TL;DR: A decentralized adaptive formation controller is designed that ensures uniformly ultimate boundedness of the closed-loop system with prescribed performance and avoids collision between each vehicle and its leader.
Abstract: This paper addresses a decentralized leader–follower formation control problem for a group of fully actuated unmanned surface vehicles with prescribed performance and collision avoidance. The vehicles are subject to time-varying external disturbances, and the vehicle dynamics include both parametric uncertainties and uncertain nonlinear functions. The control objective is to make each vehicle follow its reference trajectory and avoid collision between each vehicle and its leader. We consider prescribed performance constraints, including transient and steady-state performance constraints, on formation tracking errors. In the kinematic design, we introduce the dynamic surface control technique to avoid the use of vehicle's acceleration. To compensate for the uncertainties and disturbances, we apply an adaptive control technique to estimate the uncertain parameters including the upper bounds of the disturbances and present neural network approximators to estimate uncertain nonlinear dynamics. Consequently, we design a decentralized adaptive formation controller that ensures uniformly ultimate boundedness of the closed-loop system with prescribed performance and avoids collision between each vehicle and its leader. Simulation results illustrate the effectiveness of the decentralized formation controller.

273 citations

Journal ArticleDOI
TL;DR: An overview of recent advances in coordinated control of multiple ASVs is provided and several theoretical and technical issues are suggested to direct future investigations including network-based coordination, event-triggered coordination, collision-free coordination, optimization- based coordination, data-driven coordination of ASVs, and task-region-oriented coordination of multiple AsVs and autonomous underwater vehicles.
Abstract: Autonomous surface vehicles (ASVs) are marine vessels capable of performing various marine operations without a crew in a variety of cluttered and hostile water/ocean environments For complex missions, there are increasing needs for deploying a fleet of ASVs instead of a single one to complete difficult tasks Cooperative operations with a fleet of ASVs offer great advantages with enhanced capability and efficacy Despite various application potentials, coordinated motion control of ASVs pose great challenges due to the multiplicity of ASVs, complexity of intravehicle interactions and fleet formation with collision avoidance requirements, and scarcity of communication bandwidths in sea environments Coordinated control of multiple ASVs has received considerable attention in the last decade This article provides an overview of recent advances in coordinated control of multiple ASVs First, some challenging issues and scenarios in motion control of ASVs are presented Next, coordinated control architecture and methods of multiple ASVs are briefly discussed Then, recent results on trajectory-guided, path-guided, and target-guided coordinated control of multiple ASVs are reviewed in detail Finally, several theoretical and technical issues are suggested to direct future investigations including network-based coordination, event-triggered coordination, collision-free coordination, optimization-based coordination, data-driven coordination of ASVs, and task-region-oriented coordination of multiple ASVs and autonomous underwater vehicles

248 citations