Article
Automation & Control Systems
Zhen-Guo Liu, Hongli Dong, Weixing Chen, Weidong Zhang
Summary: This article investigates the adaptive regulation problem of uncertain delayed nonlinear systems and presents two unified adaptive control methods to achieve global asymptotic stability by introducing dynamic gain transformation and using homogeneous domination method.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2022)
Article
Automation & Control Systems
Xianglei Jia, Shengyuan Xu, Shaosheng Zhou
Summary: This technical note considers the problem of global asymptotical state regulation for a class of nonlinear systems with unknown measurement sensitivity and polynomial growth constraint. A new dynamic output feedback controller with dual nonidentification adaptive gains is proposed by solving a pair of matrix inequalities including an unknown time-varying parameter. The approach reduces the conservatism of existing results by allowing the unknown measurement sensitivity to be nondifferentiable and relaxing the growth rate of nonlinearities in the presence of a large measurement error.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Automation & Control Systems
Xin Yu, Wei Zhao, Jianwei Xia, Xiangyong Chen, Hao Shen
Summary: This paper investigates the fixed-time tracking control issue for a class of high-order nonlinear delayed systems subject to mismatched disturbances. The fuzzy logic system is applied to handle the nonlinear functions and the power integrator technique is introduced to address the high-order terms. Different from conventional methods, the adaptive backstepping method is utilized in this paper to cope with the delayed terms. The main objective is to design a suitable adaptive fuzzy fixed-time controller to ensure stability and boundedness of the closed-loop system in a fixed-time interval.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2023)
Article
Automation & Control Systems
Sang-Young Oh, Ho-Lim Choi
Summary: This article introduces a newly designed observer-based output feedback controller with three gain-scaling factors to address more complex nonlinearity structure and larger growth rate of nonlinearity. By utilizing three gain-scaling factors, there is more flexibility in selecting control parameters to improve system stability and convergence rate of observer errors. The proposed control scheme demonstrates improved features compared to existing schemes through numerical and practical examples.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2021)
Article
Automation & Control Systems
Wenshun Lv, Junwei Lu, Yongmin Li, Yuming Chu, Shengyuan Xu
Summary: In this paper, an adaptive neural control scheme is proposed for a class of unknown nonlinear systems with unknown sensor hysteresis. The scheme utilizes radial basis function neural networks to approximate the unknown nonlinearities and implements the backstepping technique to construct controllers. The control design is challenging due to the unavailability of genuine system states caused by sensor hysteresis. The proposed control scheme ensures practical finite-time stability of the closed-loop system, as proved by the Lyapunov theory. A numerical simulation example is provided to validate the effectiveness of the approach.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2022)
Article
Automation & Control Systems
Junyong Zhai, Ce Liu
Summary: This article discusses global dynamic output feedback stabilization for a class of high-order nonlinear systems. An output feedback controller is designed using power integrator technique, and dynamic gains are introduced to achieve global stabilization. The proposed method can be applied to a family of high-order upper-triangular nonlinear systems, with examples provided to demonstrate its effectiveness.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2022)
Article
Engineering, Mechanical
Yingying Shen, Junyong Zhai
Summary: The article introduces a dynamic output feedback controller for high-order nonlinear systems with uncertain output function. The controller first globally stabilizes the nominal system and then addresses the nonlinear terms by introducing a well-designed gain. This control scheme can be applied to stabilize a family of high-order nonlinear upper-triangular systems, as demonstrated by three examples.
NONLINEAR DYNAMICS
(2021)
Article
Automation & Control Systems
Chenghui Zhang, Le Chang, Lantao Xing, Xianfu Zhang
Summary: This paper proposes a novel fixed-time stabilization control method for a class of strict-feedback nonlinear systems involving unmodelled system dynamics. The key feature lies in the design of two dynamic parameters, which are delicately designed to ensure that all closed-loop system states are globally fixed-time stable. This method avoids the complexity explosion problem of backstepping control and remains valid beyond the given fixed-time convergence instant.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2023)
Article
Automation & Control Systems
Hanfeng Li, Xianfu Zhang, Shuai Liu
Summary: This article proposes an improved dynamic gain method for studying the global regulation problem of feedforward nonlinear systems. The method improves the convergence speed of the system state by designing a controller with a dynamic gain using a new time-varying function. The advantages of the improved dynamic gain method are illustrated through a simulation example.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Automation & Control Systems
Manman Yuan, Junyong Zhai
Summary: This paper discusses the problem of event-triggered output feedback control for a class of switched stochastic nonlinear systems with unknown output gain. By introducing a dynamic gain into the system, a controller is designed under event-triggering mechanism. It can be shown that all signals of the closed-loop system are bounded in probability. In addition, the Zeno behavior is excluded. An example is provided to verify the effectiveness of the proposed method.
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2022)
Article
Automation & Control Systems
Liang Liu, Jiaming Lu, Mengru Kong
Summary: This paper discusses exponentially stable problem for a class of stochastic strict feedforward nonlinear systems, presenting a parameter-dependent controller to handle nonlinearities. Through coordinate transformation and parameter selection, the proposed controller ensures stability of the closed-loop system as demonstrated by stochastic Lyapunov stability theory. Simulation results validate the efficiency of the proposed controller.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2021)
Article
Computer Science, Information Systems
Qianghui Zhou, Xiang Xu, Lu Liu, Gang Feng
Summary: This paper proposes an output feedback stabilization method for linear systems with infinite distributed delays in their inputs and outputs, and proves the global asymptotic stability of the closed-loop system. The study investigates the output feedback control problem of linear systems with infinite distributed input and output delays for the first time. Numerical examples demonstrate the effectiveness of the proposed controller.
INFORMATION SCIENCES
(2021)
Article
Automation & Control Systems
Xinyi Lu, Fang Wang, Zhi Liu, L. Philip Chen
Summary: The objective of this article is to propose an adaptive neural inverse optimal consensus tracking control for nonlinear multi-agent systems (MASs) with unmeasurable states. A new observer is created to approximate the unknown state, which includes the outputs of other agents and their estimated information. The proposed scheme not only insures that all signals of the system are cooperatively semiglobally uniformly ultimately bounded (CSUUB), but also realizes optimal control of all signals.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2023)
Article
Automation & Control Systems
Yafeng Li, Ju H. Park, Changchun Hua, Guopin Liu
Summary: This paper addresses the distributed adaptive containment control problem for uncertain nonlinear multiagent systems with time delays and unmodeled dynamics. By introducing a local reference generator and nonlinear function approximation method, a linear-like distributed adaptive output feedback controller is designed to simplify the controller design. Simulation results illustrate the effectiveness of the proposed method.
Article
Automation & Control Systems
Ce Liu, Junyong Zhai
Summary: This paper focuses on the design of an adaptive output feedback controller for a class of high-order stochastic nonlinear systems with uncertain output function. The proposed controller design includes a homogeneous observer and output feedback controller for the nominal system, and utilizes a dynamic gain technique to guarantee the convergence of system states and boundedness of the dynamic gain in probability. The design scheme can also be extended to upper-triangular systems.
