Article
Computer Science, Artificial Intelligence
Hong Cheng, Xiucai Huang, Hongwei Cao
Summary: This paper proposes a method to achieve asymptotic tracking control for uncertain nonlinear strict-feedback systems with unknown time-varying delays and unknown control direction. The Lyapunov-Krasovskii functional is used to deal with the time delays, and the neural network is applied to compensate for the unknown terms arising from the derivative of the Lyapunov-Krasovskii functional. An NN-based adaptive control scheme is constructed based on backstepping technique, and the output tracking error is ensured to converge to zero asymptotically. The proposed method settles the singularity issue commonly encountered in coping with time delay problems and improves the transient performance with proper choice of design parameters.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Jing Wu, Wei Sun, Shun-Feng Su, Yuqiang Wu
Summary: This study introduces an adaptive quantized control scheme for uncertain strict-feedback nonlinear systems with unknown control directions. By combining backstepping technique and Lyapunov stability theory, a systematic analysis method is designed to overcome obstacles related to quantized input signals and unknown control directions. The effectiveness and feasibility of the control scheme are verified through simulation examples, demonstrating the boundedness of all signals and convergence of tracking error to a small domain of origin.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2021)
Article
Automation & Control Systems
Xiongfeng Deng, Chen Zhang, Yuan Ge
Summary: This paper investigates the tracking control problem of uncertain strict-feedback nonlinear systems with unknown control direction and unknown actuator fault. An adaptive neural network dynamic surface control law is designed using the neural network control approach and dynamic surface control technique.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2022)
Article
Automation & Control Systems
Wenrui Shi, Mingzhe Hou, Mingrui Hao
Summary: This paper proposes a synthesis of an asymptotic tracking controller for strict-feedback nonlinear systems with unknown control direction. The proposed design procedure combines dynamic surface control (DSC) technique, Nussbaum gain technique (NGT), and fuzzy logic systems (FLSs). The design overcomes the issues of 'differential explosion' and unknown control direction, and achieves asymptotic tracking.
Article
Computer Science, Artificial Intelligence
Yong Chen, Deqing Huang, Na Qin, Yanhui Zhang
Summary: In this article, an adaptive iterative learning control scheme is proposed for a class of nonlinear parametric strict-feedback systems with unknown state delays, achieving point-wise tracking of desired trajectory. The proposed approach compensates for the influence of time-delay uncertainties using appropriate Lyapunov-Krasovskii functions, and addresses the issues of differential explosion and singularity.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Information Systems
Byung Mo Kim, Sung Jin Yoo
Summary: In this paper, an adaptive quantized state feedback tracking methodology is proposed for a class of uncertain multiple-input multiple-output (MIMO) nonlinear block-triangular pure-feedback systems with state quantizers. The proposed strategy overcomes the nonaffine interaction problem of states and control variables in MIMO systems and derives bounded quantization errors using adaptive compensation terms, achieving the stability of quantized feedback.
Article
Automation & Control Systems
Ehsan Aslmostafa, Sehraneh Ghaemi, Mohammad Ali Badamchizadeh, Amir Rikhtehgar Ghiasi
Summary: This paper addresses the stability problem for a class of nonlinear systems in the form of strict-feedback with input quantization. It introduces a control scheme using a sector-bounded hysteresis quantizer to achieve signal quantization and reduce potential chattering. The control scheme stabilizes the uncertain nonlinear system using a common Lyapunov function and the backstepping method, without requiring global Lipschitz assumption and eliminating restrictions on quantization design parameters.
Article
Automation & Control Systems
Jie Kong, Ben Niu, Zhenhua Wang, Ping Zhao, Wenhai Qi
Summary: This paper investigates adaptive output-feedback neural tracking control for uncertain switched MIMO nonstrict-feedback nonlinear systems with time delays. Neural networks and hypothesis are used to handle unknown factors, and an adaptive neural controller is constructed for each subsystem. It is proven that all signals and system outputs are bounded under arbitrary switching.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2021)
Article
Engineering, Mechanical
Hang Su, Weihai Zhang
Summary: This paper investigates the adaptive neural control problem of a class of nonstrict-feedback nonlinear systems with input quantization, immeasurable states, and asymmetric fuzzy dead zones. A coordinate transformation is designed to convert the system into an equivalent form, followed by the establishment of a novel connection between system input and control signal. Using backstepping technique and the structural characteristic of neural networks, an adaptive neural output feedback control scheme is proposed, which ensures the boundedness of all signals in the closed-loop system. Simulation results are provided to demonstrate the effectiveness of the proposed methods.
NONLINEAR DYNAMICS
(2022)
Article
Automation & Control Systems
A. H. Tahoun, M. Arafa
Summary: This paper addresses the leader-follower tracking problem in multi-agent networks with unknown uncertainties. By designing distributed adaptive observers and controllers, the tracking path can be estimated and good tracking performance is achieved.
Article
Automation & Control Systems
Yun Ho Choi, Sung Jin Yoo
Summary: This study focuses on the distributed adaptive leader-following consensus of uncertain strict-feedback nonlinear multiagent systems with state quantizers, addressing the problem of distributed quantized state communication. The proposed approach involves deriving local adaptive control laws for each follower based on quantized-states and weight tuning laws.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Automation & Control Systems
Jeng-Tze Huang, Yu-Wei Jiang
Summary: This paper proposes a composite adaptive neural control method for uncertain strict-feedback systems, which improves stability and tracking performance through adaptive controllers, update algorithms, and state filters.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Engineering, Electrical & Electronic
Bojun Liu, Wencong Wang, Yankai Li, Yingmin Yi, Guo Xie
Summary: This brief proposes a novel adaptive quantized predefined-time control scheme for a class of uncertain nonlinear strict-feedback systems. The backstepping design utilizes composite state tracking errors and a composite estimation error, with the introduction of two time-varying tuning functions. It is demonstrated that the output tracking error can be steered to an arbitrarily small neighborhood of the origin within a user-predefined time, which serves as an exact design parameter. The provided upper bound of settling time is less conservative compared to previous studies. The effectiveness of the control strategy is shown through a simulation example.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2022)
Article
Automation & Control Systems
Jipeng Zhao, Xiaomei Li, Shaocheng Tong
Summary: The study focuses on fuzzy adaptive control for SISO uncertain nonlinear systems, using fuzzy state observer and fuzzy logic systems to estimate states and approximate functions, introducing dynamic surface control method and Logarithm Lyapunov functions to address complexity issues in control design.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2021)
Article
Automation & Control Systems
Yingxin Shou, Bin Xu, Huayan Pu, Jun Luo, Zhongke Shi
Summary: This article proposes a composite learning control approach with a heterogeneous estimator to address the multiple uncertainties in strict-feedback nonlinear systems. By using recorded data-based neural learning and disturbance observer, the approach learns the uncertainties including nonlinear dynamics, unknown control gain function, and time-varying disturbance. The lumped prediction error is constructed and incorporated into the update law through neural approximation and disturbance observation. The proposed approach ensures input limitation by representing the control input's asymmetric saturation nonlinearity with a smooth form model and utilizes a projection algorithm to avoid singularity problem. Rigorous stability analysis of the closed-loop system is conducted, guaranteeing the boundedness of the system tracking error. Tests on a third-order nonlinear system and an autonomous underwater vehicle (AUV) demonstrate that the proposed approach improves system tracking accuracy with expected learning performance.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Engineering, Mechanical
Yun Ho Choi, Sung Jin Yoo
NONLINEAR DYNAMICS
(2018)
Article
Automation & Control Systems
Yun Ho Choi, Sung Jin Yoo
Article
Engineering, Mechanical
Yun Ho Choi, Sung Jin Yoo
NONLINEAR DYNAMICS
(2019)
Article
Automation & Control Systems
Yun Ho Choi, Sung Jin Yoo
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2019)
Article
Engineering, Electrical & Electronic
Dong Min Jeong, Yun Ho Choi, Sung Jin Yoo
JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY
(2020)
Article
Automation & Control Systems
Yun Ho Choi, Sung Jin Yoo
Article
Mathematics
Yun Ho Choi, Sung Jin Yoo
Article
Mathematics
Yun Ho Choi, Sung Jin Yoo
Article
Robotics
Yun Ho Choi, Doik Kim
Summary: This research introduces a distance-based formation control algorithm with a novel goal assignment approach to prevent undesirable formations and achieve global asymptotic convergence. Simulation and experimental results demonstrate the effectiveness of the proposed algorithm.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2021)
Article
Automation & Control Systems
Yun Ho Choi, Sung Jin Yoo
Summary: This article introduces a filter-driven-approximation (FDA)-based design for distributed containment control of multi-agent systems, ensuring convergence of followers to the convex hull of leaders. Compared to other methods using adaptive neural network or fuzzy approximators, the proposed approach is simpler and more effective.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Yun Ho Choi, Sung Jin Yoo
Summary: This study proposes an adaptive asynchronous event-triggered design strategy based on a single neural network for the distributed consensus tracking of uncertain lower triangular nonlinear multi-agent systems. By using neighbors' triggered output information to estimate leader signals and designing local trackers through asynchronous event-triggered communication, this scheme effectively saves communication and computational resources.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Automation & Control Systems
Yun Ho Choi, Sung Jin Yoo
Summary: This study focuses on the distributed adaptive leader-following consensus of uncertain strict-feedback nonlinear multiagent systems with state quantizers, addressing the problem of distributed quantized state communication. The proposed approach involves deriving local adaptive control laws for each follower based on quantized-states and weight tuning laws.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Automation & Control Systems
Yun Ho Choi, Sung Jin Yoo
Summary: This paper presents a decentralized event-triggered tracking strategy based on minimal-function approximation (MFA), utilizing one funct ion approximator and one event-triggering condition for each subsystem to achieve local tracking laws. The total closed-loop stability is analyzed using impulsive system approach and Lyapunov stability theorem, while minimum interevent times for each subsystem are derived to prevent unexpected Zeno behavior.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Computer Science, Information Systems
Byung Mo Kim, Yun Ho Choi, Sung Jin Yoo
