Article
Engineering, Mechanical
Tingting Jiang, Yuping Zhang, Shouming Zhong, Jie Bao, Kaibo Shi, Xiao Cai
Summary: This work investigates the finite-time H-infinity predictive control problem for stochastic networked control systems (NCSs) with communication constraints. It proposes a nonfragile observer (NFO)-based networked predictive control (NPC) strategy to handle time delays and packet dropouts (TD-PDs) by considering unmeasurable state and missing measurements. It constructs a novel NPC system model that takes into account missing measurements and derives sufficient conditions for ensuring the stochastic finite-time boundedness (SFTB) and prescribed H-infinity performance of closed-loop systems (CLSs) with TD-PDs. The co-design criteria for the uniform NFO-based predictive controller and the NFO are calculated based on bounded TD-PDs. An illustrative example is provided to verify the usefulness of the main result.
NONLINEAR DYNAMICS
(2022)
Article
Engineering, Electrical & Electronic
Xiaofeng Yuan, Lu Feng, Kai Wang, Yalin Wang, Lingjian Ye
Summary: A novel deep learning strategy based on multirate stacked autoencoder (MR-SAE) is proposed for predicting both the 50% boiling point and cetane content of diesel oil. The MR-SAE-based model outperforms SAE and deep belief networks in terms of performance, while also having fewer parameters and shorter training time.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Automation & Control Systems
Fanghui Li, Zhongsheng Hou
Summary: This article addresses the event-triggered model-free adaptive predictive control problem for networked nonlinear control systems under deception attacks. By using dynamic linearization technology and considering attack phenomena, a networked MFAPC scheme is proposed to compensate for network delay and reduce calculation burden, with rigorous convergence analysis provided.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Review
Green & Sustainable Science & Technology
Mayank Kumar Gautam, Avadh Pati, Sunil Kumar Mishra, Bhargav Appasani, Ersan Kabalci, Nicu Bizon, Phatiphat Thounthong
Summary: Networked control systems (NCS) have attracted the attention of control system engineers due to the paradigm shift they have brought in control system technology. The evolution of NCS can be broadly divided into three phases: prior to 2000, during 2001-2010, and from 2011 onwards. Different control techniques and practical applications have been extensively discussed, and future research areas for NCS have been identified.
Article
Mathematics, Interdisciplinary Applications
Pang Zhonghua, Bai Chuandong, Liu Guoping, Han Qinglong, Zhang Xianming
Summary: This paper presents a novel observer-based predictive control method for networked systems, considering random delays and packet losses. The proposed method introduces the concept of destination-based lumped delay, designs different compensation schemes for feedback and forward channels, and uses actual control inputs to generate future control signals. The stability condition derived is less conservative and independent of random communication constraints in both channels. Simulation results demonstrate the effectiveness of the method.
JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY
(2021)
Article
Computer Science, Artificial Intelligence
Xiaobin Gao, Feiqi Deng, Pengyu Zeng, Hongyang Zhang
Summary: This article investigates the neural network-based event-triggered control problem in discrete-time networked Markov jump systems with hybrid cyberattacks and unmeasured states. The event-triggered mechanism and Luenberger observer are employed to reduce communication load and estimate unmeasured states. Different types of cyberattacks are considered, and the control methods are designed accordingly. The gains for the observer and controller are obtained by solving a set of matrix inequalities.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Xiao Lu, Yuanyu Cai, Hongxia Wang, Haixia Wang, Guilin Zhang, Xiao Liang
Summary: This paper addresses the optimal control problem for networked control systems with Markovian packet dropouts. While previous studies only consider one-way Markovian packet dropouts, this study aims to provide a complete solution for the two-way Markovian packet dropouts. By utilizing the Pontryagin's maximum principle and mathematical induction method, a solution to the forward and backward stochastic difference equations is derived. Furthermore, the necessary and sufficient condition for the optimal control problem is obtained, and the optimal controller is determined based on the complete square method. Numerical examples are provided to illustrate the effectiveness of the proposed theory.
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2023)
Article
Automation & Control Systems
Xiao Liang, Qingyuan Qi, Huanshui Zhang, Lihua Xie
Summary: This article explores decentralized control in networked control systems with asymmetric information, presenting optimal estimators and controllers based on asymmetric observations. Iterative solutions are proposed for the Riccati equations, providing a suboptimal solution to the decentralized control problem.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Computer Science, Information Systems
Jinliang Liu, Yanhui Dong, Lijuan Zha, Engang Tian, Xiangpeng Xie
Summary: This paper investigates the design of a security tracking controller for discrete-time networked control systems with stochastic cyber-attacks using a dynamic event-triggered communication approach. The approach adjusts the amount of data transmission in the network based on the variation of the tracking error while maintaining the tracking performance of the system. The paper provides a sufficient condition for asymptotic stability of the tracking error system using Lyapunov stability theory and co-designs the tracking controller gain and event-triggering parameter by solving a linear matrix inequality. Simulation examples are presented to validate the theoretical results.
INFORMATION SCIENCES
(2022)
Review
Automation & Control Systems
Zhong-Hua Pang, Lan-Zhi Fan, Haibin Guo, Yuntao Shi, Runqi Chai, Jian Sun, Guo-Ping Liu
Summary: This paper provides a detailed investigation into the recent developments in the security of networked control systems (NCSs) subject to deception attacks from the domains of information technology (IT) and system control. It reviews several recent security incidents and analyzes prevalent cyber attacks. IT security results related to the protection-detection-reaction model are summarized. The paper then delves into the security issues in attack design, attack detection, secure state estimation, and resilient control from the domain of system control. It also discusses several novel security topics arising from the combination of IT and system control. Finally, it presents future research directions in this area.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2022)
Article
Automation & Control Systems
Xiao Lu, Ruidong Liu, Chuanzhi Lv, Na Wang, Qiyan Zhang, Haixia Wang, Guilin Zhang, Xiao Liang
Summary: This paper focuses on the optimal output feedback control problem for networked control systems with various interferences, overcoming barriers of packet dropouts and measurement delays. It provides optimal estimator and controller, and demonstrates the effectiveness of the algorithm through numerical examples.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2021)
Article
Automation & Control Systems
Jinliang Liu, Zheng-Guang Wu, Dong Yue, Ju H. Park
Summary: This paper investigates the controller design problem of networked control systems subject to cyber attacks, using a hybrid-triggering communication strategy to save limited communication resources. The stability criterion for system stabilization is obtained by employing Lyapunov stability theory and stochastic analysis techniques. Furthermore, the desired controller gain is derived through matrix inequalities, and a numerical example is used to demonstrate the utility of the proposed scheme.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Automation & Control Systems
Haoyuan Sun, Jian Sun, Jie Chen
Summary: This paper focuses on the quantized control of networked control systems under stochastic clock offsets caused by asynchronous clocks between sensors and controllers. A quantized controller is designed using a stochastic variable with specific probability density function to handle quantization error and ensure stochastic stability of the systems. The effectiveness of the proposed design method is demonstrated through two numerical examples.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Automation & Control Systems
Chunyan Han, Song Zhang, Wei Wang, Tao Shen
Summary: This article focuses on the optimal control and stabilization of discrete-time networked control systems (NCSs) with Markovian fading channels. A new multistate Markov chain is introduced to overcome the difficulties caused by multi-channel packet losses and the correlation of the Markov chain. The optimal control and stabilization problem for NCSs with diagonal Markovian fading channels is transformed into a general Markovian jump linear system. A modified maximum principle is presented using a forward-backward stochastic difference equation (FBSDE). For both finite and infinite horizon cases, necessary and sufficient solvability conditions for optimal control and stabilization are obtained, along with explicit expressions for the optimal controller. The proposed results are illustrated through a numerical example.
OPTIMAL CONTROL APPLICATIONS & METHODS
(2023)
Article
Automation & Control Systems
Xiaowei Jiang, Jianhao Li, Bo Li, Xiangyong Chen, Huaicheng Yan
Summary: This study focuses on the optimal tracking performance (OTP) of networked control systems (NCSs) considering packet dropouts and noise constraints in communication networks, and provides explicit expressions for the OTP limitation under these constraints. The results indicate that the intrinsic features of the plant and the communication parameters of the network channel will affect the OTP of the NCSs.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Yujia Wang, Tong Wang, Xuebo Yang, Jiae Yang, Feihu Jin
Summary: This paper investigates a novel strategy for fault-tolerant control of nonlinear strict-feedback systems using Neural-Networks technique, updating weights based on gradient descent algorithm, establishing a fault-tolerant controller, and providing convergence and stability proofs.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2021)
Article
Automation & Control Systems
Yujia Wang, Jiae Yang, Xuebo Yang, Tong Wang
Summary: This paper presents an adaptive neural network-based fault-tolerant control strategy to solve the tracking control problem of a three degrees of freedom (3-DOF) helicopter with unknown actuator faults, model uncertainties, and external time-varying disturbances. By utilizing neural networks to approximate and compensate for unknown functions, and introducing hyperbolic tangent functions to reduce negative effects, the proposed strategy improves the tracking performance of the closed-loop system.
INTERNATIONAL JOURNAL OF CONTROL
(2023)
Article
Computer Science, Artificial Intelligence
Zhumu Fu, Nan Wang, Shuzhong Song, Tong Wang
Summary: This article investigates the adaptive fuzzy finite-time control problem for a class of high-order stochastic nonlinear systems and proposes a novel control strategy using fuzzy logic systems and backstepping design technique to guarantee system stability and achieve performance objectives.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Automation & Control Systems
Min Ma, Tong Wang, Runsheng Guo, Jianbin Qiu
Summary: This article investigates the neural-network based backstepping control problem for autonomous marine vehicles perturbed by external disturbances, considering the actuator dead-zone phenomenon. A command filtering compensation strategy is proposed to cope with complexity explosion and decrease tracking errors, ensuring satisfactory tracking performance and boundedness of the closed-loop system signals. Simulation studies further demonstrate the effectiveness of the proposed control method.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2022)
Article
Automation & Control Systems
Jianbin Qiu, Min Ma, Tong Wang
Summary: This article investigates the command filtering-based event-triggered adaptive fuzzy control problem for a class of stochastic nonlinear systems with stochastic faults and input saturation. The unknown nonlinear functions and system dynamic changes that caused by stochastic faults are approximated by fuzzy logic systems (FLSs). The effectiveness of the proposed method is verified by simulation studies, in which the uniform ultimate boundedness of the system is guaranteed and all the signals in the closed-loop system are bounded.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Automation & Control Systems
Wenshan Bi, Tong Wang
Summary: This article presents an adaptive fuzzy decentralized control algorithm for large-scale interconnected nonlinear systems with unmodeled dynamics. The algorithm utilizes a fuzzy logic system (FLS) to identify unknown nonlinear functions and introduces dynamic signals to compensate for the effect of unmodeled dynamics. A novel adaptive state feedback control algorithm is proposed using a Lyapunov function, and a fuzzy state observer with FLSs is constructed for output feedback. The algorithm ensures the semi-global uniformly ultimately bounded (SGUUB) stability of the system and guarantees bounded signals. Numerical and practical simulation examples are provided to demonstrate the feasibility of the control algorithms.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Jianbin Qiu, Tong Wang, Kangkang Sun, Imre J. Rudas, Huijun Gao
Summary: This article focuses on the disturbance observer-based adaptive fuzzy finite-time control issue of strict-feedback nonlinear systems. The finite-time prescribed performance is considered to meet practical application requirement. A disturbance observer is proposed to estimate the external disturbance, and the stability of the closed-loop system is proven.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Nan Wang, Zhumu Fu, Shuzhong Song, Tong Wang
Summary: This article studies the finite-time adaptive fuzzy state-feedback tracking control problem for the pure-feedback system with full state constraints. By introducing the mean value theorem and finite-time-stablelike function, the pure-feedback form is transformed into a system strict-feedback case, and integral barrier Lyapunov functions are used to ensure that the state variables remain within the prescribed constraints. Fuzzy logic systems are utilized to approximate nonlinear uncertainties, achieving convergence of output tracking error and semiglobal ultimate uniform boundedness of signals in the system.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Information Systems
Zhaoke Ning, Tong Wang, Kai Zhang
Summary: This paper investigates event-triggered security control and fault detection for nonlinear systems, where output signals are wirelessly transmitted to the control and detection module. Quantization before data transmission and deception attacks during data transmission are considered. A dynamic event-triggered protocol is proposed to ease the data transmission pressure of wireless networks, and the triggering threshold changes according to the system state. By considering the dynamic event-triggered protocol, signal quantization, and randomly occurring deception attacks, an integrated dynamic output feedback controller and fault detection filter module is developed, ensuring stochastic stability and guaranteed levels of security control and detection performance.
INFORMATION SCIENCES
(2022)
Article
Mathematics
Jiae Yang, Yujia Wang, Tong Wang, Xuebo Yang
Summary: This paper focuses on the tracking control problem for a family of fractional-order systems with unknown drift functions and unknown time delays. By employing fuzzy logic systems, the unknown functions are approximated and compensated while mitigating the adverse effects of time-varying delays and approximation errors. Stability analysis shows that the tracking error can converge to a small neighborhood of the origin. Simulation confirms the effectiveness of the presented control strategy.
Article
Automation & Control Systems
Runsheng Guo, Kangkang Sun, Tong Wang, Jianbin Qiu, Danlei Chu
Summary: This article investigates the boundary control problem for a class of 2 x 2 hyperbolic partial differential equation systems with uncertain transport speeds, considering both state feedback and output feedback cases. The least-square method is used for finite-time parameter estimation based on parametric models. Proper actuation signals are designed via contradiction method in parameter estimation to avoid singular problems. Control laws are designed using the backstepping method for both state-feedback and output-feedback cases, with the estimated parameters. Simulation studies are provided to demonstrate the efficiency of the results.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2022)
Article
Automation & Control Systems
Wenshan Bi, Tong Wang, Xinghu Yu
Summary: This article introduces an adaptive fuzzy control algorithm for a class of large-scale switched fractional-order nonlinear nonstrict feedback systems. The algorithm uses fuzzy-logic systems to approximate unknown nonlinear functions and presents a virtual control law based on fractional Lyapunov stability rules. A fuzzy adaptive decentralized control method is developed under the technique of the Lyapunov function to ensure the stability and control performance of the proposed systems. Simulation results are provided to demonstrate the feasibility and effectiveness of the proposed method.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Tong Wang, Nan Wang, Jianbin Qiu, Concettina Buccella, Carlo Cecati
Summary: In this article, we address the tracking control problem for a class of stochastic nonlinear systems with output feedback signal. The controlled plant is assumed to be affected by unknown dead-zone input. By modeling the unknown dead-zone input function as a time-varying nonlinear function and a bounded disturbance and selecting appropriate design parameters, we demonstrate that the effect of the unknown dead zone can be compensated for. Furthermore, a fuzzy state observer is designed to estimate the unknown system states using fuzzy logic modeling technique, and Lyapunov stability analysis shows that the controlled plant is bounded in probability, and all signals in the closed-loop system are globally bounded in probability. The tracking errors are also ensured to converge to a small neighborhood of the origin. Finally, a simulation example of a one-link manipulator is presented to demonstrate the effectiveness of the proposed control strategy.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yujia Wang, Tong Wang, Xuebo Yang, Jiae Yang
Summary: In this article, a control strategy for nonlinear systems is proposed, which combines the gradient descent-Barzilai Borwein algorithm and radial basis function neural network. The main advantages of this strategy are the online updating of neural network parameters and the simplification of controller design process, reducing the number of parameters that need to be adjusted.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Tong Wang, Yujia Wang, Xuebo Yang, Jiae Yang
Summary: This article develops a novel cost function to solve the optimal tracking control problem for a class of nonlinear systems with known system dynamics. By designing a specific cost function related to tracking errors and their derivatives, the assumption and obstacles in traditional problems are removed, and the controller design process is simplified. Comparative simulations on an inverted pendulum system demonstrate the effectiveness and advantages of the proposed optimal tracking control strategy.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)