Article
Automation & Control Systems
Ke Xu, Huanqing Wang, Peter Xiaoping Liu
Summary: In this article, the problem of adaptive fixed-time tracking control for non-triangular structural stochastic switching nonlinear systems is addressed. Fuzzy logic systems are used to compensate for unknown nonlinearities, and the unknown control gain problem is addressed by constructing proper Lyapunov function candidates. By employing Lyapunov stability theorem, the control approach ensures semi-globally practical fixed-time stability, accurate tracking, and small tracking error. Simulation results verify the effectiveness of the developed control approach.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Automation & Control Systems
Ze-Hao Wu, Feiqi Deng, Bao-Zhu Guo, Chufen Wu, Qiaomin Xiang
Summary: In this article, the active disturbance rejection control (ADRC) is applied to address the output tracking of a lower triangular nonlinear system subject to mismatched bounded stochastic disturbances. The study first designs a set of second-order extended state observers to estimate disturbances and then develops a backstepping ADRC approach, ensuring the closed-loop output tracks a time-varying reference signal and the closed-loop states are bounded in probability. Numerical simulations validate the effectiveness of the proposed approach.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Xiaohui Yan, Mou Chen, Gang Feng, Qingxian Wu, Shuyi Shao
Summary: This article proposes an adaptive fuzzy control scheme based on HODO and DSC techniques for nonlinear systems with input saturation and external disturbances. By utilizing backstepping method and Lyapunov analysis, it is proven that all signals in the system are bounded and the tracking error converges to a compact set.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Automation & Control Systems
Jianping Cai, Congli Mei, Qiuzhen Yan
Summary: This paper proposes two semi-global adaptive control schemes for a class of parametric strict-feedback systems with non-triangular structural uncertainties. By introducing semi-global stability analysis, the problem of using the improved backstepping method directly is solved, and the stability of the closed-loop system is guaranteed.
Article
Automation & Control Systems
Jiehua Feng, Dongya Zhao, Xing-Gang Yan, Sarah K. Spurgeon
Summary: This paper investigates a class of interconnected systems, where the isolated subsystems are fully nonlinear and non-minimum phase. A decentralized robust adaptive output feedback control scheme is proposed to counter the effects of interconnections and uncertainties. The proposed control scheme guarantees signal boundedness in the closed-loop system. The effectiveness of the proposed method is demonstrated through two MATLAB simulation examples.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2023)
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, Information Systems
Yangang Yao, Jieqing Tan, Jian Wu, Xu Zhang
Summary: This article discusses the problem of event-triggered fixed-time adaptive neural dynamic surface control for stochastic non-triangular structure nonlinear systems. A novel event-triggered fixed-time adaptive controller is designed to ensure both closed-loop stability and tracking performance simultaneously in a fixed time, while avoiding the issues of complexity explosion and singularity under the traditional backstepping design framework. The design of event-triggered control mechanism effectively saves network resources and the effectiveness of the proposed method is demonstrated through rigorous theoretical derivation and simulations.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Yongchao Liu, Qidan Zhu
Summary: In this article, the issue of adaptive neural network asymptotic tracking control for nonstrict feedback stochastic nonlinear systems is studied using the backstepping algorithm. Compared with previous research, the difficulty of unknown virtual control coefficients in control design is overcome. The recursive construction of the asymptotic tracking controller is achieved through the use of bound estimation scheme, smooth functions, and approximation-based neural network, ensuring asymptotic convergence character and stability with stochastic disturbance and unknown UVCC with the help of Lyapunov function and beneficial inequalities. This theoretical finding is verified through a simulation example.
Article
Computer Science, Artificial Intelligence
Yongming Li, Jiaxin Zhang, Wei Liu, Shaocheng Tong
Summary: This work investigates an adaptive neural network optimized output-feedback control problem for a class of stochastic nonlinear systems with unknown nonlinear dynamics, input saturation, and state constraints. It proposes an optimized control strategy based on the backstepping technique and actor-critic architecture to prevent system violations of state constraints and ensure bounded signals in the closed-loop system.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Engineering, Mechanical
Yuhang Zhang, Yifan Liu, Guozeng Cui, Ze Li, Wanjun Hao
Summary: This paper investigates the problem of finite-time distributed consensus control for non-triangular stochastic nonlinear multi-agent systems (SNMASs) with input constraints. Fuzzy logical systems are used to identify the unknown nonlinear dynamics of non-triangular SNMASs. A finite-time command filter is utilized to eliminate the issue of explosion of complexity in the conventional backstepping-based distributed control algorithm, and a fractional power error compensation mechanism is constructed to improve the distributed control performance of SNMASs. It is proved that the proposed distributed controller enables all of the closed-loop system's signals to be semi-globally finite-time bounded in probability, and the consensus tracking errors will converge to a sufficiently small neighborhood of the origin in a finite time. Finally, the effectiveness of the presented finite-time distributed control scheme is illustrated with a simulated example.
Article
Automation & Control Systems
Zhen-Guo Liu, Wei Sun, Weidong Zhang
Summary: This study focuses on the adaptive control issue of high-order nonlinear systems with odd rational powers, unmodeled dynamics, and non-triangular structure. By employing the adaptive technique, adding a power integrator method, and neural network method, a new adaptive controller is successfully constructed, greatly reducing the use of parameter estimations.
Article
Automation & Control Systems
Tianliang Zhang, Shun-Feng Su, Wei Wei, Ruey-Huei Yeh
Summary: This article addresses the practically predefined-time adaptive fuzzy tracking control problem of strict-feedback nonlinear stochastic systems. A Lyapunov-type criterion for practically predefined-time stochastic stabilization (PPSS) is proposed to assure the stabilization of the system. Based on the backstepping design method, a semiglobally practically predefined-time adaptive fuzzy tracking control algorithm is proposed. The proposed control strategy is validated through practical and numerical examples.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Hui Ma, Hongru Ren, Qi Zhou, Renquan Lu, Hongyi Li
Summary: This article investigates Nussbaum gain adaptive control for a type of nonlinear systems, tackling challenges such as periodic disturbances and unknown control direction by utilizing Fourier series expansion and radial basis function neural network for function approximation. The control algorithm is designed with a Nussbaum-type function to handle dead zone output and unknown control direction, ensuring bounded closed-loop signals and tracking error convergence. Simulation results validate the effectiveness and applicability of the proposed analysis approach.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Computer Science, Information Systems
Xin Jin, Yuan-Xin Li
Summary: This article investigates the fuzzy adaptive control design for a class of stochastic nonstrict feedback nonlinear systems, introducing a bounded estimation method, smooth functions, and barrier Lyapunov functions to ensure the controlled system's performance and stability. The proposed asymptotic tracking control scheme shows superior performance in an illustrative simulation instance.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Zhaoyang You, Fang Wang
Summary: This article focuses on the adaptive fast finite-time tracking control for a class of stochastic nonlinear systems. An adequate qualification for the practically fast finite-time stability is provided, and a new adaptive fuzzy control scheme based on this qualification is presented. The existing criteria for fast finite-time stability cannot be applied to the analysis of stochastic nonlinear systems due to the stochastic disturbance. To address this issue, an original criterion for fast finite-time stability is constructed using the theorem of the integral mean value, which has a significant impact on the analysis of fast finite-time stability for stochastic nonlinear systems. Finally, a simulation example is conducted to evaluate the validity of the main result.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Automation & Control Systems
Yige Guo, Xiang Xu, Lu Liu, Yong Wang, Gang Feng
Summary: This paper investigates the stabilization problem of discrete-time linear systems with infinite distributed input delays. A novel framework is adopted to analyze the stability of the concerned systems. Two truncated predictor feedback controllers are developed for two classes of discrete-time linear systems with infinite distributed input delays via the low gain method, and it is shown that these systems are globally exponentially stable under the designed controllers. This is the first time that the stabilization problem of discrete-time linear systems with infinite distributed input delays is considered, and simulation examples demonstrate the effectiveness of the proposed controllers.
Article
Automation & Control Systems
Xiang Xu, Lu Liu, Miroslav Krstic, Gang Feng
Summary: This paper addresses the stabilization problems in cascade systems involving linear ODEs and PDEs of both hyperbolic and parabolic types. The paper considers systems where the output of one subsystem is the control input of the other subsystem, and extends the existing results by allowing for different PDE dynamics and general LTI systems. The paper develops a backstepping procedure and proves the exponential stability of the closed-loop system. A simulation example is provided to illustrate the effectiveness of the proposed controllers.
Article
Automation & Control Systems
Qianghui Zhou, Xiang Xu, Lu Liu, Gang Feng
Summary: This article investigates the output feedback stabilization problem of linear time-varying (LTV) systems with infinite distributed input delays. A novel observer is designed to estimate the system states and then a low gain output feedback controller is developed based on the estimated states. The resulting closed-loop control system is shown to be globally exponentially stable under some mild assumptions. To the best of our knowledge, the output feedback stabilization problem of LTV systems with infinite distributed input delays has not yet been studied in open literature. Two numerical examples are provided to illustrate the effectiveness of the proposed controllers.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Automation & Control Systems
Xiang Xu, Lu Liu, Miroslav Krstic, Gang Feng
Summary: This article proposes two low-gain controllers for systems with distributed input dynamics, governed by diffusion equations with counter convection. It shows that the considered PDE-ODE cascade system can be exponentially stabilized with the proposed low-gain controllers. The advantage of the proposed controllers is their simplified form compared to existing backstepping-designed controllers, but they require additional restrictions on the eigenvalues of the open-loop plant.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Automation & Control Systems
Dan Zhang, Chao Deng, Gang Feng
Summary: This article addresses the problem of resilient cooperative output regulation for a class of uncertain nonlinear multiagent systems (MASs) under denial-of-service (DoS) attacks. A novel distributed control scheme is proposed, which includes a resilient distributed observer and a distributed adaptive controller. The effectiveness of the proposed control scheme is demonstrated through a simulation example.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Automation & Control Systems
Qianghui Zhou, Lu Liu, Gang Feng
Summary: This paper investigates the robust stabilization problem of a class of uncertain Lipschitz nonlinear systems with infinite distributed input delays. A novel robust predictor feedback controller is proposed, and the controller gain can be obtained by solving a linear matrix inequality. It is shown that the proposed controller can exponentially stabilize the concerned system globally. The key contribution of this approach lies in the development of new quadratic Lyapunov functionals. The obtained results are generalized to systems with both multiple constant input delays and infinite distributed input delays.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2023)
Article
Automation & Control Systems
Jin Zhang, Lu Liu, Xinghu Wang, Haibo Ji
Summary: This note investigates a distributed optimal resource allocation problem in multiagent systems over unbalanced directed networks with locally Lipschitz gradients of local cost functions. The objective is to drive decision variables of agents to the optimal solution while satisfying network resource constraints and local feasibility constraints. A novel distributed algorithm is developed based on topology balancing and adaptive control approach, which is fully distributed and does not rely on global information about the network connectivity. The algorithm's input-to-state stability with a vanishing perturbation is established, and asymptotic convergence of decision variables towards the optimal solution is proved.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Automation & Control Systems
Peng Wang, Cheng Song, Lu Liu
Summary: This article investigates the coverage control for mobile sensors with double-integrator dynamics and different maximum velocities on a circle. A generalized energy function is introduced to maintain the order of the sensors. The results show that the sensor network can approach the optimal configuration and provide an upper bound on the coverage cost function.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Automation & Control Systems
Tao Xu, Tuo Yang, Zhisheng Duan, Gang Feng, Guanrong Chen
Summary: This article studies the coordination control problem of networked manipulators, where a two-layer control scheme is developed to estimate the position and velocity of the trajectory and design an event-driven distributed controller. The proposed controller reduces control update burden and communication costs by performing control updates and communication at certain event instants. Simulation results using MATLAB Robotics Toolbox verify the effectiveness and superiority of the proposed control scheme.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(2023)
Article
Automation & Control Systems
Jin Zhang, Lu Liu, Xinghu Wang, Haibo Ji
Summary: This article proposes a novel observer-based output feedback control approach to address the distributed optimal coordination problem of uncertain nonlinear multi-agent systems in the normal form over unbalanced directed graphs. The main challenges of the concerned problem lie in unbalanced directed graphs and nonlinearities of multi-agent systems with their agent states not available for feedback control. Based on a two-layer controller structure, a distributed optimal coordinator is first designed to convert the considered problem into a reference-tracking problem. Then a decentralized output feedback controller is developed to stabilize the resulting augmented system. A high-gain observer is exploited in controller design to estimate the agent states in the presence of uncertainties and disturbances so that the proposed controller relies only on agent outputs. The semi-global convergence of the agent outputs toward the optimal solution that minimizes the sum of all local cost functions is proved under standard assumptions. A key feature of the obtained results is that the nonlinear agents under consideration are only required to be locally Lipschitz and possess globally asymptotically stable and locally exponentially stable zero dynamics.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Computer Science, Artificial Intelligence
Xingxing Ju, Xinsong Yang, Gang Feng, Hangjun Che
Summary: This paper proposes three novel accelerated inverse-free neurodynamic approaches to solve absolute value equations. The first two approaches converge in a finite-time, while the third approach converges in a fixed-time. It is shown that the proposed methods converge to the solution of the absolute value equations, and the settling times depend on initial conditions. The robustness of the proposed approaches against bounded vanishing perturbations is also demonstrated. The theoretical results are validated through numerical examples and applications in boundary value problems.
Article
Automation & Control Systems
Xiaoyu Luo, Chengcheng Zhao, Chongrong Fang, Jianping He
Summary: This paper investigates the problem of false data injection attacks in multi-agent dynamical systems and proposes FDI attack set selection algorithms to maximize the convergence error by finding the optimal subset of compromised agents.
Article
Automation & Control Systems
Nitin K. Singh, Abhisek K. Behera
Summary: In this paper, a twisting observer is proposed for robustly estimating the states of a second-order uncertain system. The observer approximates the unknown sign term for the non-measurable state with a delayed output-based switching function, and achieves the desired steady-state accuracy by controlling the delay parameter. The application of the observer to output feedback stabilization is also discussed.
Article
Automation & Control Systems
Alexander Aleksandrov
Summary: This paper investigates the absolute stability problem for positive Persidskii systems with delay, proposes a special construction method for diagonal Lyapunov-Krasovskii functionals, and derives a criterion for the existence of such functionals guaranteeing the absolute stability, as well as obtaining sufficient conditions for a family of time-delay Persidskii systems to construct a common diagonal Lyapunov-Krasovskii functional. The efficiency of the developed approaches is demonstrated through four examples.
Article
Automation & Control Systems
Noureddine Toumi, Roland Malhame, Jerome Le Ny
Summary: This paper addresses large multi-agent dynamic discrete choice problems using a linear quadratic mean field games framework. The model incorporates the features where agents have to reach a predefined set of possible destinations within a fixed time frame and running costs can become negative to simulate crowd avoidance. An upper bound on the time horizon is derived to prevent agents from escaping to infinity in finite time. The existence of a Nash equilibrium for infinite population and its epsilon-Nash property for a large but finite population are established. Simulations are conducted to explore the model behavior in various scenarios.
Article
Automation & Control Systems
Philippe Schuchert, Vaibhav Gupta, Alireza Karimi
Summary: This paper presents the design of fixed-structure controllers for the As2 and Asw synthesis problem using frequency response data. The minimization of the norm of the transfer function between the exogenous inputs and performance outputs is approximated through a convex optimization problem involving Linear Matrix Inequalities (LMIs). A general controller parametrization is used for continuous and discrete-time controllers with matrix transfer function or state-space representation. Numerical results show that the proposed data-driven method achieves performance equivalent to model-based approaches when a parametric model is available.
Correction
Automation & Control Systems
Zhijun Guo, Gang Chen
Article
Automation & Control Systems
Matteo Della Rossa, Thiago Alves Lima, Marc Jungers, Raphael M. Jungers
Summary: This paper presents new stabilizability conditions for switched linear systems with arbitrary and uncontrollable underlying switching signals. The study focuses on two specific settings: the robust case with completely unknown and unobservable active mode, and the mode-dependent case with controller depending on the current active switching mode. The technical developments are based on graph-theory tools and path-complete Lyapunov functions framework, enabling the design of robust and mode-dependent piecewise linear state-feedback controllers using directed and labeled graphs.
Article
Automation & Control Systems
Elena Petri, Romain Postoyan, Daniele Astolfi, Dragan Nesic, W. P. M. H. (Maurice) Heemels
Summary: This study investigates a scenario where a perturbed nonlinear system transmits its output measurements to a remote observer via a packet-based communication network. By designing both the observer and the local transmission policies, accurate state estimates can be obtained while only sporadically using the communication network.
Article
Automation & Control Systems
Jonas Krook, Robi Malik, Sahar Mohajerani, Martin Fabian
Summary: This paper proposes a method to synthesise controllers for cyber-physical systems subjected to disturbances, such that the controlled system satisfies specifications given as linear temporal logic formulas. The approach constructs a finite-state abstraction of the original system and synthesises a controller for the abstraction. It introduces the robust stutter bisimulation relation to account for disturbances and uncertainty, ensuring that related states have similar effects under the same controller. The paper demonstrates that the existence of a controller for the abstracted system implies the existence of a controller for the original system enforcing the linear temporal logic formula.
Article
Automation & Control Systems
Clement Chahbazian, Karim Dahia, Nicolas Merlinge, Benedicte Winter-Bonnet, Aurelien Blanc, Christian Musso
Summary: The paper derives a recursive formula of the Fisher information matrix on Lie groups and applies it to nonlinear Gaussian systems on Lie groups for testing. The proposed recursive CRLB is consistent with state-of-the-art filters and exhibits representative behavior in estimation errors. This paper provides a simple method to recursively compute the minimal variance of an estimator on matrix Lie groups, which is fundamental for implementing robust algorithms.
Article
Automation & Control Systems
Yiheng Fu, Pouria Ramazi
Summary: This study investigates the characteristics of decision fluctuations in heterogeneous populations and explores the uncertainties in imitation behavior. The findings are important for understanding the bounded rationality nature of imitation behaviors.
Article
Automation & Control Systems
Lars A. L. Janssen, Bart Besselink, Rob H. B. Fey, Nathan van de Wouw
Summary: This paper introduces a mathematical relationship between the accuracy of reduced-order linear-time invariant subsystem models and the stability and accuracy of the resulting reduced-order interconnected linear time-invariant model. This result can be used to directly translate the accuracy characteristics of the reduced-order subsystem models to the accuracy properties of the interconnected reduced-order model, or to translate accuracy requirements on the interconnected system model to accuracy requirements on subsystem models.
Article
Automation & Control Systems
Piyush Gupta, Vaibhav Srivastava
Summary: We study the optimal fidelity selection for a human operator servicing tasks in a queue, considering the trade-off between high-quality service and penalty due to increased queue length. By modeling the operator's cognitive dynamics and task fidelity, we determine the optimal policy and value function numerically, and analyze the structural properties of the optimal fidelity policy.
Article
Automation & Control Systems
Lukas Schwenkel, Alexander Hadorn, Matthias A. Mueller, Frank Allgoewer
Summary: In this work, the authors study economic model predictive control (MPC) in periodic operating conditions. They propose a method to achieve optimality by multiplying the stage cost by a linear discount factor, which is easy to implement and robust against online changes. Under certain assumptions, they prove that the resulting linearly discounted economic MPC achieves optimal asymptotic average performance and guarantees practical asymptotic stability of the optimal periodic orbit.
Article
Automation & Control Systems
Taher Ebrahim, Sankaranarayanan Subramanian, Sebastian Engell
Summary: We propose a robust nonlinear model predictive control algorithm for dynamic systems with mixed degrees of freedom. This algorithm optimizes both continuous and discrete manipulated variables, enhancing closed-loop performance. Our approach relies on a computationally efficient relaxation and integrality restoration strategy and provides sufficient conditions to establish recursive feasibility and guarantee robust closed-loop stability. The effectiveness of the approach is demonstrated through two nonlinear simulation examples.
