Article
Engineering, Mechanical
Jie Zhang, Jing Yang, Zhongcai Zhang, Yuqiang Wu
Summary: This article considers an adaptive output feedback controller for a class of nonlinear systems with integral input-to-state stable inverse dynamics, external disturbance, and asymmetric output constraints. A tan-type barrier Lyapunov function is introduced to solve the asymmetric output constraints. The designed scheme, using extended and reduced-order state observers, backstepping technique, and linear matrix inequality, guarantees the global asymptotic stability of the closed-loop system and does not violate the asymmetric output constraints.
NONLINEAR DYNAMICS
(2022)
Article
Automation & Control Systems
Yan Zhao, Jiangbo Yu
Summary: This paper investigates the adaptive asymptotically stabilizing control problem for uncertain nonlinear systems using partial-state feedback, with a focus on input-to-state stability and the novel Nussbaum function to counteract unknown control directions. Damping terms with estimates of unknown disturbance bounds are added in control design to handle nonvanishing external disturbances, resulting in bounded signals and asymptotic convergence of system states to zero despite uncertainties. A simulation example is provided to demonstrate the effectiveness of the proposed control scheme.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2021)
Article
Automation & Control Systems
Hiroshi Ito, Hyungbo Shim
Summary: This article discusses the flexibility in dealing with the interconnection of integral input-to-state stable (iISS) and input-to-state stable (ISS) systems. It introduces a framework that combines the separate characterizations of iISS and ISS to enable global analysis. The article proposes a toolkit for robustness guarantees in observer-based output feedback control subject to measurement noise, especially for nonlinear plants. It demonstrates the flexibility in dealing with couplings and provides conditions for achieving both ISS and strong iISS in the closed-loop system.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Automation & Control Systems
Lijun Long, Jie Wang
Summary: This paper proposes a safety-critical dynamic event-triggered control strategy for nonlinear systems, which simultaneously ensures the safety and stability of event-triggered control. The strategy utilizes the union of input-to-state safe barrier function and input-to-state stable Lyapunov function, constructs an appropriate dynamic event triggered mechanism, and excludes the Zeno phenomenon. Compared with traditional continuous-time feedback control, this strategy reduces the waste of communication and computation resources.
SYSTEMS & CONTROL LETTERS
(2022)
Article
Engineering, Mechanical
Fangling Zou, Kang Wu, Yuqiang Wu
Summary: This article investigates an adaptive output feedback control problem for a non-holonomic system with integral input-to-state stable inverse dynamics and output constraints. A tan-type barrier Lyapunov function is utilized to handle asymmetric time-varying output constraints, and the full-order observer is constructed to estimate the unmeasurable state. The dynamic uncertainty is eliminated by changing the supply rate of the integral input-to-state stability. It is demonstrated that the closed-loop system is asymptotically stable, and the output does not violate the asymmetric time-varying constraints under this control scheme. A simulation example validates the effectiveness of the proposed controller.
NONLINEAR DYNAMICS
(2023)
Article
Automation & Control Systems
Kang Wu, Jiangbo Yu, Yan Zhao
Summary: This paper presents a robust adaptive stabilizing control scheme for a class of nonlinear cascaded systems with a time-varying output constraint. It considers nonlinear parameterization and unmeasured zero-dynamics, using ISS and ISS-Lyapunov functions to describe the zero-dynamics, TVBLF to ensure output constraint satisfaction, and changing supply rates technique to deal with unknown cascaded zero-dynamic states. The proposed control scheme achieves bounded signals and asymptotic stabilization without violating the output constraint.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2022)
Article
Automation & Control Systems
Mouquan Shen, Tu Zhang, Ju H. Park, Qing-Guo Wang, Li-Wei Li
Summary: In this article, an iterative proportional-integral interval estimation strategy is investigated for linear discrete-time systems. A sequence iterative proportional-integral observer is built using system output and unknown disturbance iterative estimation. A sufficient condition based on linear matrix inequality is proposed to ensure the asymptotic stability of the observer error system by utilizing a structure separation technique. The reachability of the observer error system is analyzed using zonotope. Zonotope-based iterative algorithms, with and without the output integral, are developed to generate estimated state intervals which demonstrate tighter estimation intervals compared to existing results in a simulation study.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Computer Science, Information Systems
Shizhang Chen, Chongyang Ning, Qian Liu, Qingping Liu
Summary: This study focuses on the Lyapunov characterization of input-output-to-state stability for time-varying nonlinear switched systems. It relaxes the restriction on the negativity of the time derivative of Lyapunov functions and allows subsystems to not be IOSS in the state space. The results are extended to integral input-output-to-state stability and output-to-state stability, and a state-norm estimator based on the indefinite Lyapunov function is proposed as a by-product. The effectiveness of the results is illustrated through a numerical example.
INFORMATION SCIENCES
(2022)
Article
Mathematics, Applied
Jing Wu, Wei Sun, Shun-Feng Su, Jianwei Xia
Summary: This study addresses the adaptive neural network tracking control problem for uncertain strict-feedback nonlinear systems with quantized input and output constraint. It utilizes the disintegration of hysteresis quantizer and a log-type Barrier Lyapunov function to overcome the obstacles, with uncertain nonlinearities approximated by radial basis function neural networks and a single adaptive law to reduce computational burden. The proposed control scheme ensures boundedness of signals, convergence of output tracking error, and adherence to output constraint, validated through simulation examples.
APPLIED MATHEMATICS AND COMPUTATION
(2022)
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
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
Automation & Control Systems
Hao Yu, Xia Chen, Fei Hao
Summary: This article studies the event-triggered control problem for iISS nonlinear systems, introducing integral-based and switching event-triggering mechanisms to compensate for measurement errors and unknown disturbances, and proving the closed-loop iISS and Zeno-freeness under certain assumptions on the gains of the iISS Lyapunov function.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2021)
Article
Automation & Control Systems
Lampros N. N. Bikas, George A. A. Rovithakis
Summary: In this article, a low-complexity state-feedback controller is designed for uncertain MIMO nonlinear systems with time-varying delays. The controller is capable of imposing prespecified performance attributes on the output tracking errors. The control design does not require knowledge of system nonlinearities or trajectory derivatives. The theoretical results are verified by simulation studies on a two-link robotic manipulator.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Mathematics, Applied
Le Wang, Wei Sun, Shun-Feng Su
Summary: In this study, an adaptive asymptotic tracking control scheme is proposed for strict-feedback nonlinear systems with state constraints and input saturation. The scheme ensures that system states do not violate the given constraints and the tracking error converges to zero asymptotically.
APPLIED MATHEMATICS AND COMPUTATION
(2022)
Article
Automation & Control Systems
Christoph Kawan, Andrii Mironchenko, Majid Zamani
Summary: In this article, it is shown that an infinite network of input-to-state stable (ISS) subsystems with ISS Lyapunov functions can have an ISS Lyapunov function if the couplings between the subsystems are weak enough. The strength of the couplings is described by the properties of an infinite-dimensional nonlinear positive operator built from the interconnection gains. If this operator leads to a uniformly globally asymptotically stable (UGAS) system, a Lyapunov function for the infinite network can be constructed. Necessary and sufficient conditions for UGAS are analyzed and related to small-gain conditions used in the stability analysis of finite networks.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
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.