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In this article, a robust adaptive output-feedback control approach is presented for a class of nonlinear output-feedback systems with parameter uncertainties and time-varying bounded disturbances. A reduced-order filter driven by control input is proposed to reconstruct unmeasured states. The state estimation error is shown to be bounded by dynamic signals driven by system output. The bound estimation technique is employed to estimate the unknown disturbance bound. Based on the backstepping design with three sets of tuning functions, an adaptive output-feedback control scheme with the flat-zone modification is proposed. It is shown that all the signals in the resulting closed-loop adaptive control systems are bounded, and the output tracking error converges to a prespecified small neighborhood of the origin. Two simulation examples are provided to illustrate the effectiveness and validity of the proposed approach.
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http://dx.doi.org/10.1109/TCYB.2021.3049786 | DOI Listing |
ISA Trans
September 2025
School of Science, Yanshan University, Qinhuangdao Hebei, 066004, PR China. Electronic address:
This article concentrates on the issue of event-triggered dynamic output feedback control for Markovian jump complex dynamical networks (MJCNDs) subject to multiple cyberattacks. To alleviate the communication pressure, a new adaptive event-triggered mechanism (AETM) is proposed. This AETM incorporates a dynamically adjustable parameter and mode-dependent properties to enhance flexibility.
View Article and Find Full Text PDFThis article investigates the adaptive resilient flexible output containment (FOC) control problem for semi-Markov jump fully heterogeneous multiagent systems (FHMASs) under random switching topologies and denial-of-service (DoS) attacks. In contrast to most existing containment control results, the proposed control strategy can address the challenges posed by the full heterogeneity of multiagent systems (MASs), particularly when multiple leaders exhibit different system dynamics. To better reflect real-world MASs and communication networks, multiple asynchronous semi-Markov chains are employed for the first time to capture system parameter variations and communication topology switching, incorporating generally uncertain transition rates (TRs).
View Article and Find Full Text PDFThis article investigates the observer-based adaptive decentralized control problem for a class of uncertain interconnected nonlinear fully actuated systems (FAS), considering nonsmooth actuator dynamics including actuator failures and unknown control gains. Based on the dynamic gain scaling technique, a dynamic state observer is constructed. By utilizing the high-order FAS (HOFAS) approach, an adaptive decentralized output feedback controller is designed and a closed-loop structure of the fully actuated subsystems is derived.
View Article and Find Full Text PDFIEEE Trans Cybern
September 2025
Developing model-free $H_{\infty }$ optimal control schemes in systems with unknown model parameters and unmeasurable states is challenging. In this article, an output-feedback (OPFB) suboptimal control scheme based on adaptive dynamic programming (ADP) is proposed to realize model-free $H_{\infty }$ control under uncertain disturbances. First, a free matrix is introduced to compute the suboptimal gain in the absence of an optimal OPFB gain, and a policy iterative algorithm is developed to solve for the suboptimal gain and shown to converge to a solution of the algebraic Riccati equation.
View Article and Find Full Text PDFISA Trans
June 2025
School of Mechano-electronic Engineering, Xidian University, Xi'an, 710071, China. Electronic address:
An event-triggered adaptive backstepping control methodology is proposed to achieve leader-following consensus of uncertain nonlinear high-order multi-agent systems with unknown control gains. In light of the partial observability limitation, adaptive distributed observers are employed to estimate the unobservable states of the leader, whereas local state observers are utilized to reconstruct the states of the followers. By integrating fuzzy logic systems, the unknown nonlinear dynamics are modeled, guaranteeing reliable state prediction in complex and partially observable scenarios.
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