International Journal of Robust and Nonlinear Control最新文献

This article aims to investigate the issue of asynchronous $��{�\mathscr{H}�}_{�\infty �}�$ filtering for discrete-time singular nonhomogeneous Markov jump systems with hybrid cyber attacks. To conserve and optimize network resources for communication channels, a logarithmic quantizer has been introduced. Meanwhile, double hidden Markov models (HMMs) are proposed to delineate the mode different phenomenon among system, quantizer and filter modes. This approach, combined with more comprehensive transition probabilities improves practical applicability. Then a criterion for solving the $��{�\mathscr{H}�}_{�\infty �}�$ filtering problem has been derived. Eventually, a practical example is presented to show the efficacy of the solved filter.

The primary objective of this study is to develop an adaptive controller for constrained nonlinear systems to achieve tracking control effectively in finite time. The research is conducted in three systematic steps: First, a novel practical finite-time stability (PFTS) criterion is established, providing the theoretical foundation for the design of controllers. Second, the original system with extended state constraints is converted into an unconstrained one via nonlinear mapping (NM). Third, building upon the established PFTS criterion and the command-filtered backstepping technique, an adaptive finite-time control scheme is proposed. This innovative control algorithm ensures superior tracking performance while strictly adhering to extended full-state constraints. Additionally, all system signals maintain PFTS. Both stability analysis and simulations are conducted to validate the effectiveness of the proposed control strategy.

In this work, we propose a learning framework that can synthesize a robust control Lyapunov function and a stable state-feedback controller for general nonlinear systems with quadratically bounded disturbances. While theoretically appealing, traditional approaches based on control Lyapunov functions face significant practical challenges due to unmodeled disturbances and estimation errors. To address this issue, our work employs a learning-based approach that combines Lyapunov analysis with machine-learning techniques. Additionally, we propose a random sampling-based method to verify the validity of the Lyapunov condition. Furthermore, we extend our approach to synthesize stable controllers for control-affine systems with unstructured uncertainties and actively learn the uncertainty from system trajectories. Finally, we show the effectiveness of our approach on four examples: the inverted pendulum, a third-order strict-feedback system, the cart-pole system, and the 2D quadrotor system.

Due to the coupling characteristic of permanent magnet synchronous motor (PMSM) systems, this paper considers the influence of the direct current component on the angular velocity as multiple unknown control coefficients for asymptotic control design. On this basis, to ensure the asymptotic stability of the PMSM, improvements to the traditional Nussbaum method are developed as follows. To guarantee the asymptotic tracking performance of the Nussbaum approach, novel Nussbaum gains are designed with decaying terms, which enable the residual terms to converge to zero. Considering the presence of unpredictable load disturbances, a compensation term is introduced into the Nussbaum-type gain to mitigate the impact on the dynamics of the angular velocity. Under the Nussbaum-based approach, an event-triggered control mechanism is proposed to reduce the update frequency of the control signals. A dynamic triggering threshold is designed to guarantee that the influence of intermittent feedback on system dynamics remains exponentially bounded. Different from the single-input and single-output dynamic systems, the PMSM system involves multiple inputs. Therefore, separate triggering conditions are designed for the direct and quadrature voltage components, achieving asynchronous intermittent feedback updates. The Nussbaum-based event-triggered design achieves precise tracking in the presence of coupling, parameter uncertainties, multiple unknown control coefficients, and load variation. Finally, simulation studies are presented to validate the effectiveness of the proposed adaptive event-based Nussbaum control method.

This paper investigates the mixed $��{�H�}_{�\infty �}�$ and passive filtering problem for discrete-time singular Markov jump cyber-physical systems (CPSs) with a dynamic event-triggered policy (ETP) under hybrid cyber-attacks. To conserve network bandwidth, a dynamic ETP is proposed and adopted. Owing to asynchronous phenomena between the modes of singular Markov jump CPSs and the designed filter, a hidden Markov model is utilized for the filter design. The filtering error systems are converted into time-delayed singular Markov jump CPSs with partially known conditional probabilities. By leveraging a series of linear matrix inequalities (LMIs), the criteria of stochastic admissibility for the filtering error system are derived. Moreover, the co-design of the resilient filter and dynamic ETP is proposed. Finally, two case studies are conducted to verify the correctness and validity of the proposed scheme.

In this paper, an adaptive dynamic threshold event-triggered fixed-time control is proposed for multiple uncertain robotic manipulators with position constraints on the basis of a dynamic gain function. First, a novel dynamic threshold fixed-time prescribed performance control (DFTPPC) strategy that mixes a time-varying function with fixed-time prescribed performance control, which allows the error to be reduced again, is designed. On this basis, the barrier Lyapunov function (BLF) is designed in combination with the hyperbolic tangent function to obtain the system state constraints. Second, the dynamic gain function (DGF) is designed to improve the sensitivity of the error in the feedback control and the anti-interference ability of the system. Third, a dynamic event-triggered mechanism (DETM) is designed by incorporating a time-varying scaling function and an exponentially decreasing term to not only reduce communication resources but also achieve stronger robustness. Finally, an analysis of the theory and comparative simulation experiments verifies that the proposed strategy is feasible.

This paper addresses the problem of adaptive robust attitude control for fixed-wing unmanned aerial vehicles subject to actuator faults, input dead zones, matched disturbances, and mismatched disturbances. An adaptive sliding mode disturbance observer is developed to estimate unknown mismatched disturbances and reduce conservatism in gain selection. Based on the reconstructed mismatched disturbances, a modified sliding surface is constructed, upon which a barrier function-based adaptive sliding mode controller is designed to compensate for lumped matched uncertainties and achieve accurate attitude tracking. A notable feature of the proposed controller is its ability to predefine the convergence neighborhood of the sliding variable while eliminating discontinuous terms, effectively mitigating chattering. Theoretical analysis establishes system stability, and numerical simulations validate the effectiveness of the controller.

Multi-agent systems (MASs) have attracted immense attention due to their extensive applications in various domains. However, their limitations, including external disturbance and limited resource constraints, remain under explored in contemporary research. To address these issues, an observer-based dynamic event-triggered robust circle formation protocol is proposed for the control of uncertain MASs under external disturbances. First, a full-order observer is designed to recover the state of the system when a partial state is unavailable in MASs. Second, by combining robust control and a dynamic event-triggered strategy, a new distributed circle formation control protocol is developed. This protocol ensures that the states of all agents converge to a desired equilibrium point while achieving the expected robust $��{�H�}_{�\infty �}�$ performance of uncertain MASs under mismatched uncertainties and external disturbance. Lastly, Zeno behavior is avoided by providing a positive minimum inter-event time for each agent, and the effectiveness of the proposed method has been verified via simulation.

This study epitomizes the problem of procuring targeted state tracking objectives while ensuring fault tolerance, disturbance suppression and reduced network load for nonlinear networked control systems represented based on interval type-2 fuzzy framework. In detail, a generalized extended state observer (GESO) is implemented for concurrent assessments of system states alongside providing the controller with estimations of actuator faults and external disturbances to counteract their detrimental effects on the system behaviour. Further, a model reference system incorporating fuzzy membership is included to construct the indented tracking control algorithm. Specifically, the state information extracted from GESO is propagated to the established tracking control protocol by means of adaptive event-triggered mechanism to reduce network load while ensuring peak performance. Altogether, the intended targets are achieved by devising a GESO-based adaptive event-triggered model-reference tracking controller. Notably, through the employment of fuzzy membership-linked Lyapunov–Krasovskii functionals, we set forth the necessary prerequisites for obtaining asymptotic stability for the configured system specified by linear matrix inequalities (LMIs). Certainly, to substantiate the utility of assembled controller, numerical findings are conveyed through graphical plots.