International Journal of Robust and Nonlinear Control最新文献

This article proposes an online learning-based event-triggered model predictive control (OLEMPC) scheme for constrained nonlinear systems with state-dependent uncertainties. The scheme incorporates both the nominal and the learned models to ensure favorable theoretical properties during online learning. A composite measurement-triggering strategy is devised to reduce the number of state measurements as well as solving the optimization problems. This strategy attenuates the conservatism in measurement and triggering through combining the event- and self-triggering approaches. By implementing the algorithm, both state measurement and triggering frequency further decrease with the online refinement of the prediction model, and the prediction horizon adaptively shrinks as the state approaches the terminal region. It is shown that the feasibility of the optimization problem and stability of the closed-loop system are guaranteed. Simulation results verify the effectiveness of this scheme in ensuring closed-loop performance and alleviating computational burden.

This article presents an event triggered control approach for discrete-time T-S fuzzy systems, which integrates the homogeneous polynomial method and a dynamic event triggering mechanism. First, a kind of discrete-time loop functions and the dynamic event triggering scheme based on periodic sampling are constructed to establish stability conditions with known system information. Second, by introducing the homogeneous polynomial method to relax the stability criteria, the sampling interval is increased and the system conservatism is reduced. Additionally, an integrated framework for collaboratively designing the event triggering functions and controllers is proposed, which reduces the complexity of sampling transmission while ensuring system stability. Finally, numerical and practical examples are used to illustrate the feasibility and effectiveness of the proposed method.

This article investigates a multiplayer nonzero-sum differential game (MNDG) with switching topology and input delay. The Hamilton–Jacobi–Isaacs (HJI) equation is used to derive the optimal control strategies. To solve the issue of switching topology, the control strategies are decoupled with the communication topology. Then, the stability of the system can be achieved regardless of changes in communication topology with the designed control strategies. This does not require a restriction on the dwell time, making the system stability conditions more relaxed. The Lyapunov–Krasovskii functional (LKF) is constructed to prove the stability of the system in the presence of time-varying input delay. To cope with the real-world situation, the input delay is considered to be disturbed by a time-varying perturbation in this article. With these treatments, the system is more in line with real-world applications. Simulation examples are given to validate the efficiency of the presented methods.

In this article, the reachable set estimation problems for a class of singular discrete-time nonlinear systems (SDNS) with time-varying delay (TVD) are presented. First, we employ a Lyapunov function scheme and utilize zero-type free matrix equations. Based on this, a criterion is derived in the form of linear matrix inequalities (LMIs) to guarantee that the considered system to be regular, causal, and the state trajectory $��{�x�}_{�1�}�$ of the system is bounded. Second, by developing the singular systems decomposition technique into slow and fast subsystems and using the introduced auxiliary lemma, we establish that the $��{�x�}_{�2�}�$ state trajectory of the fast subsystem is also bounded, and the reachable set of singular nonlinear system is bounded within the ellipsoid. Finally, Numerical examples are presented to illustrate the effectiveness of the proposed method.

This article proposes a second-order discrete-time sliding mode controller (DSMC) by only using sampled-data information of the system output. On one hand, different from the continuous-time sliding mode control, the finite-time stability of the closed-loop system under DSMC is usually not kept due to the limited sampling frequency. On the other hand, in the output feedback framework, the second-order sliding mode observer cannot independently estimate the internal state of the system if there exist unknown mismatched term and unknown time-varying coefficient of the control input. To solve the above problems, a joint design strategy of second-order discrete-time sliding mode observer and sliding mode controller based on the “non-separation principle” is proposed. The stability and robustness of the closed-loop system is analyzed based on the Lyapunov design method. Finally, simulation example verifies the correctness of the theory.

This article discusses the finite-time boundedness (FTB) issue and $��{�L�}_{�2�}�$-gain analysis for switched stochastic systems, particularly under the dual influence of DoS attacks and false data injection attacks, by employing a novel event-triggered mechanism. Different from the moment calculation method, a probability-based FTB is studied which offers more pertinence. To obtain a less conservative condition of FTB, a switched Lyapunov function is constructed. Additionally, a memory-based dynamic event-triggered mechanism is designed to reduce the amounts of triggering and mitigate the state response fluctuations. Based on the incomplete information above, state feedback controllers are devised to satisfy stochastic FTB, ensuring the successful attainment of finite-time $��{�L�}_{�2�}�$-gain. Sufficient conditions are cast into a convex optimization problem by LMIs which can be solved easily. Finally, a compared numerical example and an RLC series circuit are adopted to demonstrate the availability of the theoretical results.

The cooperative localization technology of multi-AUV has been a research hotspot in the area of underwater localization in recent years. Aiming at the situation that the localization accuracy is reduced and diverging due to uncertain information such as outliers and unknown time-varying noise in the measurement information in the dynamic topology environment, a new measurement filtering module is designed by using factor graphs. First, the K-means algorithm is introduced based on the cooperative localization algorithm based on factor graphs in the dynamic environment, and an outlier filtering module is designed to filter measurement outliers. After outlier correction, the EM algorithm is introduced to resolve the issue of unknown measurement noise variance caused by unknown time-varying noise and design an adaptive filtering module. Finally, to address the issue of degraded real-time performance due to a large number of system communication nodes and diverse quality, the paper introduces the CRLB algorithm, distance evaluation factor, and measurement smoothing factor to design a node optimization module. Finally, these modules are integrated into the cooperative localization system through factor graphs. Through experimental analysis, the proposed algorithm effectively reduces outliers and unknown time-varying noise in measurement information, as well as the impact of a large number of system nodes on localization accuracy and real-time performance. In addition, the proposed algorithm also shows strong robustness in the face of extreme underwater environments.

This article proposes an adaptive predefined-time event triggered fuzzy tracking control strategy for strict-feedback nonlinear systems with unknown control direction and mismatched disturbances by applying command filters. The Nussbaum-type function is employed to tackle the unknown control direction problem, and the fuzzy logic systems (FLSs) are applied to model the nonlinear functions. And then, command filters are adopted to deal with the problems of complexity explosion, and the event-triggered technique is exploited to conserve communication resources. With the help of backstepping mechanism and predefined-time stability theory, the constructed adaptive control scheme is proposed to ensure all signals in the closed-loop system are bounded and that the tracking error converges to a residual set. Finally, a simulation example proves the validity of presented control mechanism.

This paper proposes a method to improve the identification performance of linear dynamic systems by utilizing knowledge from samples of non-identical distribution systems. Traditional identification methods heavily rely on the quality of the dataset, such as sample length and noise level, which constrains their performance due to the assumption of identical distribution. Motivated by the concept of sample-based transfer learning, we propose a sample transfer identification method and derive the condition to avoid negative transfer. We develop a fast iterative transfer identification method for low storage costs, considering the computational burden imposed by the sample size from the source system. Additionally, based on the fast iterative transfer identification method, considering the need to update the current measurement data model in real time, a fast iterative online sample transfer identification method is explored. Through simulations, we validate the effectiveness and superiority of the proposed methods. The results show that sample transfer identification is superior to non-transfer identification and fast iterative sample transfer identification effectively reduces the calculation amount when dealing with low quality measurement data.

This paper investigates the event-triggered generalized extended state observer-based estimation issue for a class of nonlinear networked control systems with unmodeled dynamics, external disturbances and multi-channel attacks. In order to resist different forms of attack threats on multiple communication channels from sensors to the observer, Markov chain is introduced to describe the stochastic switching or jumping behavior between different attack modes. Considering the limited network resources, the measured outputs are transmitted to the observer only when the triggering conditions are met. Moreover, the parameters modeled by the Bernoulli process are adopted to help analyze potential random gain variations of the generalized extended state observer. By employing Lyapunov stability theory and stochastic systems analysis, sufficient conditions are derived to ensure that the augmented system is exponentially bounded in mean square, and the expected observer gains are further determined through linear matrix inequalities. Finally, a numerical example and a simulation related to the RLC series circuit system are conducted, illustrating the proposed method's effectiveness.