International Journal of Robust and Nonlinear Control最新文献

This paper investigates the fixed-time control of switched neutral Filippov systems using semi-intermittent and completely intermittent control strategies. Novel fixed-time stability lemmas are established for both semi-intermittent and completely intermittent control schemes. The semi-intermittent lemma incorporates an indefinite function and a unified exponent condition on the Lyapunov function, while the completely intermittent lemma relies solely on the unified exponent condition. These conditions offer significant advantages in terms of generality and applicability. The proposed stability lemmas are entirely new and generalize existing results, including non-intermittent and periodically intermittent stability lemmas. As an application, a switched neutral Filippov system is formulated, which extends previous models by incorporating greater generality. The fixed-time stability of the zero solution for the controlled system is rigorously analyzed under both discontinuous and bounded intermittent controllers. Notably, bounded controllers are shown to be more effective in reducing convergence time, thereby offering a faster stabilization approach. To validate the theoretical findings, numerical simulations of a drilling system modeled by the switched neutral Filippov system are presented, demonstrating the efficacy of the proposed control strategies.

This article is devoted to the problem of periodic event-triggered output feedback stabilization for non-strict feedback switched stochastic nonlinear systems (SSNSs) in the context of average dwell time (ADT) constraints. Specifically, a mode-dependent state observer is constructed to estimate the unmeasured system state. Striving to curtail the waste of communication resources (WCRs) of the considered system, this article builds a periodic event-triggered control (PETC) scheme, which constitutes an event-triggered controller and a discrete-time event-triggering mechanism (ETM). With the proposed scheme, only the information regarding the system output and the system mode at the sampling instant is exploited. To guarantee that the closed-loop system (CLS) is asymptotically stable (AS) in mean square (MS) despite the incidence of asynchronous switching, a novel correlation between the sampling period and the ADT is thus established. Finally, the effectiveness of the proposed scheme is confirmed using two practical examples.

The problem of global prescribed performance control (PPC) of the cascade nonlinear systems is investigated in this paper. It is concentrated on the cases of the state-dependent internal dynamics, inseparable state couplings, nonlinear virtual control coefficients, and totally unknown closed-loop dynamics, which render the existing solutions infeasible. To conquer this challenge, a flexible PPC approach is put forward in this paper, which not only yields uniform performance insurance under any initial condition of the closed-loop system but also leads to a continuous and amplitude-reduced control input with respect to the existing solutions. Besides, it shows a striking control simplicity, with no need for parameter identification, function approximation, disturbance estimation, derivative calculation, or command filtering. A comparative simulation on a ship model illustrates the theoretical results.

This article introduces a novel robust self-triggered tube-based model predictive control (TMPC) strategy for discrete-time uncertain linear systems affected by bounded additive disturbances. Unlike traditional methods that treat control design and triggering as separate tasks, the proposed framework integrates the co-design of control inputs and triggering intervals into a single optimization problem. This integration enables the simultaneous computation of the optimal control sequence and the maximum allowable triggering interval at each sampling instant, significantly reducing online computational complexity. Additionally, it introduces a hybrid control policy that transitions between open-loop and closed-loop control modes throughout the prediction horizon, effectively limiting uncertainty growth and ensuring feasibility over longer time frames. By incorporating the triggering interval directly into the cost function, the proposed method achieves a favorable balance between control performance and resource efficiency. Simulation results demonstrate the effectiveness of the approach in reducing communication frequency and computational load, while ensuring robust stability and the satisfaction of constraints.

This paper studies the resilient control problem for a class of nonlinear cyber-physical systems (CPSs) under sensor and actuator attacks. Different from those in the existing literature, both the additive and multiplicative cyber-attacks imposed on the sensor and actuator in this paper are unknown and time-varying. For the unavailable system states, an observer driven by compromised system input is constructed to estimate them. During the design procedure of backstepping control, some novel techniques are developed to deal with the additive and multiplicative sensor attacks. Additionally, two Nussbaum functions are introduced to solve the unknown control direction problem arising from uncertain sensor and actuator attacks. Meanwhile, a self-triggered mechanism (STM) is employed to minimize communication overhead, enabling more efficient data transmission and reducing the need for continuous real-time updates. Based on the state estimations and the compromised output signal, a new adaptive neural self-triggered resilient control strategy is proposed for this class of systems via the combination of the backstepping technique and the neural network-based approximation method. Stability analysis shows that all signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) despite the existence of cyber-attacks. Eventually, two simulation examples show that the proposed self-triggered resilient control strategy can facilitate the endogenous security of closed-loop systems under various sensor and actuator attacks and show the superiority in transfer efficiency and response time.

This article addresses the fault-tolerant control problem for underactuated autonomous underwater vehicles (AUVs) within the framework of path following control, aiming to effectively overcome the challenges posed by input delays, unknown actuator faults, and output constraints. The main contributions of this study include the following aspects: First, an asymmetric barrier Lyapunov function (ABLF) is constructed to analyze system boundedness, ensuring that the system output remains within predefined performance bounds and does not exceed desired limits. Second, an adaptive fault-tolerant controller is designed to maintain the boundedness of all system errors, even in the presence of unknown actuator faults. Third, an integral compensatory controller is proposed to effectively mitigate the effects of input delays. Finally, theoretical analysis and simulation results demonstrate that the proposed control strategy effectively overcomes the combined challenges of output constraints, actuator faults, and input delays for underactuated AUVs.

This paper studies the time-varying formation-containment control problem for multiple underactuated unmanned surface vehicles (USVs). A novel matrix-weighted consensus control strategy is proposed for multiple underactuated USVs to achieve time-varying formation containment. The area of the convex hull formed by the group leaders can scale down or up, which relies only on the motions of the first leader. Moreover, a series of dynamic matrix-value weights enables the group leaders to achieve a time-varying formation shape, and all followers ultimately converge into the convex hull formed by the group leaders. The proposed formation-containment control laws can be implementable in an arbitrarily local coordinate frame only using relative position measurements, which are favorable for many practical applications. Barrier Lyapunov functions and adaptive backstepping procedure are incorporated into the formation-containment controller design such that formation-containment errors can converge to a small neighborhood of zero in a prescribed time, where the convergence time can be specified in advance by the user. Simulation studies are performed to show the performance of the proposed formation-containment control laws.

This paper investigates load frequency control (LFC) of the Takagi–Sugeno (T-S) fuzzy reaction-diffusion power system under cyberattacks. First, a refined model is developed by introducing reaction-diffusion and nonlinear properties of the system, enabling a more realistic representation of spatially distributed dynamics. Second, a composite network attack model is proposed, which probabilistically integrates key features of various network attacks to better capture the stochastic behavior of adversaries. This formulation enhances the realism of cyberthreat representation and improves the model's resilience against diverse and unpredictable attack patterns. Then, an integral re-averaging (IRA) control strategy based on spatiotemporal measurement is proposed and integrated with an optimal sampling interval algorithm. This combination enables data simplification and resource optimisation while ensuring richer state acquisition and system stability. Furthermore, we present a looped function for Lyapunov–Krasovskii functionals (LKFs), which reduces the constraints and thus helps to derive more relaxed asymptotic stability criteria. Finally, numerical simulations demonstrate the feasibility and effectiveness of the proposed method under cyberattacks.

Generally, valve-controlled systems excel in tracking precision but suffer from high energy consumption, while pump-controlled systems exhibit high energy efficiency yet struggle with unsatisfactory response capabilities. This paper develops a pump-valve coordinated (PVC) system that employs robust adaptive control techniques integrated with fuzzy theory to address nonlinearities and uncertainties, achieving both high precision and high energy efficiency. An innovative dominant input control strategy is introduced to manage the coupling effects in the compound actions of the PVC system. Furthermore, valve operation may experience sticking and increased frictional resistance, whereas pump operation may encounter internal component wear and excessive leakage. Therefore, fault-tolerant control is incorporated to manage additional terms generated by actuator faults. Multiple fuzzy approximators are designed to handle various potential uncertainties in the system, allowing for more comprehensive control performance and broader applicability. The Lyapunov analysis illustrates that all states of the system are semi-globally stable, and the tracking error converges to a small neighborhood containing the origin. Finally, the effectiveness of the fuzzy adaptive fault-tolerant (FAFT) controller is validated through comparative simulations for the PVC system.

For complex nonlinear systems with modeling difficulties and partial observability (only motion signals available), their control performance is significantly limited by model uncertainties, unmeasurable disturbances, and critically, lack of prior knowledge of system dynamics. To overcome these limitations, this article proposes a widely applicable reinforcement learning (RL)-based output feedback control approach whose model-free nature and adaptive learning capability enable deployment across diverse nonlinear systems beyond the initial constraints. An integrated controller-observer design embeds an actor-critic architecture to achieve model-free observation and control. Within this framework, the actor dynamically compensates completely unknown unmodeled disturbances while the critic evaluates control performance. Crucially, an interactive mechanism is established where the actor directs observer-based state reconstruction, with reconstructed states feeding back to both actor and critic. Rigorous theoretical analysis demonstrates that the proposed controller ensures semi-global uniform ultimate boundedness (SGUUB) for all closed-loop signals. Simulations validate the effectiveness of the proposed control method and its superior steady-state performance under significant disturbances.