International Journal of Robust and Nonlinear Control最新文献

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.

This paper is devoted to the dynamic event-triggering anti-disturbance control of nonlinear multi-agent systems under random deception attacks. An intermediate observer is built for each agent to simultaneously estimate both the system states and unknown disturbances. A novel dynamic event-triggering condition based on the variable threshold is designed to minimize communication resources, which comprises both instantaneous and historical triggering errors. A resilient security control law and a corresponding sufficient condition, expressed as linear matrix inequalities, are developed to guarantee the bounded stability of the resultant closed-loop system. Ultimately, simulations are performed to evaluate the effectiveness of the provided scheme.

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.

In this article, an adaptive finite-time neural optimal control technique is proposed for stochastic nonlinear systems (SNSs) by applying the reinforcement learning (RL) technique of the identifier-critic-actor architecture. Radial basis function neural networks (RBFNNs) are used to estimate the uncertain functions in the considered system. Unlike a deterministic system, SNS control must account for stochastic disturbance in the process of system control. Therefore, the finite-time update rules for the critic and actor in SNSs are formulated based on the negative gradient of a positive function, which is derived from the partial derivatives of the Hamilton–Jacobi–Bellman (HJB) equation discussed herein. Using the discussed approach, the RL algorithm is simplified, and two preconditions are released: Persistent excitation and known dynamics. The controller design method proposed in this article not only simplifies the control scheme but also solves the singularity problem that occurs in the traditional backstepping optimal control techniques. Stability analysis reveals that the tracking error tends toward a vicinity of zero, while the boundedness of all system signals is ensured within a finite time. Finally, a simulation example involving an intelligent ship autopilot was provided to verify the effectiveness of the theoretical results. Meanwhile, it can also demonstrate the effectiveness of the academic achievements.