ISA transactions最新文献

Multirate sampled data (MRSD) dynamic systems are abundant in modern engineering systems. The inconsistent sampling rates cause data asynchrony and alter system properties, negatively impacting fault diagnosis with delayed and missed detections. In this paper, a fast rate fault detection scheme for dynamic systems is proposed, which is directly driven by MRSD. Firstly, the lifting technique is employed to transform asynchronous MRSD into single but slow rate sampled data. An auxiliary lifted output is constructed to compute a parity vector via subspace identification, facilitating a multi-dimensional diagnostic observer satisfying the Luenberger conditions. Then a post filter addresses causality constraint, allowing fast rate residual generation. Further, a fast rate residual evaluation scheme is developed. The effectiveness and superiority of the proposed scheme are demonstrated by a heating, ventilation and air conditioning (HVAC) example.

Distributed sliding mode controllers are proposed to address the optimal consensus problem for high-order nonlinear multi-agent systems under intermittent communication networks. Specifically, agents exchange information with neighbors only during non-overlapping time intervals, whereas all communication ceases completely during the interruption intervals. This feature significantly complicates the achievement of consensus. Under partial observability constraints, unmeasurable states are estimated via adaptive state observers. Additionally, a distributed optimization algorithm is employed to minimize the cost function and construct the optimal reference signal. To mitigate the problem of non-existent high-order derivatives, Hermite interpolation is adopted for optimal virtual signals. The distributed sliding mode controllers are designed to ensure that the tracking error of each agent converges to zero. Finally, stability analysis confirms the boundedness of the closed-loop distributed cooperative optimization framework, and simulation results verify the efficacy of the proposed method in practical scenarios.

This work presents a prescribed performance backstepping sliding mode control framework designed for a class of nonlinear systems with unknown disturbances. A key feature of the proposed method is the incorporation of a dynamic-gain neural network observer to handle system uncertainties and estimate unmeasurable states. In contrast with static gain observers, it adaptively adjusts its gain in real time, eliminating precise tuning. Moreover, an innovative integral nonsingular fast terminal sliding mode control (INFTSMC) strategy integrated with prescribed performance control (PPC) is developed to ensure that tracking errors adhere to pre-specified transient and steady-state requirements, enhancing reliability in practical applications. The control method manages dynamics, while the neural network observer compensates for nonlinearities, ensuring robustness under uncertainty. The system stability is analyzed via the Lyapunov theory. Simulation results demonstrate the effectiveness of the method.

In this article, precise control of a class of full-state constrained systems with uncertainty and unknown dynamics is studied. A neuroadaptive strategy is proposed to address unknown dynamics and parametric uncertainties. Moreover, disturbance observers are built to estimate unknown disturbances. Subsequently, a novel Lyapunov function incorporating an asymmetric prescribed performance function is constructed, ensuring that the tracking error converges to a small region within a fixed time. Furthermore, a neuroadaptive fixed-time prescribed performance controller with full-state constraints and disturbance compensation is developed, avoiding the use of tracking error transformation function in previous prescribed performance control and thus simplifying the controller design process. Moreover, dynamic surface technology is adopted to prevent the differential explosion problem generated in backstepping design. In addition, Lyapunov theory proves that the error system is locally ultimately exponentially bounded, and the asymmetric fixed-time prescribed tracking performance is guaranteed without violating any state constraints. Finally, the designed controller is tested by experiments.

The presented research effort offers a secure sampled hybrid control design for networked switched fuzzy systems. Precisely, we investigate the challenges posed by actuator failures, hybrid cyber attacks, time-varying delays and external disturbances over the finite horizon. A unified control design that effectively handles these simultaneous factors in a single framework is an area that warrants investigation. In this connection, a hybrid control law which comprises the robust and fault-tolerant control is established. One of the primary appealing aspects of the designed control law is that it employs robust control when the system is free of faults and switches to the fault-tolerant one when the system encounters a fault. Further, by considering the state information from ϑ(t_k) to ϑ(t) and ϑ(t) to ϑ(t_k+1), a two-sided looped functional is developed. Based on that, adequate conditions that promise the core objective of this examination are formulated in terms of linear matrix inequality. Apart from this, to escalate the controller functionality in regard to cyber threats, hybrid cyber attacks that comprise the deception and denial-of-service attacks are taken into account and Bernoulli distribution is utilized to govern the random characteristics of the attack. Additionally, to address the repercussions of the external disturbance, extended dissipative theory is deployed. In the closing part, two examples, including the single-link robotic arm model accompanied by the simulation findings, are presented to showcase the capability and applicability of the achieved theoretical outcomes and control design.

Accurate state estimation is essential in safety-critical systems yet remains challenging under unknown but bounded uncertainties. Conventional point observers, such as Kalman or Luenberger designs, often produce fragile estimates that degrade in the presence of disturbances and modeling errors. Interval observers (IOs), in contrast, enclose the true trajectories within clearly defined upper and lower bounds, providing guaranteed robustness, resilience to uncertainty, and formal safety assurances beyond what traditional methods can offer. This paper proposes a robust multi-step interval observer (IO) for discrete-time systems that combines a predictive structure with convex linear matrix inequality (LMI) design to deliver certified bounds with improved reliability and efficiency. The key innovation is a q-step predictive structure that aggregates system dynamics over a selectable horizon to enhance accuracy, disturbance rejection, and delay resilience. Observer gains are computed via a single convex LMI, ensuring non-negative error dynamics without coordinate transformations or iterative tuning. The effectiveness of the proposed framework is demonstrated on two challenging applications: a non-minimum phase (NMP) unmanned aerial vehicle (UAV) subject to wind disturbances and model mismatch, and a Lithium-ion battery management system (BMS) performing state-of-charge (SOC) estimation under sinusoidal load variations. In both cases, the proposed IO achieves tighter interval bounds (±0.1 % vs. ±2.5 %), faster convergence and reduced computation. These results confirm that the proposed method is computationally efficient, scalable and applicable for real-time deployment. The proposed IO framework opens promising new directions for IO design in aerospace, automotive and energy systems.

Regulating the output of unstable process is a great challenge particularly in the presence of process parameters uncertainties, nonlinearity, transportation delays and load disturbances. These processes are even more sensitive to input changes of ramp types. In this communication, a fractional order Smith predictor scheme is analytically designed to handle the unstable process undergoing above mentioned unavoidable conditions. The suitable values of the design parameters β, α and λ are determined by exploring the stability region and investigating system robustness towards process model uncertainties. The suggested control is also well implemented on a nonlinear jacketed continuous stirred tank reactor and the performance enhancement of 71.5 % is achieved under the perfectly matched condition. When the process parameters are perturbed, 73.9 % percentage improvement is observed. Performance indices such as Integral of Squared Error (ISE), Integral of Time-weighted Absolute Error (ITAE), Integral of Absolute Error (IAE), and Total Variation (TV) are also calculated.

Conflicts between driver and automation remain a key challenge in cooperative driving systems, as prolonged differences in intentions can degrade human experience, reduce trust, and compromise driving safety. In this paper, a conflict resolution approach is proposed for human-vehicle cooperative systems to minimize errors in trajectory tracking and conflicts between driver and automation. By considering driver-in-the-loop, a vehicle-driver-conflict model is introduced, and then an online reinforcement learning algorithm is developed to calculate the automated steering angle for cooperative systems, which can assist humans even in conflict situations. Based on Lyapunov's theory, the states of the vehicle-driver-conflict model are guaranteed to be uniformly ultimately bounded and the convergence of the control signal and cost function to their optimal values is verified. Finally, the effectiveness of the proposed method is demonstrated and compared with other methods through numerical simulations.

This paper examines the prescribed-time consensus of multi-agent systems under time-varying communication delays and Denial-of-Service (DoS) attacks. Considering a general class of DoS attacks with limited duration, a novel control protocol accompanied by time-varying node delays communication delays and integral action is proposed to guarantee the secure prescribed-time consensus. Moreover, we propose a controller to achieve prescribed-time consensus by utilizing the Artstein's reducing transformation, effectively addressing the challenges posed by time-varying delays. By using the comparison principle and Lyapunov stability theory, consensus convergence properties are analyzed, and sufficient criteria are obtained. Furthermore, a distributed prescribed-time observer is introduced to guarantee that all follower agents obtain the leader's state information within the prescribed time, even if only a subset initially has access. To conclude, a numerical simulation is offered to substantiate the robustness and implementation of our theoretical insights.

Unmanned surface vehicle swarms (USVS), comprising multiple unmanned surface vehicles (USVs), serve as critical platforms for a wide range of demanding marine operations. However, coordinating USVS poses challenges relating to flock cohesion and communication, as well as the adverse effects of uncertainty. Within this context, this paper introduces an event-based adaptive flocking control law for USVS to address these issues. Firstly, an integrated dual-mode potential function, combining a novel artificial potential function (APF) and a directionally consistent potential function (DCPF), is designed to enhance flocking cohesion while guaranteeing connectivity. Then, a boundary-locked event-triggered (BLET) mechanism effectively reduces the communication load with computable minimum inter-event time (MIET) and maximum triggering interval time (MTIT). Finally, a reinforcement learning-based echo state network (RLESN) is incorporated to compensate for unmodeled dynamics and external disturbances, thereby significantly improving the system's robustness. Simulation results and comparative analyses validate the effectiveness and advantages of the proposed methodology.