IET Control Theory and Applications最新文献

Wind energy conversion systems (WECSs) based networked microgrids has been widely used in recent years. The mean square error (MSE) metric can yield imprecise outcomes if measurement data is polluted by non-Gaussian disturbances or extreme values. To address this problem, we propose a new robust square root cubature Kalman filter (SRCKF) method called maximum correlation criterion (MCC)-SRCKF, which incorporates MCC into the SRCKF framework of dynamic state estimation. In MCC, by considering the high-order moments of the error distribution, it demonstrates anti-interference ability against non-Gaussian noise, thus serving as an ideal alternative in the MSE cost function field of SRCKF. Furthermore, within the framework of SRCKF, this study introduces statistical linear regression models and non-moving point iteration strategies to solve the optimal state estimation under MCC conditions. Therefore, a historical measurement triggered DoS attack model is proposed from the attacker's perspective, aiming to destabilise the WECS-based networked microgrids. The security conditions of the power system under such attacks are obtained. The proposed method is validated numerically using an IEEE 39-bus system, and the results demonstrate its effectiveness and superiority.

This paper addresses the time-varying formation control issue of networked multi-agent systems with actuator fault and random communication constraints. A time-varying formation active fault-tolerant predictive control scheme is proposed. First, a composite state observer is designed to jointly estimate the system state and actuator fault. Then, an active fault-tolerant predictive control algorithm is developed to generate a control prediction sequence such that actively compensating for actuator fault and random communication constraints. The design principle of control parameters is obtained by deriving the system stability condition. Finally, numerical simulations and practical experiments are carried out to verify the effectiveness and feasibility of the proposed control scheme.

This paper delves into the application of robust optimal control theory for voltage regulation in DC microgrids with uncertain ZIP loads. The primary challenge in DC microgrids with local ZIP loads is addressed through a two-phase approach encompassing classical robust control and data-driven control methodologies. Initially, the robust control problem for voltage regulation is tackled using an undiscounted optimal approach. Subsequently, the classical structure of the proposed robust optimal control scheme is converted into a data-driven control strategy employing a reinforcement learning (RL) algorithm. Given the system's unmatched uncertainties, a virtual control input is necessary during the robust control problem-solving process, preventing the extension to a model-free control strategy. By converting the unmatched uncertainties into matched ones in the first phase, a data-driven robust control strategy is achieved using the RL-based algorithm in the second phase. The simulation results which are obtained using MATLAB/SimPowerSystems toolbox showcase the effectiveness of the data-driven approach in achieving stability and adaptability in uncertain DC microgrid environments.

This paper investigates the stability analysis of second-order systems with digital proportional-integral-derivative (PID) controller based on the impulsive system conversion. The evaluation method of digital PID controlled systems is optimised to more precisely determine its maximum acceptable sampling period. This approach reduces the communication burden under the expected performance. Firstly, since the sampled-data PID (SDPID) controller represents a standard form of the digital PID controller, the second-order system using SDPID controller is converted to impulsive form through the introduction of a virtual variable. The system conversion method is further applied to the second-order system with polynomial uncertainty to demonstrate its effectiveness. Next, an impulsive-type looped-functional is established for the aforementioned impulsive system to lose the functional's decreasing constraint. Based on the aforementioned modifications, several less conservative criteria are developed. Based on these criteria, a more precise evaluation of system's admissible range of sampling periods is achieved. This results in a reduction in the utilisation of communication resources. Finally, the numerical example and experimental test are performed to validate the superiority and the effectiveness of the developed method.

This paper discusses fault-tolerant control using recursive Gaussian processes (RGP) and observer-structured robust output feedback controllers. The proposed method can detect variations in plant dynamics based on estimated plant state using RGP. The paper gives a design method for robust dynamic output feedback controllers based on generalized plants considering uncertainties due to some faults. When output feedback control is required, running RGP that use the plant state is complicated. In addition, the existence of the uncertainties makes conventional observers unreliable. These issues motivate us to adopt observer-structured controllers, which are obtained from a priori designed dynamic output feedback controllers through only appropriate state transformations without control performance degradation. Running this scheme online makes it possible to guarantee robust stability for plants after dynamics changes due to some faults. Two numerical examples of a web server model and an airplane model illustrate the effectiveness of the proposed method.

In modern information warfare, single-agent and multi-agent systems (MAS) play a critical role in achieving integrated combat capabilities. This study examines agent-based game strategies in airborne networked systems, focusing specifically on the air-fleet kill chain—the central framework that unifies fighters, early-warning aircraft, and missiles into a cohesive, intelligent system. We analyse how MAS mitigate the complexity, uncertainty, and adversarial dynamics of aerial combat by optimising detection, decision-making, and strike efficiency through networked collaboration. Two representative scenarios are presented: (1) an AI-driven fighter (ALPHA AI) using genetic-fuzzy tree algorithms to surpass human pilots, and (2) adversarial multi-agent reinforcement learning (MADDPG) in OpenAI's simulation suite. We then propose a systematic MAS-based kill-chain optimisation design that integrates deep reinforcement learning, Bayesian inference, and tactical decision frameworks. Simulation results demonstrate enhanced coordination, real-time adaptability, and optimised damage probabilities in both single- and multi-agent confrontations. Our findings establish a theoretical foundation for transitioning from rule-based to AI-driven system-of-systems warfare in next-generation aerial combat.

The ambient pressure is an important indicator for ensuring stable and efficient testing of the aeroengine in the high-altitude test (HAT) facilities. With the complex and quickly changing test environment and storage limitation of the underlying hardware, processing the pressure signals to access its filtering and differentiating signals to participate in designing feedback controller becomes difficult. The integration step of a differentiator algorithm, which is typically closely related to the sampling period, affects the performances of filtering and differentiating. In this work, a feature recognition based adaptive algorithm is proposed to enable the tracking differentiator (TD) by adaptively selecting an appropriate integration step in real-time. In particular, the sampled signals are transformed into images and analyzed by a proposed feature recognition algorithm. This algorithm can transform the real-time signals into an indice of system's dynamic characteristic. Simulation and experiment results show that the proposed adaptive TD algorithm can effectively improve the filtering and differentiating performances compared with the original TD, and meet the signal processing requirements of HAT facilities.

This paper investigates prescribed-time (Pre-T) robust consensus tracking for general linear multi-agent systems (LMASs) subject to uncertainties and disturbances. Such uncertainties and disturbances, which are common in practical systems, often hinder the achievement of Pre-T convergence. To address this challenge, a class of time-varying scaling functions is introduced as part of the observer and controller gains, ensuring robust consensus tracking of the closed-loop system within the prescribed time while mitigating the adverse effects of disturbances on tracking performance. Building on these scaling functions, a novel distributed Pre-T observer is developed to accurately estimate the leader's state for each follower at an arbitrarily chosen prescribed time $�T_1$. The proposed Pre-T observer relies solely on local interaction information among neighboring followers and does not require global knowledge of the entire LMAS. Using the estimated leader's state, a Pre-T controller with some robustness terms is designed for each follower to counteract the negative impacts of system uncertainties and external disturbances. Furthermore, sufficient conditions for the existence of feasible control parameters are derived to guarantee that Pre-T robust consensus tracking with a specified $�H_{\infty }$ performance index is achieved at a prescribed settling time $�T_2$ and maintained thereafter. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed approach.

For linear multivariable systems, we consider the minimum-time output control problem with explicit constraints on the inputs' intensity and piecewise-constant controls. We face the problem by imposing that each output passes through a given set of points. The proposed solution consists in repeatedly solving a system of linear equations until all constraints on inputs' intensity are satisfied. The proof of a local inverse relationship between the control effort and the control time is given, which provides a convergence guarantee for the algorithm. Numerical simulations performed on a well-known benchmark are reported to assess the effectiveness of the proposed method.

In this paper, an event-triggered adaptive improved terminal sliding mode controller is proposed for addressing the consensus tracking problem of multi-manipulators system under the weak communication networks. Firstly, an improved terminal sliding surface is designed to reduce the steady-state error of system. Then, by devising a variable-rate reaching law for sliding surface, the control chattering is greatly weakened. Furthermore, a trigger mechanism with time-varying threshold is incorporated into the designed controller to adjust the update frequency of the control law for conserving communication resources. This strikes a balance between tracking accuracy and resources conservation. Eventually, the tracking performance of the weak-connected system is greatly improved under the proposed algorithm. Based on Lyapunov stability theory, it is proven that the consensus tracking error of the system asymptotically converges to zero. Simulation results verify the effectiveness and performance improvement of the proposed scheme.