IET Control Theory and Applications最新文献

During aircraft operations, pilots rely on human-machine interaction platforms to access essential information services. However, the development of a highly usable aerial assistant necessitates the incorporation of two interaction modes: active-command and passive-response modes, along with three input modes: voice inputs, situation inputs, and plan inputs. This research focuses on the design of an aircraft human-machine interaction assistant (AHMIA), which serves as a multimodal data processing and application framework for human-to-machine interaction in a fully voice-controlled manner. For the voice mode, a finetuned FunASR model is employed, leveraging private aeronautical datasets to enable specific aeronautical speech recognition. For the situation mode, a hierarchical situation events extraction model is proposed, facilitating the utilization of high-level situational features. For the plan mode, a multi-formations double-code network plan diagram with a timeline is utilized to effectively represent plan information. Notably, to bridge the gap between human language and machine language, a hierarchical knowledge engine named process-event-condition-order-skill (PECOS) is introduced. PECOS provides three distinct products: the PECOS model, the PECOS state chart, and the PECOS knowledge description. Simulation results within the air confrontation scenario demonstrate that AHMIA enables active-command and passive-response interactions with pilots, thereby enhancing the overall interaction modality.

This article presents an innovative approach for distinguishing replay attacks from faults in hybrid systems. To develop the idea at first, a novel technique for replay attack detection, which differentiates it from typical system faults, is introduced. This is achieved through a tactical combination of stochastic coding and a window-based comparison mechanism, setting a new standard in hybrid system security. In the realm of fault detection, robust L₂-L_∞ adaptive observers are employed for precise mode detection, allowing for an accurate assessment of the system's operational state. Additionally, robust H_∞ Sliding Mode Observers are utilized to identify abnormal behaviours in the system's output, further solidifying the fault detection capabilities. A significant enhancement in the proposed approach is the integration of a Luenberger observer with the Butterworth low-pass filter. This novel addition not only refines the filtering process but also contributes to the overall reliability and accuracy of the system. The efficiency and versatility of these methods are demonstrated through their application to a four-tank hybrid system. This practical simulation showcases the adaptability of the approach to real-world scenarios, highlighting its potential in diverse applications within the field of hybrid systems.

Aiming at cooperative navigation of multi-aircrafts, an adaptive EKF-SLAM algorithm for non-fixed landmark is proposed. First, the aircraft motion model and angle measurement model are established. Second, the fixed landmark cooperative EKF-SLAM algorithm is established. Furthermore, a distributed EKF-SLAM algorithm is designed. Then, the adaptive EKF-SLAM algorithm with non-fixed landmark is proposed. Finally, simulation results verify the effectiveness of the proposed algorithm.

This article discusses finite-time lag bipartite (FETLB) synchronization of double-layer networks with non-linear coupling strength and multiple time delays. The cooperative and competitive interactions between nodes are considered based on signed graphs. To address the non-linear couplings strength, the T-S fuzzy logic theory is used. An intermittent control approach is introduced to achieve FETLB synchronization, effectively minimizing control costs. Moreover, by the Lyapunov functional method, we derive criteria for achieving FETLB synchronization and provide estimations for the synchronization settling time. In addition, numerical simulation affirms the validity of the theoretical findings, showcasing the practical application of the synchronization results in secure communication.

In this work, a novel energy management control framework is developed for hybrid electric vehicles (HEVs) driving in car-following scenarios. In order to enhance the energy efficiency while maintaining the driving safety, a hierarchical control approach consisting of an upper level speed tracking control scheme and a lower level energy management control strategy is proposed. For the upper level tracking control system, an iterative learning model predictive control (ILMPC) scheme is developed to guarantee the tracking performance and the driving safety simultaneously. Additionally, a model predictive control (MPC) algorithm is adopted at the lower level to optimize the torque distribution in real-time based on the driving cycles generated by the upper level control system. With the proposed hierarchical control framework, HEVs are able to improve the energy efficiency significantly by taking the advantages of the operational repeatability. The convergence of the proposed control strategy is analyzed rigorously, and its effectiveness is illustrated through numerical simulations.

Cascade control structures with inner current and outer speed loop, usually utilizing PI controllers, are widely used for electrical drives to meet high-quality requirements. The present paper introduces design guidelines via pole placement for achieving control gains both in continuous and discrete time preserving the original cascade control structure with the initially applied controllers. This paper also presents an additional prefilter design to eliminate the undesirable effect of the reference integrals. The paper presents closed-form expressions for the control gains as the function of desired damping ratios, the natural angular frequency of the control loop, and machine parameters to achieve the desired system dynamics. The proposed design methodology is demonstrated on brushed DC and permanent magnet synchronous machines.

This study focuses on a model reference adaptive control method that ensures identical orientation outputs for different prototypes of a two-axis gimbal produced in mass production. In this method, unlike traditional MRAC structures, an MRAC structure is used in conjunction with state feedback control. First, the reasons for the need for an adaptation mechanism in gimbals and why Model Reference Adaptive Control (MRAC) alone won't be sufficient have been discussed. In the first section, various applications of MRAC have also been mentioned. Then, the mathematical foundation of the model reference adaptive controller used in this study is elaborately explained, followed by stability analyses. In the next step, an ideal reference model exhibiting desired behavior and a real system model with different dynamics are created in a simulation environment. This allows a comparison of the adaptation capabilities of only MRAC and MRAC+State Feedback controllers. Based on the information gathered in this section, the recommended approach in the article is tested on a real gimbal system, and the results are shared. The obtained results demonstrate that the MRAC+State Feedback control structure significantly reduces the error in the gimbal's orientation response compared to the reference model.

In this article, the stabilization of coupled time fractional parabolic partial differential equations subject to external disturbances is investigated. By using sliding mode control method and backstepping approach, a boundary state feedback controller is designed to reject the matched disturbance and achieve the Mittag-Leffler input-to-state stability of closed-loop system. The existence of the generalized solution to the closed-loop system is proven by Galerkin approximation scheme. Simulations are presented to illustrate the validity of our theoretical results.

This article investigates the distributed fault-tolerant formation control problem of a multi-leader system with process faults and system uncertainties. This study guarantees all the followers to achieve a specified time-varying formation and track the combination of multiple leaders. By treating faults as a special type of states, an enhanced system is constructed. Furthermore, an intermediate variable observer is designed, but the observer matching condition is not necessary. The observer is used to estimate faults and system uncertainties, for reconstructing the formation controller. The observer gains are determined by solving linear matrix inequalities. Subsequently, controller gains are designed based on Lyapunov theorem and adaptive techniques. Finally, the proposed method is verified through a simulation example.

This paper investigates the challenges associated with the velocity consensus and the lane following in heterogeneous automated vehicles considered as agents of a multi agent system. A cloud-based distributed model predictive control structure is proposed to address the distributed intrinsic of such systems. Moreover, for situations that the cloud-based control signal is not reachable, backup controllers based on explicit distributed model predictive control are developed. The proposed cloud-based controller is implemented in the Amazon cloud servers to show the practicality of the proposed approach. Simulation results are presented to demonstrate the effectiveness of our methodology in terms of handling practical limitations of connected and automated vehicles.