ISA transactions最新文献

To enhance the peak shaving flexibility of the thermal power unit (TPU) in the context of renewable energy grid connection, this article designs a flexible collaborative power generation scheme that combines TPU with vanadium redox flow batteries (VRFB) for coordinated operation. Firstly, a real-time decomposition technique for automatic generation control instruction combining improved complete ensemble empirical mode decomposition with adaptive noise, permutation entropy, and transformer network is developed to address the issue of delayed response on the boiler side. The designed real-time decomposition technology provides real-time instruction for fast tracking control of the system. Secondly, considering the complex disturbances and unmodeled dynamics in the TPU-VRFBs coupled system, an error-based active disturbance rejection control strategy with second-order disturbance compensation is proposed. By introducing two-dimensional state variables to precisely reconstruct the total disturbance of the system, the response speed and accuracy can be effectively improved. The simulation results show that under two typical cases, compared with traditional PID control (relying only on TPU), the proposed strategy reduces ITAE on the electric power channel by at least 57.1 %, verifying its superior dynamic performance and robustness.

In this paper, a disturbance observer-based adaptive event-triggered model predictive control (DAEMPC) method is proposed for a class of nonlinear systems with constraints and bounded disturbances. First, a disturbance observer is employed to actively compensate for disturbances. Leveraging the space decomposition technique, the disturbances are divided into the matched parts and the remaining unmatched parts. The matched disturbances are compensated using the pre-designed disturbance observer. To address the effects caused by the remaining unmatched disturbances, a bounded controller and an optimal controller with an adaptive event-triggered mechanism are respectively designed based on whether the system state resides within the stable region. The larger terminal stability estimation set is calculated based on the bounded controller. Furthermore, rigorous theoretical analysis is performed to prevent Zeno behavior. Finally, the simulation results for two numerical examples verify the effectiveness of the proposed algorithm.

This paper proposes a novel adaptive fuzzy nonsingular fast terminal sliding mode control (AFNFTSMC) scheme integrated with time delay control (TDC) for autonomous underwater vehicles (AUVs). The proposed controller enhances robustness and tracking performance by combining the model-free characteristics of TDC with the fast convergence and singularity avoidance of AFNFTSMC. TDC is employed to estimate and compensate for nonlinear dynamics and external disturbances in real time, without requiring an accurate mathematical model. To address the chattering problem inherent in conventional sliding mode control, a fuzzy logic system is integrated, enabling smooth control inputs through continuous adaptation. Using Lyapunov theory, the system is proven to possess global stability and finite-time convergence properties. The effectiveness of the proposed method is validated through numerical simulations using the Delphin2 AUV model under external disturbance conditions.

Modeling the nonlinear dynamics of autonomous surface vehicles (ASVs) is a complex challenge, driven by the intricate interplay of hydrodynamic effects and environmental uncertainties. In response to this challenge, this paper develops the system state and observation dynamics for ASVs using multivariate Gaussian process regression (MVGPR), then designs a learning-based model predictive control (MPC) scheme for trajectory tracking of ASVs. First, we introduce the application of MVGPR to model the ASVs dynamics, enabling accurate multi-input and multi-output correlation and uncertainty estimation, addressing the limitations of traditional Gaussian process regression (GPR) in high-dimensional settings. Based on the learned models, an unscented Kalman filter (UKF) is designed to improve state estimation accuracy through prior prediction and posterior updating, ensuring robustness even under unmeasurable states. Additionally, considering the impact of denial-of-service (DoS) attacks in communication networks, an MVGPR-based learning MPC framework is developed. By leveraging predictive capabilities, this framework eliminates the need for external compensators. The proposed method achieves robust and precise trajectory tracking while improving system stability under complex and uncertain maritime environments. Finally, the effectiveness of the proposed learning-based MPC algorithm is verified through comparative simulations and hardware experiments.

In this paper, a robust disturbance rejection control scheme is proposed for the trajectory tracking of flexible joint robots (FJRs) affected by multi-source disturbances and input saturation. The unknown time-varying disturbances, which are categorized into matched and mismatched, generally encompass uncertainties in internal parameters, coupling effects, unmodeled dynamics, and changing external environments. To address these disturbances, the mismatched disturbances are transformed into matched disturbances through the flatness method, eliminating the computational cost of estimating mismatched disturbances. Meanwhile, a generalized proportional integral observer (GPIO) is adopted to estimate the unmeasurable states and disturbances. In addition, to deal with input saturation, an anti-windup compensation auxiliary system is constructed. Finally, an output feedback control framework based on continuous sliding mode control (CSMC) is further designed. The proposed control approach facilitates asymptotic convergence of the tracking error and mitigates the chattering effect inherent in traditional sliding mode control (SMC), thereby enhancing both control and dynamic performance while satisfying input constraints. A detailed stability analysis of the closed-loop system is also presented. The feasibility and effectiveness of this approach are validated through comparison with a traditional disturbance rejection control scheme.

This paper investigates security synchronization in complex dynamical networks (CDNs) using a self-triggered impulsive control (STIC) with actuation delays and a quantizer. 1) By integrating network topology and Lyapunov function theory, we introduce a new self-triggered mechanism (STM), unlike traditional computationally intensive STMs, which embeds a fixed dwell time to eliminate Zeno behavior and avoid continuous state monitoring, solving the delay-induced desynchronization issue ignored by existing STIC studies. 2) A logarithmic quantizer is fused with STM, reducing communication resource consumption compared to unquantized STIC, while ensuring low relative quantization error. 3)Global Asymptotic Stability in the Mean (GAS-M) synchronization criteria are derived via the Lyapunov method and graph theory, explicitly linking STM parameters, network topology, and attack intensities. 4) A numerical example of Chua's circuits under deception attacks and denial-of-service (DoS) attacks is provided to demonstrate the effectiveness of the theoretical findings on GAS-M under random attacks.

To address the issue of path tracking and stability of four wheel steering and four wheel independent drive (4WS-4WID) autonomous vehicles under extreme conditions, this paper proposes a coordinated control method for four wheel steering (4WS) and direct yaw moment control (DYC). First, a sliding mode observer (SMO) is employed to estimate lateral forces and correct the cornering stiffness, and based on model predictive control (MPC) theory, an adaptive MPC path tracking controller is designed. Then, considering the nonlinear characteristics of tires, the 4WS stability controller and DYC stability controller with nonlinear lateral force are designed by sliding mode control (SMC). On this basis, with the control objectives of sideslip angle and yaw rate, the 4WS weight and DYC weight are coordinated through an extension method to obtain the optimal rear wheel angle and additional yaw moment, ensuring stability control while reducing unnecessary energy waste. Finally, the Carsim-Simulink co-simulation tests and controller-in-the-loop experiment verify the coordinated control strategy can significantly enhance both path tracking accuracy and vehicle stability under extreme conditions.

Unmanned aerial vehicle (UAV) flight control systems are exposed to diverse security threats, including environmental disturbances, component faults, and cyber-attacks. Existing fault-tolerant control methods lack generalizability in representing such heterogeneous perturbations, particularly adversarial cyber-attacks, due to their reliance on type-specific modeling assumptions. In this study, an Information Flow Metamodel-Based Adaptive Approximate Modeling (IFM-AAM) framework is proposed to address this issue. The framework establishes an Information Flow Metamodel (IFM) as a high-level structural abstraction for approximate modeling. IFM represents heterogeneous perturbations as symbolic anomalies within a unified information flow structure, enabling consistent propagation analysis and impact computation across diverse perturbation sources. The IFM design supports flexible instantiation of domain models (DMs) at appropriate abstraction levels, addressing the computational challenges of modeling densely coupled flight control systems. In addition, a lightweight Actor-Critic reinforcement learning (RL) mechanism is integrated into the instantiated DMs for real-time adaptive tuning of control parameters, further enhancing modeling fidelity under perturbations. Comparative simulations against PX4 Autopilot demonstrate that DMs constructed by IFM-AAM effectively approximate control performance across nominal and diverse perturbation scenarios. These results confirm the effectiveness of our unified representation and propagation analysis for heterogeneous perturbations. Moreover, validations on an onboard computer verify the real-time performance and low computational overhead of DMs.

In the context of three-dimensional angle-of-arrival (3D-AOA) target tracking, this study proposes a state-constrained and noise-separated pseudo-linear Kalman filtering (SC-NS-PLKF) algorithm to address nonlinear filtering challenges. Whereas existing bias-compensated (BC), instrumental-variable (IV) and unbiased (UB) PLKF methods only correct the pseudo-linear bias, SC-NS-PLKF achieves high-precision unbiased estimation with enhanced algorithmic stability. Specifically, this method (i) derives a new pseudo-linear measurement model through nonlinear equivalent transformation and noise separation; (ii) employs auxiliary filtering to supply the target position required by NS-PLKF and constructs an ellipsoidal constraint domain that guarantees divergence prevention; (iii) provides a rigorous proof of bounded estimation error and includes a complexity analysis to validate computational efficiency. Extensive Monte-Carlo simulations demonstrate significant gains in accuracy and stability compared to state-of-the-art methods.

In this study, an innovative adaptive sliding mode fault-tolerant control approach is developed for an over-actuated vertical takeoff and landing (VTOL) fixed-wing unmanned aerial vehicle (UAV) with the capability to suppress overestimation of adaptive parameters. This method is designed to effectively address model uncertainties and actuator faults without relying on any previous information regarding the specifics of faults or the boundaries of uncertainties. An innovative adaptive sliding mode control (SMC) mechanism is designed which can autonomously adjust to compensate for the unpredictable nature of these challenges, ensuring the stability and reliability of the UAV system under various operational conditions. Taking into account the over-actuated characteristics of the studied VTOL UAV, a control allocation module is further designed to efficiently distribute the control signals produced by the adaptive SMC scheme. It is noteworthy that the designed adaptive control approach can effectively prevent the overestimation of adaptive parameters, thereby reducing the occurrence of undesired control chattering. Finally, the superiority and efficacy of the designed control technique are convincingly illustrated through an extensive range of comparative hardware-in-the-loop simulation tests.