IET Control Theory and Applications最新文献

Heating, ventilation and air conditioning (HVAC) chilled water systems offer significant potential for energy saving and reinforcement learning (RL) methods have been extensively studied and validated for optimizing HVAC energy consumption. However, RL's low sample efficiency and reliance on randomized exploration limit its practical application. To enhance the robustness and stability of RL-based energy optimization methods, an RL optimization approach based on a mechanism-data hybrid-driven model is proposed, derived from the MBPO (Model-based policy optimization) scheme. Firstly, a novel end-to-end HVAC chilled water system model is developed to serve as the foundation for the hybrid model design. Second, a hybrid-driven RL environment model framework is introduced, combining a mechanistic model with a probabilistic neural network. The mechanistic component provides generalization capabilities, while the data-driven component offers adaptability. Third, improvements to MBPO are proposed, including double policy optimization and adaptive branch rollout, further to enhance dynamic environmental adaptability and model utilization efficiency. Finally, comparative and ablation experiments conducted using both simulation environments and measured data demonstrate that the proposed method achieves higher learning efficiency and improved robustness.

This paper investigates dynamic instantaneous-integration event-triggered control for networked non-linear rail vehicle suspension systems over the train communication network (TCN). The primary goal is to ensure the desired suspension performance while efficiently utilizing the TCN resources. Firstly, a 2-DOF non-linear model is developed, with the non-linearities approximated by a neural network using a dissipativity-learning method. To efficiently conserve TCN resources during signal transmission, this paper proposes a novel dynamic instantaneous-integral fusion event-triggered (DIFT) scheme. Compared with event-triggered schemes based solely on instantaneous or accumulated state errors, the DIFT condition uses a fusion of the instantaneous error and the integral of the state error and compares it to a dynamic threshold that depends on the sampled state at the triggering instant and an auxiliary dynamic variable. This integrated approach ensures robust, Zeno-free event-triggered updates while maintaining desired control performance and communication efficiency. Furthermore, stability and dissipativity conditions based on a looped Lyapunov function are proposed to guarantee the stability and dissipative performance of the closed-loop suspension system. Then, the desired controller, the event-triggered parameter and the updating law for weight are co-designed. Simulation experiments are conducted to validate the effectiveness of the proposed method.

Tiltrotor UAV is a hybrid aircraft that enjoys the advantages of both the conventional fixed-wing and helicopter in a single platform. The design of a practical and efficient flight control system for this novel aircraft with a complex configuration is still a challenge. This paper proposes a practical fixed-time control scheme for a novel distributed tiltrotor UAV to improve flight stability. Based on the analysis of the control principle, the dynamic model and control allocation are derived to address the coordinated control problems with redundant actuators. A fast fixed-time sliding mode attitude controller is subsequently designed to improve the response speed and robustness of the control system. Furthermore, a smooth mode transition strategy is developed to guarantee the stable conversion between fixed-wing and helicopter modes. Experiments are conducted under different modes to investigate the effectiveness of the proposed flight control scheme.

This study presents the development of a robust second-order sliding mode controller (SOSMC) that incorporates position error constraints into the control design. To achieve this, a barrier Lyapunov function (BLF) is employed, enabling the enforcement of predefined bounds on tracking errors throughout the system's evolution. This approach ensures that errors remain within previously known limits, thereby improving safety and reliability in practical applications, particularly robotic systems. The proposed control scheme guarantees the existence of a sliding mode and achieves exponential convergence of the tracking errors, even in the presence of bounded disturbances and model uncertainties. Hence, the main contribution of this study is the integration of the BLF into the SOSMC framework, which not only maintains robustness but also addresses the critical issue of constraint satisfaction. This is often overlooked in traditional sliding mode designs. The effectiveness and improved performance of the proposed controller are validated through simulation studies conducted on a three-degree-of-freedom robotic manipulator and through experiments on a six-degree-of-freedom robotic manipulator. Comparative results demonstrate that, unlike a conventional SOSMC without error constraints, the proposed controller successfully maintains position errors within the specified limits while preserving fast convergence and robustness. These findings highlight the significant benefits of incorporating barrier Lyapunov functions in sliding mode control strategies for systems with strict performance and safety requirements.

Space robotic systems are essential for on-orbit servicing missions, including satellite refueling, in-space assembly of large structures, and active debris removal. This paper presents a robust, task-specific optimal trajectory planning framework for dual-arm space robots. The method unifies two key problems—point-to-point grasping and continuous trajectory tracking—within an efficient optimization framework. For point-to-point grasping, the objective is to maximize the gradient of the distance between the predicted and target end-effector positions, yielding a time-optimal trajectory. For continuous trajectory tracking, the approach minimizes spacecraft base attitude disturbance while ensuring bounded end-effector tracking error. Additional objectives, such as manipulability and energy efficiency, are incorporated as weighted terms. Physical constraints on joint angles, velocities, accelerations, and self-collision avoidance in both planning problems are formulated as linear constraints. Task-specific constraints are also integrated: an approaching cone constraint for grasping and trajectory relaxation error bounds for tracking. Both problems are cast as convex optimization formulations, enabling efficient real-time solutions. The robustness of the method is demonstrated under challenging conditions, including high initial momentum in grasping and gravity-gradient-induced momentum in tracking. Extensive comparative simulations on a highly redundant 14-degree-of-freedom (14-DoF) dual-arm space robot validate the superior effectiveness, efficiency, and robustness of the proposed approach.

This study presents a novel exponential proportional-derivative controller with filter (exp-PDN) for stabilising the nonlinear and underactuated ball and beam system. Unlike conventional PID-based approaches, the proposed controller removes the integral term, resulting in faster transient responses and improved robustness. It incorporates nonlinear exponential shaping of both the error and its derivative, along with a filtered derivative path for enhanced noise handling. A custom multi-objective cost function, comprising the squared error, settling time, and percent overshoot, is proposed to evaluate control performance. The quadratic interpolation optimiser (QIO), a recently developed metaheuristic based on analytical interpolation, is employed to optimise the controller parameters. To validate its effectiveness, the exp-PDN controller is compared against five state-of-the-art metaheuristic algorithms: QIO, spider wasp optimiser, komodo mlipir algorithm, golden eagle optimiser, and slime mould algorithm. The QIO-optimised exp-PDN achieves the best performance, with the lowest cost value (0.3211), minimal overshoot (5.52%), fast rise time (0.97 s), and smallest steady-state error (4.1643 × 10⁻⁴). Further comparisons with QIO-optimised phase-lead and PID-with-filter controllers demonstrate the superiority of the proposed method in both transient and steady-state behaviour. In summary, this work advances the control of nonlinear unstable systems by delivering a structurally simple yet highly responsive control architecture. The combination of dual-channel exponential shaping and efficient metaheuristic optimisation results in state-of-the-art closed-loop performance, highlighting the practical value of the proposed exp-PDN framework for real-world control applications.

Wireless sensor networks (WSNs) are highly vulnerable to malware attacks due to resource constraints. However, existing propagation models often overlook the heterogeneous security levels in hierarchically protected WSNs, leading to inaccurate representations of dynamics across network layers. This paper proposes a novel malware propagation model specifically for hierarchically protected WSNs to capture the distinct defense capabilities of heterogeneous nodes. Based on epidemiological theory, nodes are stratified into high protection level (HPL) and low protection level (LPL) categories within a susceptible-exposed-infected-recovered (SEIR) differential equation framework. We rigorously derive the basic reproduction number ($�R_0$) and prove the global asymptotic stability of equilibrium points using Lyapunov function analysis. Theoretical and experimental results demonstrate that malware is eradicated when , whereas it persists endemically when $��R_0�>�1�$. Specifically, simulations reveal that while HPL nodes exhibit lower infection rates, their compromise significantly accelerates network-wide propagation due to high connectivity. Furthermore, a critical communication radius threshold is identified; exceeding this threshold triggers a transition to an endemic state. The proposed model offers theoretical insights for defense strategies, suggesting that optimizing the HPL node proportion and regulating communication radius are critical for mitigating malware propagation.

This paper addresses the consensus problem for time-varying multi-agent systems (MASs) under deception attacks on time scales. A novel hybrid control strategy is proposed, integrating sampled-data feedback with time-varying window impulsive control, which eliminates the conventional requirement of preset fixed impulsive instants. To characterize deception attacks on transmission channels, Bernoulli-distributed random variables are introduced to model the occurrence and probability of attacks. Moreover, average parameters related to impulsive gains and time-varying window boundaries are incorporated into the design. Based on time-scale theory and Lyapunov stability analysis, sufficient conditions are derived for achieving both leader-following consensus and dynamic average consensus. These conditions, which depend on the average parameters, not only relax the constraints on impulsive gains but also reduce the required frequency of impulsive control inputs. The effectiveness of the proposed control scheme is validated through numerical simulations.

Bearing fault diagnosis in induction motors under variable load and speed conditions remains a challenging task due to the complexity of fault-induced transients in current signals. This study presents a novel deep learning-based fault classification framework utilising Variational Mode Decomposition (VMD) for adaptive feature extraction and a Multi-branch Convolutional Neural Network (1D-MCNN) architecture for classification. The VMD hyperparameters were optimised based on kurtosis to ensure the extraction of the most informative Intrinsic Mode Functions (IMFs), significantly enhancing feature quality. Experimental validation under fixed, variable and noisy operating conditions demonstrated the superior performance of the proposed approach. The 1D-CNN multi-branch model consistently outperformed conventional artificial neural network (ANN) and single-branch convolutional neural network (CNN) architectures, achieving 99.85% accuracy in fixed-speed conditions and 99.75% in variable-speed operations. Moreover, t-SNE visualisations revealed improved class separability, confirming the robustness of the extracted features. These results highlight the efficacy of VMD-guided deep learning architectures in accurately detecting bearing faults across diverse operational scenarios, reinforcing their potential for industrial predictive maintenance.

This paper presents decentralised and centralised fixed-structure H∞ robust control methods optimised by the Particle Swarm Optimisation and Gravitational Search Algorithm (PSOGSA) for a coupled multi-input multi-output (MIMO) microsurgical manipulator. The design framework explicitly considers uncertainties and disturbance constraints. Conventional H∞ loop-shaping controllers are typically of high order, complex, and difficult to implement in practice. To address this limitation, Proportional-Integral-Derivative (PID)-structured decentralised and centralised H∞ controllers are proposed, providing compact structures while retaining robustness. The novelty of this work lies in embedding H∞ robustness criteria into practical PID-based frameworks, bridging the gap between theoretical robust design and experimental implementation in microsurgical applications. The proposed controllers are evaluated against a reduced-order H∞ controller derived from Hankel norm approximation and a Ziegler–Nichols tuned PID controller, using both simulation and experimental studies. Results demonstrate that the proposed controllers achieve improved stability margins (0.449–0.521 compared with 0.436 for the reduced-order design), maintain low root-mean-square errors (≈0.067–0.089) and remain robust under voltage disturbances where conventional PID control fails. These findings confirm the contribution of a practical and efficient robust control strategy for enhancing the precision and reliability of microsurgical manipulators.