IET Control Theory and Applications最新文献

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.

This paper investigates the dynamic cluster consensus problem for second-order multi-agent leader–follower systems operating under directed communication topologies. Focusing on two scenarios, including single-chain leadership systems and multi-connection leadership systems, the study constructs a three-layer cooperative architecture that decouples the system into a leader layer, an intermediate follower layer, and a bottom follower layer. A consensus control protocol is then designed based on a hierarchical dynamic clustering strategy. For the single-chain system, a dynamic adjustment mechanism is proposed for the bottom-layer topology, and this mechanism utilizes a composite position-velocity error to achieve adaptive optimization of the communication links among neighbouring followers. For the multi-connection system, the method is extended to include dynamic adjustment of the leader–follower communication topology and an anchoring mechanism, thereby resolving issues of cluster partitioning and leader isolation. Furthermore, the design of a distributed control protocol with weight adjustment and an anti-ambiguity mechanism ensures that the system can form stable multi-cluster structures, achieve uniform convergence of states and velocities within each cluster, and maintain communication isolation between clusters. The theoretical analysis, grounded in graph theory and Lyapunov stability theory, proves the convergence of the system under weaker assumptions, while simulation results verify the effectiveness of the proposed method.

In this paper, a flight control scheme utilizing a fully actuated auxiliary system is investigated to improve the manoeuvring flight performance and safety of manoeuvrability-limited unmanned autonomous helicopters (UAHs). A saturation-like function is employed to approximate state constraints. A dedicated fully actuated auxiliary system is integrated into the control framework to address the detrimental impacts induced by input saturation and state constraints. The control architecture is developed through a backstepping methodology. The stability of the closed-loop system is verified by Lyapunov stability analysis. Simulation results demonstrate that the proposed control scheme effectively resolves the flight control challenges associated with manoeuvrability limitations in UAHs and achieves superior tracking performance under the presence of state constraints and input saturation.

This paper investigates the error accumulation problem in centralized training and decentralized execution (CTDE) policy-based multi-agent reinforcement learning (MARL) algorithms, which arises from local observation inaccuracies. To address this issue, we propose a novel MARL algorithm that incorporates imitation learning using local observations. Firstly, by analysing the multi-agent proximal policy optimization algorithm and examining the problems arising when global states are replaced with local observations, it is proved that insufficient observations can lead to information loss, thereby introducing errors of advantage function, and it is demonstrated that the generalized advantage estimation method accumulates errors during the training process. Then, imitation learning is introduced and a novel training framework that combines reinforcement learning and imitation learning is proposed. During the reinforcement learning phase, an MARL agent trained with global observations acts as an expert. Subsequently, imitation learning is applied to train another agent that mimics the expert's decisions using only local observations. Finally, the effectiveness of this algorithm is verified in some commonly used multi-agent environments, which demonstrates its superior performance compared to traditional multi-agent reinforcement learning algorithms.

Willems' fundamental lemma (WFL) is widely used in the data-driven model predictive controllers (MPCs) to model the plant and predict its response to the given input sequence. The advantage of this model is that it can be incorporated as a linear matrix equality constraint into the optimization problem of MPC. However, the response of a dynamical system depends not only on the input applied to it but also on its initial condition. The WFL does not directly state anything about how the initial condition of the system should be defined and incorporated into the data-driven model equations. The conventional method for incorporating the initial condition of the system in a data-driven model is to insert a number of the most recent input and output samples of the system into the same data-driven model used for predicting the system's response, and this equation is then added as a new constraint to the optimization problem. This paper shows that the conventional method of incorporating the system's initial condition into the data-driven model is inaccurate and leads to errors in predicting the system's output. Moreover, the correct method for doing this has been presented for linear time-invariant systems, and the results have also been extended to the non-linear case. The proposed method has been used to design a data-driven MPC for a lab-scale wind turbine and experimental results have been presented.

This paper examines the issue of collision-avoidance trajectory tracking control in a multi-quadrotor unmanned aerial vehicle (UAV) slung load system, with particular emphasis on the scenario where the reference trajectory is unreachable. The challenge of tracking an unreachable reference trajectory is effectively addressed by integrating a trajectory planner and a trajectory tracking controller within a unified distributed model predictive control (DMPC) framework. Moreover, the nonlinear system is linearized using the first-order Taylor approximation, significantly simplifying the computation in DMPC. To ensure collision avoidance with both dynamic and static obstacles, the MINVO basis is employed to calculate the minimum volume of the exterior polyhedral approximation of the obstacles' trajectories, which is significantly smaller than that achieved using the B-spline or Bernstein bases typically utilized in the planning literature. Simulation experiments involving four UAVs, one payload, two static obstacles, and one dynamic obstacle are conducted to evaluate the effectiveness of the proposed DMPC method.