IET Control Theory and Applications最新文献

This paper proposes an innovative dual-layer multi-agent-based SIS epidemic model, incorporating a physical contact layer to model disease spread through travel or migration between cities, and an information layer to enable the sharing of infection data among healthcare providers across cities even without direct physical connections. An observer is designed to estimate the infected fraction in each city, utilising estimates from neighbouring cities connected in the physical layer in a distributed manner; these estimates are then leveraged in the information layer to synchronise each city's infection trajectory with a virtual leader. Additionally, the control input, typically formulated in multi-agent systems (MAS), is adopted as the sliding surface, with its stability demonstrated via Lyapunov analysis within the dual-layer SIS framework. An adaptive sliding mode control (ASMC) strategy is developed to address parameter uncertainties to reach this sliding surface, effectively integrating the physical and information layers’ dynamics to drive cities toward disease eradication. Finally, a numerical example is provided to validate the accuracy of the theoretical results.

Stronger policies and raised climate goals leading into COP27 are driving the development of renewable energy to new records. Based on the analysis and forecasts of International Energy Agency, renewables are set to account for almost 95% of the increase in global power capacity through 2026. The rapid growth of renewables brings a lot of new challenges to the energy systems. Smart energy systems have been developed to meet the requirements of high-level penetration of renewable energy, distributed energy resources, multi-energy integration etc. In smart energy systems, the power generation process faces more internal and external uncertainties, the operating conditions are more complex, the requirements for reliability and flexibility are higher, and the characteristics of network collaboration are more significant. Therefore, knowledge-based control theories, control technologies and optimization methods are inspiring and promising to enhance the performance of smart energy systems.

In this perspective, the goal of this special issue is to provide a forum to exhibit recent developments in knowledge-based control and optimization theories, methodologies, techniques, and their applications to smart energy systems. There are in total thirteen papers accepted for publication in this Special Issue through careful peer reviews and revisions. Under the overarching theme of data-driven applications in power systems, the selected papers are broadly categorised into five topics. The summary of every topic is given as follows.

Monirul et al., in their paper “Adaptive state of charge estimation for lithium-ion batteries using feedback-based extended Kalman filter,” consider high-order equivalent circuit model (ECM) to capture the dynamic characteristics of lithium-ion batteries. The feedback-based extended Kalman filtering (FEKF) algorithm is established. The optimal simulation knowledge is adopted to improve the SOC estimation approach remarkably and provide a reference value. The nonlinear predicting and corrective techniques are applied to the experiment in the extended calculation process. The established high-order ECM utilizing the FEKF algorithm achieves superb performance from the lithium-ion battery pack.

Yang et al., in their paper “Self-paced learning LSTM based on intelligent optimization for robust wind power prediction,” propose a wind power prediction method that leverages an enhanced multi-objective sand cat swarm algorithm (MO-SCSO) and a self-paced long short-term memory network (spLSTM). The progressive advantage of selfpaced learning (SPL) is used to effectively solve the instability caused by noisy data during long short-term memory network (LSTM) training. The improved MO-SCSO is employed to iteratively optimize the hyperparameters of spLSTM. A combined MOSCSO-spLSTM model is constructed for wind power prediction, which is validated with the data of onshore wind farms in Austria and offshore wind farms in Denmark.

In recent years, considerable attention has been attracted to the synchronization of chaotic systems due to their important applications. However, fractional order non-linear chaotic systems face critical challenges, particularly from input delays and external disturbances in practical applications. In this paper, a robust synchronization method based on state prediction is introduced to address these challenges. First, a novel adaptive disturbance observer for fractional order systems is proposed, ensuring that disturbance estimation is achieved within an arbitrary time. The effects of disturbances are mitigated by this observer, which plays a crucial role in synchronization scheme design. Second, an arbitrary time exponential sliding mode controller that integrates state prediction and the disturbance observer is presented to handle input delay in fractional chaotic systems subjected to external disturbances. Third, a control scheme incorporating state prediction and sliding mode control is developed to address chaos synchronization for fractional systems with time varying input delays and disturbances. Additionally, an upper bound for input delay is established, demonstrating that if the delay remains below this threshold, the synchronization error is constrained. The efficacy and practical applicability of the proposed synchronization scheme are confirmed through simulation studies and experimental validation on a real-time Speedgoat machine.

Electric vehicles (EVs) with distributed drive configurations demonstrate improved energy storage potential through battery-dominated systems, enabling independent torque allocation across individual wheels. This paper proposes a differential torque control framework for distributed-drive electric vehicles to enhance trajectory tracking accuracy and yaw stability during double-lane change maneuvers. A hierarchical control architecture with three layers are developed, integrating model predictive control with quadratic programming-based torque allocation to coordinate longitudinal velocity tracking and lateral path following. The lateral controller generates real-time differential torque commands (front-rear axle torque variation range: $��\pm �282.68�$–$��\pm �409.42��N�·�m�$) through a 3-DOF vehicle dynamic model, while the longitudinal controller maintains speed errors below 0.1 m/s through four-wheel independent torque regulation. Co-simulation on the CarSim-Simulink platform demonstrates the controller's adaptability to road friction coefficients ($��\mu �=�0.5�,�0.8�$) and speed conditions ($��u�=�40�,�50�,�60�$ km/h). The results achieve maximum yaw rate stabilization at 0.38 rad/s during high-speed maneuvers. Simulation results reveal that despite lateral deviation amplification (80–160 m trajectory segments) and torque oscillation divergence under $��\mu �=�0.5�$

This article proposes a data-driven model predictive control (MPC) method for multirotor collision avoidance, considering uncertainties and the unknown dynamics caused by a payload. To address this challenge, sparse identification of nonlinear dynamics (SINDy) is employed to derive the governing equations of the multirotor system. SINDy is capable of discovering the equations of target systems from limited data, under the assumption that a few dominant functions primarily characterize the system's behavior. In addition, a data collection framework that combines a baseline controller with MPC is proposed to generate diverse trajectories for model identification. A candidate function library, informed by prior knowledge of multirotor dynamics, along with a normalization technique, is utilized to enhance the accuracy of the SINDy-based model. Using data-driven model from SINDy, MPC is used to achieve accurate trajectory tracking while satisfying state and input constraints, including those for obstacle avoidance. Simulation results demonstrate that SINDy can successfully identify the governing equations of the multirotor system, accounting for mass parameter uncertainties and aerodynamic effects. Furthermore, the results confirm that the proposed method outperforms conventional MPC, which suffers from parameter uncertainty and an unknown aerodynamic model, in both obstacle avoidance and trajectory tracking performance.

In this paper, we present a new method to determine the continuous-time response of sampled-data systems with uniform sampling, zero-order hold, and full-state feedback. In such systems, a continuous-time plant is controlled using a discrete-time control law. Traditionally, sampled-data systems are designed in discrete time, resulting in, given by the nature of this kind of modelling, unmodelled intersample behaviour. We show that the Laplace transform of the otherwise piecewise-continuous state response can be expressed in closed form that fully represents the intersample dynamics. A practical technique is also provided to decouple individual vibration components and reconstruct response functions in the time domain. The proposed approach is also able to capture intersample vibrations compared to common methods, which may lead to inaccurate results in specific cases. The presented new formulae are derived analytically and verified by simulations through numerical examples and experiments on a DC motor drive.

This paper investigates the cooperative time-varying formation fault-tolerant control problem for multiple quadrotors with unknown actuator faults and full state constraints. In order to ensure the safety and operability of quadrotors in the confined flight environment, a novel transformed function is first introduced to convert the original quadrotor systems into unconstrained equivalent systems, which increases the flexibility of the controller design. Then, a distributed kinematic control protocol and fault-tolerant dynamic control protocol using the adaptive neural networks estimation technique are developed to guarantee the cooperative time-varying formation of multiple quadrotors subject to uncertain parameters. Meanwhile, the unknown actuator loss of effectiveness and bias faults are compensated and the state variables of position subsystem and attitude subsystem can be maintained within the designed performance constraint sets even when actuator faults occur. Via Lyapunov stability theory, the cooperative formation fault-tolerant performance analysis is presented. The proposed control strategy is validated through simulations.

Suspended constant force (SCF) control is a critical technology in suspended gravity offloading systems. However, inherent underactuation, unmodelled dynamics, and external disturbances can significantly degrade control performance and even compromise system stability. In this article, pneumatic artificial muscle (PAM) actuators are used as a replacement for traditional passive dampers to address the underactuation problem. Additionally, we propose a novel systematic robust output feedback model predictive control (ROFMPC) framework, which incorporates a radial basis function neural network (RBFNN)-based model compensator, a Luenberger state estimator, and a tube model predictive controller. The RBFNN-based model compensator compensates for unmodelled dynamics, while the Luenberger state estimator observes external disturbances. The model predictive controller then generates the optimal control sequence. Analytical results indicate that our designed SCF system encounters similar control challenges as those in antagonistic PAM (APAM). Therefore, sufficiently comprehensive numerical simulations and physical experiments are conducted on the APAM platform to verify the effectiveness of the proposed control framework. These results demonstrate that the proposed ROFMPC framework significantly improves force trajectory tracking performance for constant force control.

Designing water level control for a U-tube steam generator (UTSG) in nuclear power plants (NPP) remains a challenge, especially at low power demand due to unreliable steam flow measurements. This paper addresses the steam flow rate as a disturbance to the plant, treating it as an inaccessible variable. To estimate the disturbance (steam flow rate) and system states, an adaptive integral terminal sliding mode observer is developed. These estimated values can be utilized in the water level control design to enhance the reliability and performance of the control system. An adaptive observer is developed such that the augmented systems formed by the error dynamical systems and the designed adaptive laws are globally uniformly ultimately bounded. This technique is applied to a non-minimum phase system model representing the UTSG system to improve water level control and prevent possible serious consequences. Various disturbance signal forms with different amplitudes are simulated to demonstrate the reliability of the proposed technique. The simulation results show the effectiveness of the method proposed in this paper.

Model predictive control (MPC) is advantageous for autonomous vehicle path tracking but suffers from high computational complexity for real-time implementation. Event-triggered MPC aims to reduce this burden by optimizing the control inputs only when needed instead of every time step. Existing works in literature have been focused on algorithmic development and simulation validation for very specific scenarios. Therefore, event-triggered MPC in real-world full-size vehicle has not been thoroughly investigated. This work develops event-triggered MPC with switching model for autonomous vehicle lateral motion control, and implements it on a production vehicle for real-world validation. Experiments are conducted under both closed road and open road environments, with both low speed and high speed maneuvers, as well as stop-and-go scenarios. The efficacy of the proposed event-triggered MPC, in terms of computational load saving without sacrificing control performance, is clearly demonstrated. It is also demonstrated that event-triggered MPC can sometimes improve the control performance, even with less number of optimizations, thus contradicting to existing conclusions drawn from simulation.