IET Control Theory and Applications最新文献

This study presents a gradient descent approach for addressing the matrix equation $��A�W�B�+�C�W^T�D�=�E�$. Additionally, this method is utilized to solve the optimization problem $��{\min }_W�{�\parallel �A�W�B�+�C�W^T�D�-�E�\parallel �}^2�$ with the Frobenius norm. We provide a comprehensive analysis of the convergence and characteristics of these techniques. To improve the convergence rate, we incorporate a specific variant of the momentum method. To validate the effectiveness of our proposed iterative methods, we offer various numerical examples and compare the outcomes with those of existing algorithms. Lastly, we investigate an application within the context of time-varying linear systems.

This paper presents a novel application of the differential form of the state-dependent Riccati equation technique (SDRE) i.e., the state-dependent differential Riccati equation (SDDRE) as an indirect solution to the robust tracking control (RTC) problem for determining maximum dynamic load. To address this, the complicated RTC problem is solved indirectly through introducing a parallel sub-optimal problem. Minimising a modified performance index, the uncertainty and disturbances are effectively handled, as well as establishing a compromise between error reduction and small control effort while maximising-load carrying capacity. To overcome the challenges associated with directly solving the uncertain state-dependent differential Riccati equation (USDDRE) for complex systems, a modified Lyapunov-based approach is developed. Additionally, a stability proof is provided for the proposed controller. The proposed controller is then applied to a flexible joint-selective compliance articulated robot arm (FJ-SCARA) carrying a load to demonstrate both its superiority and robustness.

Understanding how different functional brain networks interact is crucial for revealing the complexity of brain function and behavior. This study addresses this gap by investigating how brain transitions occur between functional brain networks, focusing on the controllability of brain structural subsets. Previous studies on brain controllability have primarily focused on whole-brain connectivity networks, which do not adequately capture the transition abilities of weakly connected regions. To address this issue, we introduce a new metric—combinatorial average energy controllability (CAEC)—which assesses the influence of functional networks based on their ability to modulate other networks using low-energy control inputs. By employing manifold learning and geodesic distance calculations, we aggregate influence vectors to provide a comprehensive view of energy propagation capacities in less connected functional networks, complementing conventional average controllability measures. Our findings demonstrate that even regions with weak connections can propagate input energy, while some moderately connected ones do not, and strong connections preserve their distribution abilities. Additionally, we utilize optimal control cost calculations to compare with CAEC results, revealing how the brain's structure and connections affect its function. This study offers new insights into how increased activity in different functional networks influences brain activity, with implications for understanding cognitive processes and addressing neurological disorders.

This paper introduces a semi-model-free adaptive backstepping dynamical sliding mode control scheme for trajectory tracking of robot manipulators subject to uncertain dynamics. The proposed methodology synthesizes backstepping control and dynamical sliding mode control paradigms through Lyapunov stability theory to derive an innovative dynamic control law coupled with an adaptation mechanism. A key advantage of this approach is its dependence solely on the nominal inertia matrix, thereby circumventing the requirement for a comprehensive dynamic model. In contrast to conventional model-based adaptation laws, which depend on precise knowledge of system dynamics, and model-free approaches that often rely on the restrictive assumption of zero time-derivative for uncertain terms, the proposed adaptive law bypasses both limitations. Instead, this adaptive mechanism estimates the aggregate effects of uncertain dynamic components—encompassing centripetal and Coriolis forces, gravitational effects, external disturbances, and unmodelled dynamics—and incorporates these estimates within the dynamic control framework. Through rigorous stability analysis, we demonstrate that the integration of these control techniques ensures global uniform boundedness of both tracking and estimation error trajectories, thereby establishing robust convergence properties. The efficacy of the proposed control architecture is validated through comprehensive numerical simulations conducted on a 6-degree-of-freedom Universal Robots UR5 manipulator platform, implemented within both MATLAB and the Gazebo simulation environment interfaced with the robot operating system framework. Simulation results demonstrate the closed-loop system's superior performance in tracking predefined trajectories despite significant model uncertainties. An integrated motion planner further optimizes performance by reducing peak torque during goal-to-goal positioning tasks.

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.