IET Control Theory and Applications最新文献

In this paper, linear algebra based (LAB) control methodology is proposed as a feasible technique for tracking control of drones. The method introduces a straightforward mathematical procedure, which leads to a low computational cost implementation. A comprehensive analysis of LAB control applied to drone trajectory tracking, including detailed mathematical derivations, stability analysis, and practical implementation considerations is provided. Moreover, compared to one of the most popular non-linear control techniques, that is, feedback linearization (FL), LAB is able to reach satisfactory tracking and energy saving even under the consideration of complex trajectories (with sudden changes). The paper addresses key implementation challenges including sampling period selection, control parameter tuning methodologies, and actuator saturation handling. Extensive simulation results demonstrate the effectiveness of the proposed approach and its advantages over traditional FL method.

The joint optimisation of unit commitment and economic dispatch (ED) is one of the key issues in smart grid scheduling and control. Integrating the discrete on/off statuses of units in the unit commitment problem with the continuous active power outputs in ED significantly increases the overall complexity of the combined optimisation problem. We propose an innovative distributed algorithm based on a graph attention network to address this challenge. The graph neural network is used to extract the inter-unit relational features and predict the future power dispatch schedule of each unit, while the parallel distributed coordination algorithm (PDCA), acting as the power dispatch algorithm, schedules and controls the output power of the units, including their start-up and shut-down states. Experimental results show that our algorithm performs well on both the IEEE 30-bus and IEEE 118-bus test systems, achieving a 1559 times speed boost compared to advanced solvers, and reaching economic optimality while satisfying all critical constraints to obtain an industrial acceptable solution.

This study addresses vehicle trajectory tracking control under tri-modal cyber attacks, encompassing fixed sensor-to-controller/controller-to-actuator channel attacks in lateral dynamics and sparse multi-sensor attacks in position tracking. A hybrid fuzzy modeling framework is developed, integrating fuzzy logic inference with Takagi-Sugeno fuzzy techniques to approximate vehicle dynamics with time-varying velocity, payload-dependent mass, and unmeasurable cornering stiffness avoiding the conservatism inherent in conventional linear fractional transformation approaches for cornering stiffness parameterization. A dual-observer architecture combining an extended state observer and a supervisory fuzzy reduced-order observer (ESO-SFRO) is proposed for simultaneous system state reconstruction and tri-modal attack signal estimation. Based on the estimated states, a cyber-resilient controller is designed to ensure lateral stability and trajectory tracking accuracy. Experimental validation via CarSim/Simulink co-simulation demonstrates the proposed ESO-SFRO based controller exhibits superior dynamic stability and trajectory tracking performance under coupled cyber-physical disturbances.

This paper investigates the L1 gain stability problem of reliable control for positive systems with input saturation under multi-asynchronous switching. Firstly, by constructing a system state observer and integrating it with an output feedback control strategy, the input variables for the system controller were obtained, and a reliable controller with input saturation was designed. Secondly, to prevent data accumulation, an adaptive event-triggered control strategy that ensures the non-negativity requirements of positive systems is introduced between the observer and the system state. This strategy can adjust the tightness of the event-triggering process, which not only improves control efficiency but also reduces the risk of the Zeno effect. The following describes a switching strategy based on event-triggered control. Under the guidance of a time-varying mode-dependent average dwell-time switching strategy, the multi-asynchronous delay problem of sub-observers and sub-controllers with respect to subsystems is addressed, leading to a closed-loop control system based on error feedback. By constructing co-positive Lyapunov function, sufficient conditions for the positivity of the system under both synchronous- and asynchronous-switching are provided, and the L1 gain stability of the system in both synchronous and asynchronous intervals is verified. Finally, the significance of the proposed method is validated through an example.

This research highlights the application of unmanned aerial vehicle (UAV) control in a reduced and laboratory scale model called 1-DOF twin rotor system (1D-TRS). Although the PID controller is widely used in UAVs due to its versatility and functionality, it has precision and disturbance rejection limitations. The discrete-time fractional-order PID (FOPID) controller is a valid alternative for UAV control that requires approximating the integral and derivative terms using an infinite summation of fractional terms, from which the most representative are selected for practical implementation. For low-scale UAV models, implementation is challenging since microcontrollers must process fractional operations in discrete time into limited hardware capabilities. In this context, tuning control parameters also represents a challenge since the PID constants and the fractional order parameters must be considered. This study proposes a discrete-time FOPID controller emphasizing the integral term, utilizing the same components as a conventional FOPID but with improved tuning flexibility. Since the integral term plays a key role in reducing tracking errors, this approach enhances accuracy without significantly increasing computational complexity, ultimately resulting in an FOPI + D controller. Tuning the controller parameters is also considered an a priori idea to set $��\lambda �=�\mu �=�0.5��$ to leverage the capacities of the particle swarm optimization (PSO) method for adjusting the PID constants without increasing its computational cost if all the FOPID parameters were considered to perform tests and PSO calibration to obtain the desired control results. Experimental tests were conducted on the 1D-TRS using different reference signals, such as step, steps, and ramp inputs. Additionally, external disturbances were introduced during experimental scenarios. The proposed controller was compared against traditional PID and discrete FOPID controllers, with performance metrics demonstrating improved system response. The results showed that the proposed controller enhances trajectory tracking in 1D-TRS, improving both precision and robustness against disturbances. It is a valuable alternative for UAV applications where stability and tracking accuracy are critical.

Voltage regulation is essential for maintaining the stability and efficiency of power systems, and automatic voltage regulators (AVRs) play a key role in ensuring consistent voltage levels and reducing disturbances. In this study, a novel cascaded controller, referred to as RPIDD²-PI, is proposed for AVR systems, representing its first known application in literature. The controller is designed to enhance voltage regulation by improving precision, stability, and dynamic responsiveness. To optimize the controller parameters, an improved metaheuristic algorithm called the enhanced cooperation search algorithm (ECSA) has been developed. This algorithm incorporates several features, including control randomization, a linear transfer function, an adaptive p-best mutation strategy, and a greedy selection mechanism. These enhancements provide a balanced trade-off between exploration and exploitation during the optimization process. Simulation results show that the ECSA-tuned controller achieves excellent dynamic performance, with a rise time of 0.0314 s, a settling time of 0.0472 s, and zero overshoot, outperforming existing methods. The standard deviation of 7.9591E−05 across multiple runs highlights the consistency and robustness of the solution. Robustness tests confirm the controller's reliability under parameter uncertainties in the amplifier, exciter, and generator components, showing only minor variations in dynamic performance. Furthermore, the proposed controller demonstrates strong transient stability by minimizing oscillations and enabling a fast return to steady-state conditions. Comparative studies against more than 40 recent optimization-based controllers published between 2020 and 2023 confirm that the ECSA offers faster convergence and higher efficiency.

We deduce a sufficient condition for the exponential (integral) turnpike property for infinite-dimensional generalized linear-quadratic optimal control problems in terms of structural properties of the control system, such as exponential stabilizability and detectability. The proof relies on the analysis of the exponential convergence of solutions to the differential Riccati equations to the algebraic counterpart, and on a necessary condition for exponential stabilizability in terms of a closed range test.

This paper addresses the critical control challenges inherent in doubly fed induction generator (DFIG) systems, which are pivotal components of modern wind energy conversion systems (WECS). These systems often face performance degradation due to their nonlinear dynamics, sensitivity to grid disturbances, and difficulty in achieving robust control under fluctuating operational conditions. To tackle these issues, this study proposes an innovative approach for optimizing proportional-integral-derivative (PID) controller parameters using the hiking optimization algorithm (HOA). Inspired by Tobler's walking function, HOA is integrated with an enhanced version of the Zwe-Lee Gaing (ZLG) objective function that incorporates penalty terms for overshoot, settling time, control effort, and abrupt signal variations. This enables a robust balance between transient and steady-state performance in dynamic environments. Extensive simulations validate the effectiveness of the HOA-optimized PID controller against five state-of-the-art met heuristic algorithms: starfish optimization algorithm, grey wolf optimizer (GWO), dragonfly algorithm (DA), flow direction algorithm (FDA), and sine-cosine algorithm (SCA). The results demonstrate that HOA achieves superior performance across all key metrics, including zero overshoot, rapid settling time (0.08922 s), and minimal steady-state error. Statistically, HOA maintains the highest reliability with a standard deviation of just 0.0013 over 30 independent trials. In the frequency domain, HOA outperforms competitors by achieving the highest phase margin (87.163) and gain margin (26.11 dB), ensuring robust stability. The proposed controller also excels in disturbance rejection and input tracking under varying conditions. These findings establish HOA as a powerful and reliable optimization tool for advanced PID control of DFIG systems, with broader applicability in industrial control systems requiring high performance and adaptability.

This paper presents a novel and efficient analysis based on linear matrix inequalities (LMIs) to derive optimized reduced models that preserve dissipativity for discrete-time periodic systems described by the two-dimensional (2D) Roesser model. To simplify stability analysis, we assume that the horizontal and vertical directions of the augmented system share the same period. By leveraging periodic Lyapunov functionals, we establish less conservative conditions that guarantee the existence of a 2D periodic reduced model that maintains the fundamental properties of the full-order system, ensuring asymptotic stability and $��(�O_1�,�O_2�,�O_3�)�$ dissipativity. Furthermore, we examine a specific case of dissipativity related to the $�H_{\infty }$ norm, addressing a crucial aspect of system performance. The parameters of the reduced model are determined through convex optimization techniques, and numerical simulations validate the theoretical results, demonstrating the effectiveness of the proposed approach and highlighting the correlation between optimal dissipative performance indices and different Lyapunov functionals.

Wind energy conversion systems (WECSs) based networked microgrids has been widely used in recent years. The mean square error (MSE) metric can yield imprecise outcomes if measurement data is polluted by non-Gaussian disturbances or extreme values. To address this problem, we propose a new robust square root cubature Kalman filter (SRCKF) method called maximum correlation criterion (MCC)-SRCKF, which incorporates MCC into the SRCKF framework of dynamic state estimation. In MCC, by considering the high-order moments of the error distribution, it demonstrates anti-interference ability against non-Gaussian noise, thus serving as an ideal alternative in the MSE cost function field of SRCKF. Furthermore, within the framework of SRCKF, this study introduces statistical linear regression models and non-moving point iteration strategies to solve the optimal state estimation under MCC conditions. Therefore, a historical measurement triggered DoS attack model is proposed from the attacker's perspective, aiming to destabilise the WECS-based networked microgrids. The security conditions of the power system under such attacks are obtained. The proposed method is validated numerically using an IEEE 39-bus system, and the results demonstrate its effectiveness and superiority.