IET Control Theory and Applications最新文献

Efficient route planning in transportation networks, particularly under stochastic conditions like severe weather (i.e. snow or hail), poses a significant computational challenge. This article addresses this challenge by modeling the route planning problem as a Markov decision process (MDP) problem, establishing reachability criteria, and identifying the minimum-weight arborescence in the directed graph. To achieve this, the reachability determination algorithm is designed to assess the courier company's reachability to all junctions based on the queue-typed data structure and breadth-first search idea. Subsequently, the minimal-cost route planning algorithm is developed to find a feasible transport route with the minimal cost of clearing obstacles by resorting to the Edmonds' algorithm and some feasible data structures. In particular, the article introduces a Fibonacci-heap-typed data structure to the minimal-cost route planning algorithm, resulting in a remarkable reduction of the time complexity from $��O�\left(�m�n�\right)�$ to $��O�(�m�+�n�\log �n�)�$, where $�m$ and $�n$ represent the cardinalities of the arc set and nodeset, respectively. Ultimately, the proposed method is applied to optimize route planning in a transportation network, providing a cost-efficient solution for logistics and transportation planning.

In networked evolutionary games (NEGs), some elements in the payoff matrix of players may change due to environmental factors. In this article, the stability of NEGs with payoff perturbation is studied using the semi-tensor product (STP) of matrices, and some new results are given. First, the perturbation of a column of the payoff matrix is considered, and a necessary and sufficient condition to ensure that the transition matrix of the profiles evolution dynamics to remain unchanged is provided. Second, the case that multiple columns of the game profile transition matrix are perturbed is investigated, and a necessary and sufficient condition for the stability of the NEGs is proposed. Finally, the validity of the results is illustrated by an example.

This paper investigates the finite-time $�H_{\infty }$ fault detection problem for large-scale power systems via the Markov jumping mechanism subject to unknown disturbances. The novel power system is described by a large-scale system model, and the residual dynamic properties of unknown input signals and fault signals, including unknown disturbances and modelling errors, are obtained by reconstructing the system. Then, the energy norm indicators of the residual disturbance signal and fault signal are, respectively, selected to reflect their suppression effect on disturbance and sensitivity to faults. Moreover, the design of a fault detection observer is formulated as an optimisation problem. Based on Lyapunov theory and linear matrix inequalities (LMI), sufficient conditions for the designed fault detection observer solutions are given, and an optimisation design method is provided. Finally, the simulation results show that the optimised observer can detect the fault signal effectively and can contain the effect of unknown disturbances on the residuals within a given range when a fault occurs.

This article studies the event-triggered anti-disturbance control problem for switched singular systems (SSSs) based on the uncertainty and disturbance estimator (UDE). First, a composite controller consisting of feedback control and anti-disturbance control based on UDE is proposed. By constructing the multiple Lyapunov functions and using the average dwell time (ADT) method, sufficient conditions are given to ensure that the closed-loop system is regular, impulse-free and globally uniformly asymptotically stable (GUAS). Further, in order to save resources, the event-triggered control method is introduced into the feedback control, and a novel hybrid event-triggered mechanism (ETM) is proposed, which guarantees that asynchronous switching and Zeno behavior are excluded. And the solvable conditions in terms of linear matrix inequalities (LMIs) are given. Finally, the validity of the proposed results is verified by the simulations.

This study introduces a network-based controller to regulate frequency within power systems. The primary objective of this investigation is to ensure the stability of power systems experiencing delays in control signal transmission. Initially, a modified networked predictor is proposed to anticipate future signals within a decentralized load frequency control framework. Subsequently, a timestamp technique is employed to actively compensate for the impact of transmission delays. Additionally, an event trigger mechanism is incorporated to complement the extended state observer and optimize signal transmission resource usage. The outcomes of the study indicate that the proposed method effectively mitigates delay effects while maintaining superior performance. Moreover, the proposed method remarkably reduces the required transmission resources.

This paper investigates the control problem of spacecraft rendezvous with obstacle constraint, considering the external disturbance forces caused by orbit perturbation. Firstly, the translational dynamic model of spacecraft rendezvous is given and then rewritten into a second-order fully-actuated system form. Then, by employing the prescribed performance control method, the performance function and error transformation are determined, pre-defining the prescribed performance bounds. Moreover, the fully-actuated system approach is used to linearize the original nonlinear system, which simplifies the processes of control law design and ensures model accuracy. After that, to ensure that the spacecraft could avoid the dangerous zone during its manoeuvre, the artificial potential function is introduced, based on which a sliding mode surface is designed. Finally, the prescribed performance control–artificial potential function-based control law is derived, further adopting the neuro-adaptive method to deal with external interferences. The stability of the close-loop control system is analysed through the Lyapunov approach and the effectiveness of the proposed control scheme is verified by carrying out a numerical simulation.

This article presents a novel approach for adaptive nonlinear state estimation in a modified autoregressive time series with fixed coefficients, leveraging an adaptive polynomial Kalman filter (APKF). The proposed APKF dynamically adjusts the evolving system dynamics by selecting an appropriate autoregressive time-series model corresponding to the optimal polynomial order, based on the minimum residual error. This dynamic selection enhances the robustness of the state estimation process, ensuring accurate predictions, even in the presence of varying system complexities and noise. The proposed methodology involves predicting the next state using polynomial extrapolation. Extensive simulations were conducted to validate the performance of the APKF, demonstrating its superiority in accurately estimating the true system state compared with traditional Kalman filtering methods. The root-mean-square error was evaluated for various combinations of standard deviations of sensor noise and process noise for different sample sizes. On average, the root-mean-square error value, which represents the disparity between the true sensor reading and estimate derived from the adaptive Kalman filter, was 35.31% more accurate than that of the traditional Kalman filter. The comparative analysis highlights the efficacy of the APKF, showing significant improvements in state estimation accuracy and noise resilience.

This study focuses on the highway platoon driving mode and proposes a distributed adaptive control algorithm based on an observer. Firstly, the adaptive observer is designed to compensate for the effect of unknown driving resistance and thus enhance the adaptation ability of the system to uncertainty. Secondly, an auxiliary system is introduced to specifically address actuator saturation constraints, ensuring the stability of the platoon driving in extreme conditions. Lastly, combining an event-triggered mechanism, a control strategy is designed to achieve the stability of the entire platoon while maximizing the conservation of communication resources. The algorithm's viability and efficiency are confirmed through simulation outcomes.

This paper proposes an improved two-degree-of-freedom active disturbance rejection controller for the coupling problem of asynchronous motor vector system. To simplify the analysis process and accommodate observers of different types, a unified expression based on different controllers for the system output is developed. The closed-loop transfer function generated by the reference input and load disturbance is given. For the coupling problem of motor output speed and immunity, the structure of a higher-order extended state observer is reconstructed. The extended state observer estimates both the output speed and the total system disturbance, which serve as feedback and feed-forward compensation quantities. Compared to the PI controller and traditional active disturbance rejection controller, the proposed controller achieves decoupling of output response speed and immunity, simplifies the process of parameter tuning. Finally, simulation and experiment results verify the feasibility and effectiveness of the algorithm in this paper.

This article presents a reachability-based receding horizon control (RHC) method for addressing persistent monitoring problems with count requirements. An agent is assigned to monitor multiple targets in a given environment to minimize the average uncertainty metric of all targets, while ensuring the monitoring count requirements of specific targets within predetermined time windows. To account for the spatial and temporal constraints in the monitoring requirements, a persistence predicate within the signal temporal logic (STL) specifications is introduced, which incorporates cumulative target state signals to effectively describe the monitoring count constraints. Considering the complexities arising from global time domain information requirements in STL constraints validation, an STL formula segmentation method based on completion progress is proposed. Subsequently, a reachability-based controller for the agent is developed by solving a short-term RHC problem while ensuring the satisfaction of the STL formulae. Simulation results are provided to illustrate the performance of proposed method.