IET Control Theory and Applications最新文献

In this work, a novel energy management control framework is developed for hybrid electric vehicles (HEVs) driving in car-following scenarios. In order to enhance the energy efficiency while maintaining the driving safety, a hierarchical control approach consisting of an upper level speed tracking control scheme and a lower level energy management control strategy is proposed. For the upper level tracking control system, an iterative learning model predictive control (ILMPC) scheme is developed to guarantee the tracking performance and the driving safety simultaneously. Additionally, a model predictive control (MPC) algorithm is adopted at the lower level to optimize the torque distribution in real-time based on the driving cycles generated by the upper level control system. With the proposed hierarchical control framework, HEVs are able to improve the energy efficiency significantly by taking the advantages of the operational repeatability. The convergence of the proposed control strategy is analyzed rigorously, and its effectiveness is illustrated through numerical simulations.

Cascade control structures with inner current and outer speed loop, usually utilizing PI controllers, are widely used for electrical drives to meet high-quality requirements. The present paper introduces design guidelines via pole placement for achieving control gains both in continuous and discrete time preserving the original cascade control structure with the initially applied controllers. This paper also presents an additional prefilter design to eliminate the undesirable effect of the reference integrals. The paper presents closed-form expressions for the control gains as the function of desired damping ratios, the natural angular frequency of the control loop, and machine parameters to achieve the desired system dynamics. The proposed design methodology is demonstrated on brushed DC and permanent magnet synchronous machines.

This study focuses on a model reference adaptive control method that ensures identical orientation outputs for different prototypes of a two-axis gimbal produced in mass production. In this method, unlike traditional MRAC structures, an MRAC structure is used in conjunction with state feedback control. First, the reasons for the need for an adaptation mechanism in gimbals and why Model Reference Adaptive Control (MRAC) alone won't be sufficient have been discussed. In the first section, various applications of MRAC have also been mentioned. Then, the mathematical foundation of the model reference adaptive controller used in this study is elaborately explained, followed by stability analyses. In the next step, an ideal reference model exhibiting desired behavior and a real system model with different dynamics are created in a simulation environment. This allows a comparison of the adaptation capabilities of only MRAC and MRAC+State Feedback controllers. Based on the information gathered in this section, the recommended approach in the article is tested on a real gimbal system, and the results are shared. The obtained results demonstrate that the MRAC+State Feedback control structure significantly reduces the error in the gimbal's orientation response compared to the reference model.

In this article, the stabilization of coupled time fractional parabolic partial differential equations subject to external disturbances is investigated. By using sliding mode control method and backstepping approach, a boundary state feedback controller is designed to reject the matched disturbance and achieve the Mittag-Leffler input-to-state stability of closed-loop system. The existence of the generalized solution to the closed-loop system is proven by Galerkin approximation scheme. Simulations are presented to illustrate the validity of our theoretical results.

This article investigates the distributed fault-tolerant formation control problem of a multi-leader system with process faults and system uncertainties. This study guarantees all the followers to achieve a specified time-varying formation and track the combination of multiple leaders. By treating faults as a special type of states, an enhanced system is constructed. Furthermore, an intermediate variable observer is designed, but the observer matching condition is not necessary. The observer is used to estimate faults and system uncertainties, for reconstructing the formation controller. The observer gains are determined by solving linear matrix inequalities. Subsequently, controller gains are designed based on Lyapunov theorem and adaptive techniques. Finally, the proposed method is verified through a simulation example.

This paper investigates the challenges associated with the velocity consensus and the lane following in heterogeneous automated vehicles considered as agents of a multi agent system. A cloud-based distributed model predictive control structure is proposed to address the distributed intrinsic of such systems. Moreover, for situations that the cloud-based control signal is not reachable, backup controllers based on explicit distributed model predictive control are developed. The proposed cloud-based controller is implemented in the Amazon cloud servers to show the practicality of the proposed approach. Simulation results are presented to demonstrate the effectiveness of our methodology in terms of handling practical limitations of connected and automated vehicles.

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.