This paper addresses the problem of delaydependent robust and reliable H _∞ static output feedback (SOF) control for a class of uncertain discrete-time Takagi-Sugeno fuzzy-affine (FA) systems with time-varying delay and actuator faults in a singular system framework. The Markov chain is employed to describe the actuator faults behaviors. In particular, by utilizing a system augmentation approach, the conventional closed-loop system is converted into a singular FA system. By constructing a piecewise-Markovian Lyapunov-Krasovskii functional, a new H _∞ performance analysis criterion is then presented, where a novel summation inequality and S-procedure are succeedingly employed. Subsequently, thanks to the special structure of the singular system formulation, the piecewise-affine SOF controller design is proposed via a convex program. Lastly, illustrative examples are given to show the efficacy and less conservatism of the presented approach.

This paper proposes a novel system-augmentation approach to the delay-dependent reliable piecewise-affine ℋ _∞ static output feedback control for nonlinear systems with time-varying delay and sensor faults in the piecewise-Markovian-Lyapunovfunctional-based framework. The nonlinear plant is described by a continuous-time Takagi-Sugeno fuzzy-affine model with parametric uncertainties, and the sensor faults are characterized by a Markov process. Specifically, by applying a state-input augmentation technique, the original closed-loop system is first reformulated into a descriptor fuzzy-affine system. Based on a new piecewise-Markovian Lyapunov-Krasovskii functional, combined with a Wirtinger-based integral inequality, improved reciprocally convex inequality, and S-procedure, a novel bounded real lemma is then derived for the underlying closed-loop system. Furthermore, by taking advantage of the redundancy of descriptor system formulation, together with a linearization procedure, the piecewiseaffine controller synthesis is carried out. It is shown that the desired piecewise-affine controller gains can be attained by solving a linear matrix inequality based optimization problem. Finally, simulation examples are performed to confirm the effectiveness and less conservatism of the presented approach.

Notice of Violation of IEEE Publication Principles After careful consideration by a duly constituted committee, an author of this article, Hamid Reza Karimi, was found to have acted in violation of the IEEE Principles of Ethical Publishing by artificially inflating the number of citations to this article. This paper studies the piecewise-affine memory H _∞ filtering problem for nonlinear systems with time-varying delay in a delay-dependent framework. The nonlinear plant is characterized by a continuous-time Takagi-Sugeno fuzzy-affine model with parametric uncertainties. The purpose is to develop a new approach for filter synthesis procedure with less conservatism. Specifically, by constructing a novel Lyapunov-Krasovskii functional, together with a Wirtinger-based integral inequality, reciprocally convex inequality and S-procedure, an improved criterion is first attained for analyzing the H _∞ performance of the filtering error system, and then via some linearization techniques, the piecewise-affine memory filter synthesis is carried out. It is shown that the existence of desired filter gains can be explicitly determined by the solution of a convex optimization problem. Finally, simulation studies are presented to reveal the effectiveness and less conservatism of the developed approaches. It is anticipated that the proposed scheme can be further extended to the analysis and synthesis of continuous-time fuzzy-affine dynamic systems with integrated communication delays in the networked circumstance.

This brief is devoted to the reliable piecewise affine (PWA) H _∞ static output feedback control problem for PWA systems with actuator faults. The actuator faults are characterized by a Markov process. Specifically, by adopting a state-input augmentation technique, the original closed-loop system is first reformulated into a descriptor PWA system. On the basis of a piecewise-Markovian Lyapunov function, and by exploiting advantage of the redundancy of descriptor system formulation, together with a linearization procedure, the PWA controller synthesis is then presented. It is shown that the desired PWA controller parameters can be attained via solving a linear matrix inequality-based optimization problem. Finally, a simulation example is carried out to confirm the effectiveness and less conservatism of the developed approach.

Electric vehicles (EVs) have been increasingly used in the logistics and transportation industry due to their cost-effectiveness and sustainability. However, one of the major challenges in optimizing routes for EVs is the EV routing problem (EVRP), which arises from their limited battery capacity. This article proposes a reinforcement learning (RL)-based end-to-end framework to address EVRP with different sizes. The framework includes a graph attention network (GAT)-based encoder and an attention-based decoder. In particular, an improved GAT-based encoder is employed to encrypt node and edge information from the graph-structured EVRP instances, resulting in high-dimensional node embeddings and graph embedding for downstream tasks. The decoder comprises a dual-layer attention module, which generates solutions (a sequence of input nodes) based on the global state and the embeddings from the encoder. This encoder-decoder architecture constitutes the policy network, which takes instances as input and produces solutions in an auto-regressive manner. The policy network is trained using REINFORCE with a baseline. The experiments indicate that the proposed deep RL (DRL) method demonstrates more solvability efficiency than conventional methods (exact algorithms and heuristic algorithms) and shows superior performance than other DRL-based methods.

The past few decades have witnessed the boom of electric vehicles (EVs) techniques in response to their energy efficiency and reduction of the carbon footprint. However, limited cruising range and sparse charging infrastructure could restrain a massive deployment of EVs. To mitigate the problem, the need for routing and charging scheduling algorithms subject to minimization of time and economic costs emerged. The objective of this paper is to propose an improved ant colony optimization (ACO) and adaptive large neighborhood search (ALNS)-based bilevel algorithm for the solvability of routing and charging scheduling problem of EVs. Specifically, in the first stage, the feasibility of EVs’ journeys is enhanced through two procedures: ameliorating the computation method for individual ants’ node selection probabilities and upgrading ACO’s pheromone update strategy after each iteration. In the second stage, the initial solutions from the final solutions of latter procedure are updated using different destroy and repair operators to optimize heuristic solutions. Finally, the effectiveness and superiority of the proposed algorithm are evaluated by comparisons with two other heuristic algorithms, and it is shown that the proposed algorithm provides better solution performance in terms of less time and economic costs based on the road network model of the Suzhou-Wuxi Highway Network.