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Showing papers on "Convex optimization published in 2022"


Journal ArticleDOI
TL;DR: This paper investigates the secure transmission design for an IRS-assisted UAV network in the presence of an eavesdropper and results validate the effectiveness of the proposed scheme and the performance improvement achieved by the joint trajectory and beamforming design.
Abstract: Despite the wide utilization of unmanned aerial vehicles (UAVs), UAV communications are susceptible to eavesdropping due to air-ground line-of-sight channels. Intelligent reflecting surface (IRS) is capable of reconfiguring the propagation environment, and thus is an attractive solution for integrating with UAV to facilitate the security in wireless networks. In this paper, we investigate the secure transmission design for an IRS-assisted UAV network in the presence of an eavesdropper. With the aim at maximizing the average secrecy rate, the trajectory of UAV, the transmit beamforming, and the phase shift of IRS are jointly optimized. To address this sophisticated problem, we decompose it into three sub-problems and resort to an iterative algorithm to solve them alternately. First, we derive the closed-form solution to the active beamforming. Then, with the optimal transmit beamforming, the passive beamforming optimization problem of fractional programming is transformed into corresponding parametric sub-problems. Moreover, the successive convex approximation is applied to deal with the non-convex UAV trajectory optimization problem by reformulating a convex problem which serves as a lower bound for the original one. Simulation results validate the effectiveness of the proposed scheme and the performance improvement achieved by the joint trajectory and beamforming design.

99 citations


Journal ArticleDOI
TL;DR: Wang et al. as discussed by the authors proposed a new nonconvex penalty called generalized logarithm(G-log) penalty, which enhances the sparsity and reduces noise disturbance.

71 citations


Journal ArticleDOI
TL;DR: In this article , a novel convexity-oriented time-dependent reliability-based topology optimization (CTRBTO) framework is investigated with overall consideration of universal uncertainties and time-varying natures in configuration design.

61 citations


Journal ArticleDOI
TL;DR: Based on fuzzy relational inequality, a bi-level linear program optimizes the visible light brightness and operating costs of access points in a wireless transmission station system as mentioned in this paper , which has been shown to be both practical and successful.

54 citations


Journal ArticleDOI
TL;DR: In this paper , a waveform optimization model accounting for the minimization of the beampattern integrated sidelobe level (ISL) along with the mainlobe width, peak-to-average power ratio, and energy constraints, as well as multispectral requirements where the interference energy injected by the MIMO radar in each shared frequency band in a particular direction, is precisely controlled to ensure the desired quality of service at each communication system.
Abstract: This article deals with the multiple-input–multiple-output (MIMO) radar beampattern design in an effort to the coexistence with multiple communication systems. A waveform optimization model accounting for the minimization of the beampattern integrated sidelobe level (ISL) along with the mainlobe width, peak-to-average power ratio, and energy constraints, as well as multispectral requirements where the interference energy injected by the MIMO radar in each shared frequency band in a particular direction, is precisely controlled to ensure the desired quality of service at each communication system. Through an equivalent reformulation of the original nonconvex problem, a polynomial-time sequential convex approximation (SCA) procedure that involves the tackling of a series of constrained convex problems is proposed to monotonically decrease the ISL with the convergence guaranteed to a Karush–Kuhn–Tucker point. Herein, to speed up the convergence, a fast iterative algorithm based on the alternating-direction-method-of-multipliers framework is introduced to globally solve the convex problems during each iteration of the SCA procedure. Numerical results are provided to assess the proposed algorithm in terms of the computational complexity, the achieved beampattern, and spectral compatibility with some competitive counterparts available in the open literatures.

33 citations


Journal ArticleDOI
TL;DR: Under imperfect CSI, this paper provides a new optimization framework for energy-efficient transmission in AmBC enhanced NOMA cooperative vehicle-to-everything (V2X) networks by optimizing the power allocation at BS and reflection at backscatter sensors while guaranteeing the individual quality of services.
Abstract: —Automotive-Industry 5.0 will use beyond fifth- generation (B5G) technologies to provide robust, computationally intelligent, and energy-efficient data sharing among various onboard sensors, vehicles, and other devices. Recently, ambient backscatter communications (AmBC) have gained significant interest in the research community for providing battery-free communications. AmBC can modulate useful data and reflect it towards near devices using the energy and frequency of existing RF signals. However, obtaining channel state infor- mation (CSI) for AmBC systems would be very challenging due to no pilot sequences and limited power. As one of the latest members of multiple access technology, non-orthogonal multiple access (NOMA) has emerged as a promising solution for connecting large-scale devices over the same spectral resources in B5G wireless networks. Under imperfect CSI, this paper provides a new optimization framework for energy-efficient transmission in AmBC enhanced NOMA cooperative vehicle-to-everything (V2X) networks. We simultaneously minimize the total transmit power of the V2X network by optimizing the power allocation at BS and reflection coefficient at backscatter sensors while guaranteeing the individual quality of services. The problem of total power minimization is formulated as non-convex optimization and coupled on multiple variables, making it complex and challenging. Therefore, we first decouple the original problem into two sub-problems and convert the nonlinear rate constraints into linear constraints. Then, we adopt the iterative sub-gradient method to obtain an efficient solution. For comparison, we also present a conventional NOMA cooperative V2X network without AmBC. Simulation results show the benefits of our proposed AmBC enhanced NOMA cooperative V2X network in terms of total achievable energy efficiency. —Beyond 5G, ambient backscatter communica- tions (AmBC), non-orthogonal multiple access (NOMA), vehicle-to-everything (V2X), imperfect channel state information (CSI).

28 citations


Journal ArticleDOI
TL;DR: The distributed convex optimization problem for a class of nonlinear multi-agent systems disturbed by random noise over a directed graph is investigated and the best system parameters such that the second moment of the estimation error has the minimum value are determined.

25 citations


Journal ArticleDOI
TL;DR: This letter proposes a robust beamforming design to minimize the total transmit power by jointly optimizing the transmit beamforming and phase shifts of IRS subject to the outage rate probability constraints.
Abstract: In this letter, we study intelligent reflecting surface (IRS) aided simultaneous wireless information and power transfer (SWIPT) terahertz secure systems under a non-linear energy harvesting (EH) model. Assuming that the cascaded channel state information is imperfect, we propose a robust beamforming design to minimize the total transmit power by jointly optimizing the transmit beamforming and phase shifts of IRS subject to the outage rate probability constraints. We first transform the outage constraints into deterministic forms by using the Bernstein-type inequality. Then, we applying semidefinite programming to change the original non-convex problem into an equivalent convex problem, and develope an alternate iterative optimization algorithm to obtain a feasible solution of the original problem. Finally, simulation results validate the effectiveness of our proposed scheme.

21 citations


Journal ArticleDOI
TL;DR: Simulation results demonstrate that the proposed robust secure beamforming scheme can effectively improve ASR, and also outperforms the nonrobust one.
Abstract: This paper investigates a full-duplex (FD) secure communication system with the assistance of an intelligent reflecting surface (IRS). Compared with the traditional FD system, the IRS-assisted FD communication not only greatly improves the spectrum efficiency but also provides a new way to enhance physical layer security due to the overlapping of multiple signals at the eavesdropper. Furthermore, we consider a more practical scenario without perfect channel state information (CSI) because it is very difficult to obtain the perfect CSI especially for cascaded channels via IRS. In addition, the eavesdropper is usually passive and hidden which will not actively exchange CSI with the user, which leads to an obstacle for obtaining the perfect CSI of eavesdropping channels. To this end, a worst-case achievable security rate (ASR) optimization problem is formulated under the bounded CSI error model. Due to the existence of non-convexity and highly coupled variables, this problem is extremely challenging. To directly tackle the nonconvexity of the considered optimization problem, similar to successive convex approximation (SCA), we first transform the original problem into its equivalent convex optimization problem directly, and finally obtain the optimal solution of the original non-convex problem by iteratively calculating the convex optimization problem. On this basis, we iteratively solve the transmission beamforming and IRS phase shift through Alternate Optimization (AO). In particular, when optimizing the phase shift coefficient, a penalty convex-concave procedure solution is proposed. Simulation results demonstrate that our proposed robust secure beamforming scheme can effectively improve ASR, and also outperforms the nonrobust one.

20 citations


Journal ArticleDOI
TL;DR: In this article , a model predictive control (MPC) for discrete-time Markov jump systems (MJSs) is investigated and an asynchronous MPC controller is designed to tackle the issue of asynchronization between the modes of the controller and those of the plant.
Abstract: This article investigates the model predictive control (MPC) for discrete-time Markov jump systems (MJSs). First, the asynchronization between the modes of the controller and those of the plant is studied. An asynchronous MPC controller is designed to tackle this issue. Next, to reduce the computational cost and communication burden, a version of the dynamic event-triggered mechanism (ETM) is presented. Finally, the exogenous disturbances are considered and the notion of mean-square input-to-state stability (ISS) is taken into account in the controller design. The highlight of this article is the introduction of both dynamic ETM and asynchronous control into the MPC design. The control algorithm is developed and formulated as a convex optimization problem. Moreover, the recursive feasibility and the closed-loop mean-square ISS are both studied. Finally, some simulations are given to show the effectiveness of the derived MPC method.

17 citations


Journal ArticleDOI
TL;DR: In this paper , a multi-UAV enabled mobile Internet of Vehicles (IoV) model is proposed, where the UAVs track to serve the mobile vehicles and send downlink information to the vehicles during the flight time.
Abstract: Due to its flexibility and high maneuverability, Unmanned Aerial Vehicle (UAV) is able to quickly provide wireless connections to the ground vehicles in mobile environment. In this paper, a multi-UAV enabled mobile Internet of Vehicles (IoV) model is proposed, where the UAVs track to serve the mobile vehicles and send downlink information to the vehicles during the flight time. Considering the constraints of anti-collision and communication interference between the UAVs, the system throughput is maximized by jointly optimizing vehicle communication scheduling, UAV power allocation and UAV trajectory. The formulated non-convex optimization problem is separated into three subproblems, including communication scheduling optimization, power allocation optimization and UAV trajectory optimization, which can be solved by successive convex approximation (SCA). A joint iterative optimization algorithm of the three subproblems is put forward to get the optimal solution. Then, a fairness optimization problem is proposed to guarantee the fair communications for each vehicle. The numerical results reveal the excellent performance of the multi-UAV enabled mobile IoV by joint communication and trajectory optimization.

Journal ArticleDOI
TL;DR: In this paper, the authors characterize the solution of a broad class of convex optimization problems that address the reconstruction of a function from a finite number of linear measurements, and derive the generic parametric representation of the solution components.

Journal ArticleDOI
TL;DR: QPDO as mentioned in this paper is a primal-dual method for convex quadratic programs which builds upon and weaves together the proximal point algorithm and a damped semismooth Newton method.
Abstract: Abstract This paper introduces QPDO, a primal-dual method for convex quadratic programs which builds upon and weaves together the proximal point algorithm and a damped semismooth Newton method. The outer proximal regularization yields a numerically stable method, and we interpret the proximal operator as the unconstrained minimization of the primal-dual proximal augmented Lagrangian function. This allows the inner Newton scheme to exploit sparse symmetric linear solvers and multi-rank factorization updates. Moreover, the linear systems are always solvable independently from the problem data and exact linesearch can be performed. The proposed method can handle degenerate problems, provides a mechanism for infeasibility detection, and can exploit warm starting, while requiring only convexity. We present details of our open-source C implementation and report on numerical results against state-of-the-art solvers. QPDO proves to be a simple, robust, and efficient numerical method for convex quadratic programming.

Journal ArticleDOI
TL;DR: In this article , an intelligent reflecting surface (IRS)-assisted wireless powered communication network (WPCN) architecture is proposed for power-constrained Internet-of-Things (IoT) smart devices, where IRS is exploited to improve the performance of WPCN under imperfect channel state information (CSI).
Abstract: In this paper, a novel intelligent reflecting surface (IRS)-assisted wireless powered communication network (WPCN) architecture is proposed for power-constrained Internet-of-Things (IoT) smart devices, where IRS is exploited to improve the performance of WPCN under imperfect channel state information (CSI). We formulate a hybrid access point (HAP) transmit energy minimization problem by jointly optimizing time allocation, HAP energy beamforming, receiving beamforming, user transmit power allocation, IRS energy reflection coefficient and information reflection coefficient under the imperfect CSI and non-linear energy harvesting model. On account of the high coupling of optimization variables, the formulated problem is a non-convex optimization problem that is difficult to solve directly. To address the above-mentioned challenging problem, alternating optimization (AO) technique is applied to decouple the optimization variables to solve the problem. Specifically, through AO, time allocation, HAP energy beamforming, receiving beamforming, user transmit power allocation, IRS energy reflection coefficient and information reflection coefficient are divided into three sub-problems to be solved alternately. The difference-of-convex (DC) programming is used to solve the non-convex rank-one constraint in solving IRS energy reflection coefficient and information reflection coefficient. Numerical simulations verify the superiority of the proposed optimization algorithm in decreasing HAP transmit energy compared with other benchmark schemes.

Journal ArticleDOI
TL;DR: In this article, a two-stage robust optimization at the hourly level is proposed to minimize the network loss without the minute level optimization, which directly uses historical data with minimal mathematical knowledge.


Journal ArticleDOI
TL;DR: In this article , a convex formulation of the optimal control problem with a discounted cost function is presented, which relies on lifting nonlinear system dynamics in the space of densities using the linear Perron-Frobenius operator.

Journal ArticleDOI
TL;DR: A new approach based on a Riemannian manifold, which is the product of complex circles and a Euclidean space is proposed to improve SINR (signal-to-interference-plus-noise ratio), which can achieve higher SINr via different input SNR while consumes a lower computation time.

Proceedings ArticleDOI
TL;DR: In this article , the optimal control problem of nonlinear systems under safety constraints with unknown dynamics is considered, and the problem can be formulated as an infinite-dimensional convex optimization over occupancy measures.
Abstract: This letter considers the optimal control problem of nonlinear systems under safety constraints with unknown dynamics. Departing from the standard optimal control framework based on dynamic programming, we study its dual formulation over the space of occupancy measures. For control-affine dynamics, with proper reparametrization, the problem can be formulated as an infinite-dimensional convex optimization over occupancy measures. Moreover, the safety constraints can be naturally captured by linear constraints in this formulation. Furthermore, this dual formulation can still be approximately obtained by utilizing the Koopman theory when the underlying dynamics are unknown. Finally, to develop a practical method to solve the resulting convex optimization, we choose a polynomial basis and then relax the problem into a semi-definite program (SDP) using sum-of-square (SOS) techniques. Simulation results are presented to demonstrate the efficacy of the developed framework.

Journal ArticleDOI
TL;DR: In this paper , the authors proposed a UAV-mounted RIS (UIRS) communication system for confidential data dissemination from an access point (AP) toward multiple ground user equipments (UEs) in IoT networks.
Abstract: Unmanned aerial vehicles (UAVs) are envisioned to be extensively employed for assisting wireless communications in the Internet of Things (IoT). On the other hand, terahertz (THz)-enabled intelligent reflecting surface (IRS) is expected to be one of the core enabling technologies for forthcoming beyond-5G (B5G) wireless communications that promise a broad range of data-demand applications. In this article, we propose a UAV-mounted IRS (UIRS) communication system over THz bands for confidential data dissemination from an access point (AP) toward multiple ground user equipments (UEs) in IoT networks. Specifically, the AP intends to send data to the scheduled UE, while unscheduled UEs may behave as potential adversaries. To protect information messages from the privacy preservation perspective, we aim to devise an energy-efficient multi-UAV covert communication scheme, where the UIRS is for reliable data transmissions, and an extra UAV is utilized as an aerial cooperative jammer, opportunistically generating artificial noise (AN) to degrade unscheduled UEs detection, leading to communication covertness improvement. This poses a novel max-min optimization problem in terms of minimum average energy efficiency (mAEE), aiming to improve covert throughput and reduce UAVs’ propulsion energy consumption, subject to satisfying some practical constraints such as the covertness requirements for which we obtain analytical expressions. Since the optimization problem is nonconvex, we tackle it via the block successive convex approximation (BSCA) approach to iteratively solve a sequence of approximated convex subproblems, designing the binary user scheduling, AP’s power allocation, maximum AN jamming power, IRS beamforming, and both UAVs’ trajectory and velocity planning. Finally, we present a low-complex overall algorithm for system performance enhancement with complexity and convergence analysis. Numerical results are provided to verify the analysis and demonstrate significant outperformance of our design over other existing benchmark schemes concerning the mAEE performance.

Journal ArticleDOI
01 Oct 2022
TL;DR: In this article , the authors provide a comprehensive tutorial of three major convex optimization-based trajectory generation methods: lossless convexification (LCvx), two sequential convex programming algorithms, and guaranteed sequential trajectory optimization (GuSTO).
Abstract: Reliable and efficient trajectory generation methods are a fundamental need for autonomous dynamical systems. The goal of this article is to provide a comprehensive tutorial of three major convex optimization-based trajectory generation methods: lossless convexification (LCvx) and two sequential convex programming algorithms, successive convexification (SCvx) and guaranteed sequential trajectory optimization (GuSTO). Trajectory generation is defined as the computation of a dynamically feasible state and control signal that satisfies a set of constraints while optimizing key mission objectives. The trajectory generation problem is almost always nonconvex, which typically means that it is difficult to solve efficiently and reliably onboard an autonomous vehicle. The three algorithms that we discuss use problem reformulation and a systematic algorithmic strategy to nonetheless solve nonconvex trajectory generation tasks using a convex optimizer. The theoretical guarantees and computational speed offered by convex optimization have made the algorithms popular in both research and industry circles. The growing list of applications includes rocket landing, spacecraft hypersonic reentry, spacecraft rendezvous and docking, aerial motion planning for fixed-wing and quadrotor vehicles, robot motion planning, and more. Among these applications are high-profile rocket flights conducted by organizations such as NASA, Masten Space Systems, SpaceX, and Blue Origin. This article equips the reader with the tools and understanding necessary to work with each algorithm and know their advantages and limitations. An open source tool called the SCP Toolbox accompanies the article and provides a practical implementation of every numerical example. By the end of the article, the reader will not only be ready to use the lossless convexification and sequential convex programming algorithms, but also to extend them and to contribute to their many exciting modern applications.

Journal ArticleDOI
TL;DR: In this article , a distributed estimation and control problem for time-varying large-scale interconnected systems (LISs) is considered, and a decoupling strategy with sequential-structure is developed to deal with the interconnected terms in large scale systems.
Abstract: This article is concerned with the distributed estimation and control problem for time-varying large-scale interconnected systems (LISs). A novel decoupling strategy with sequential-structure is developed to deal with the interconnected terms in large-scale systems. Then, by using the idea of bounded recursive optimization, the local estimator gain for each subsystem is designed by solving self-relative convex optimization problem that is constructed based on each subsystem’s own information and its neighboring information. In this case, such design scheme of each local estimator can realize fully distributed estimation. Based on the distributed estimator, fully distributed estimator-based control method is also designed by constructing self-relative convex optimization problems. Notice that the solutions to the above-constructed convex optimization problems can be easily obtained by the standard software packages, and the computational complexity of each optimization problem is low even though the scale of interconnected systems is large. Furthermore, stability conditions are derived such that the designed distributed estimator and controller for time-varying LISs are asymptotically bounded. Finally, two illustrative examples are employed to show the effectiveness of the proposed methods.

Journal ArticleDOI
01 Jan 2022
TL;DR: In this article, a computationally efficient optimization-based approach is proposed to ensure safety of dynamical systems without introducing undesired equilibria even in the presence of multiple non-convex unsafe sets.
Abstract: This letter presents an approach to deal with safety of dynamical systems in presence of multiple non-convex unsafe sets. While optimal control and model predictive control strategies can be employed in these scenarios, they suffer from high computational complexity in case of general nonlinear systems. Leveraging control barrier functions, on the other hand, results in computationally efficient control algorithms. Nevertheless, when safety guarantees have to be enforced alongside stability objectives, undesired asymptotically stable equilibrium points have been shown to arise. We propose a computationally efficient optimization-based approach which allows us to ensure safety of dynamical systems without introducing undesired equilibria even in presence of multiple non-convex unsafe sets. The developed control algorithm is showcased in simulation and in a real robot navigation application.

Journal ArticleDOI
TL;DR: In this article, the convergence rates of the iteratively regularized Gauss-Newton method were derived by defining the iterates via convex optimization problems in a Banach space setting.

Journal ArticleDOI
TL;DR: In this article , the authors investigated the delay sensitive secure transmission problem in unmanned aerial vehicle (UAV)-relayed vehicular ad hoc networks (VANETs) and formulated this problem as a total information delay minimization problem by optimizing the UAV relay trajectory and channel allocation.
Abstract: This paper investigates the delay sensitive secure transmission problem in unmanned aerial vehicle (UAV)-relayed vehicular ad hoc networks (VANETs). Specifically, we take the security assurance into account and formulate this problem as a total information delay minimization problem by optimizing the UAV relay trajectory and channel allocation. To tackle this optimization problem, we propose an alternating optimization framework, where the UAV relay trajectory is solved by the Newton method, and the channel allocation is obtained by relax-and-round and sequential convex approximation methods. Finally, simulation results show the performance improvement of our algorithm compared with the current works in terms of the total information delay.

Journal ArticleDOI
TL;DR: In this paper , the authors reviewed the component sizing and energy management issues in electrified vehicles and summarized a variety of convex optimization methods for solving these problems and discussed the prospects and future trends of the convex optimisation method in the design and control of EVs.
Abstract: Component sizing and energy management are essential for minimizing vehicle costs and maximizing energy efficiency in electrified vehicles. Usually, a hierarchical optimization framework is used to solve the component sizing problem. The possible component sizes are enumerated in the outer loop, and their effectiveness is assessed in the inner loop. When optimizing energy consumption, highly robust and effective energy management strategies are important for both individual vehicles and vehicle platoons. Convex optimization has become an effective and important method to solve multi-dimensional problems due to its computational efficiency. In this paper, we reviewed the component sizing and energy management issues in electrified vehicles and summarized a variety of convex optimization methods for solving these problems. The prospects and future trends of the convex optimization method in the design and control of electrified vehicles were presented and discussed.

Journal ArticleDOI
TL;DR: In this paper , an adaptive moment estimation (ADMM) and ADMM-ADAM (Alternating Direction Method of Multipliers and Adaptive Moment Estimation) framework is proposed for solving inverse problems.
Abstract: Alternating direction method of multipliers (ADMM) and adaptive moment estimation (ADAM) are two optimizers of paramount importance in convex optimization (CO) and deep learning (DL), respectively. Numerous state-of-the-art algorithms for solving inverse problems are achieved by carefully designing a convex criterion, typically composed of a data-fitting term and a regularizer. Even when the regularizer is convex, its mathematical form is often sophisticated, hence inducing a math-heavy optimization procedure and making the algorithm design a daunting task for software engineers. Probably for this reason, people turn to solve the inverse problems via DL, but this requires big data collection, quite time-consuming if not impossible. Motivated by these facts, we propose a new framework, termed as ADMM-ADAM, for solving inverse problems. As the key contribution, even just with small/single data, the proposed ADMM-ADAM is able to exploit DL to obtain a convex regularizer of very simple math form, followed by solving the regularized criterion using simple CO algorithm. As a side contribution, a state-of-the-art hyperspectral inpainting algorithm is designed under ADMM-ADAM, demonstrating its superiority even without the aid of big data or sophisticated mathematical regularization.

Journal ArticleDOI
TL;DR: In this article, the authors reviewed the component sizing and energy management issues in electrified vehicles and summarized a variety of convex optimization methods for solving these problems and discussed the prospects and future trends of the convex optimisation method in the design and control of EVs.
Abstract: Component sizing and energy management are essential for minimizing vehicle costs and maximizing energy efficiency in electrified vehicles. Usually, a hierarchical optimization framework is used to solve the component sizing problem. The possible component sizes are enumerated in the outer loop, and their effectiveness is assessed in the inner loop. When optimizing energy consumption, highly robust and effective energy management strategies are important for both individual vehicles and vehicle platoons. Convex optimization has become an effective and important method to solve multi-dimensional problems due to its computational efficiency. In this paper, we reviewed the component sizing and energy management issues in electrified vehicles and summarized a variety of convex optimization methods for solving these problems. The prospects and future trends of the convex optimization method in the design and control of electrified vehicles were presented and discussed.

Journal ArticleDOI
TL;DR: The proposed method has a relatively good robustness in localization even under the circumstance that the prior knowledge of the NLOS links or its distribution does not know, and is demonstrated in the simulations compared with other state-of-the-art techniques.
Abstract: This paper addresses the target localization problem using time-of-arrival (TOA)-based technique under the non-line-of-sight (NLOS) environment. To alleviate the adverse effect of the NLOS error on localization, a total least square framework integrated with a regularization term (RTLS) is utilized, and with which the localization problem can get rid of the ill-posed issue. However, it is challenging to figure out the exact solution for the considered localization problem. In this case, we convert the RTLS problem into a semidefinite program (SDP), and then obtain the solution of the original problem by solving a generalized trust region subproblem (GTRS). The proposed method has a relatively good robustness in localization even under the circumstance that the prior knowledge of the NLOS links or its distribution does not know. The outperformance of the proposed method is demonstrated in the simulations compared with other state-of-the-art techniques.

Journal ArticleDOI
01 Jul 2022
TL;DR: In this paper , the authors investigated quantized fuzzy control for discrete-time switched nonlinear singularly perturbed systems, where the singularized perturbed parameter (SPP) is employed to represent the degree of separation between the fast and slow states.
Abstract: This article investigates the problem of quantized fuzzy control for discrete-time switched nonlinear singularly perturbed systems, where the singularly perturbed parameter (SPP) is employed to represent the degree of separation between the fast and slow states. Taking a full account of features in such switched nonlinear systems, the persistent dwell-time switching rule, the technique of singular perturbation and the interval type-2 Takagi-Sugeno fuzzy model are introduced. Then, by means of constructing SPP-dependent multiple Lyapunov-like functions, some sufficient conditions with the ability to ensure the stability and an expected H∞ performance of the closed-loop system are deduced. Afterward, through solving a convex optimization problem, the gains of the controller are obtained. Finally, the correctness of the proposed method and the effectiveness of the designed controller are demonstrated by an explained example.