Abstract:The design of distributed discrete-time algorithms to cooperatively solve an additive cost optimization problem in multi-agent networks is presented in this paper.The striking feature of the distributed algorithms lies in the use of only the sign of the relative state information between neighbors;which substantially differentiates our algorithms from the existing ones.Moreover,the algorithm does not require the interaction matrix to be doubly stochastic.We first interpret the proposed algorithms in terms of the penalty method in the optimization theory and then perform a non-asymptotic analysis to study the convergence for static network graphs.Compared with the celebrated distributed subgradient algorithms,which,however,use the exact relative state information,the convergence speed in the proposed algorithms is essentially not affected by the loss of information.We also extend our results to the cases of deterministically and randomly time-varying graphs.Finally,we validate the theoretical results through simulations.

Abstract:The convergence speed in the distributed consensus of integrators over both cooperative and competitive undirected networks is investigated in this paper.It has been well known that as long as the (δ,T) connectivity condition is satisfied,exponential convergence of the consensus can be attained.We further show that,for a cooperative network,increasing the coupling strength can improve the convergence speed.The lower bound of the convergence is also explicitly specified with respect to the coupling strength.Based on this,in the competitive network,the convergence of consensus can be guaranteed by strong cooperative couplings dominating the competitive ones.The results of this paper are established by means of a uniformly completely observable theory.We also show that it is possible to reconstruct the consensus error at a certain time instant from the observations of the past relative state information over a fixed length interval.

Abstract:Adaptive fuzzy backstepping fault-tolerant control for a class of nonlinear large-scaleinterconnected switched systems with actuator failures is investigated in this paper.The system switches dynamically according to a piecewise right continuous function.Without loss of generality,stuck faults and the faults loss of effectiveness are considered.Fuzzy logic systems are utilized to approximate the unknown nonlinear functions.Considering the actuator failures,an adaptive fuzzy fault-tolerant controller is designed.It is proved that the proposed control method can guarantee that all the signals of the closed-loop system are bounded according to the Lyapunov theorem.A simulation example is presentedto demonstratethe effectiveness of the proposed control strategy.

Abstract:The feedback control problem in delayed neutral-type neural networks with Le'vy noise is addressed in this paper.Delay is considered as constant in this study.An appropriate Lyapunov function is used to analyze the mean square stability of the closed-loop system.Using the Lyapunov method,the general Itô formula,and the linear matrix inequality technique,the sufficient condition to guarantee stability in mean square sense for the closed-loop system is derived.Finally,a numerical example is given to illustrate the effectiveness of the obtained results.

Abstract:An adaptive controller design for a class of nonlinear single-input single-output systems with partial state constraints is presented in this paper.The approach adopted is to consider the asymmetric dead zone of the non-linear systemsand employ the barrier Lyapunov function(BLF) to prevent partial states from transgressing the constraints.Using a BLF-based backstepping technique,a good tracking performance is obtained and the non-linearity of the dead zone is addressed.For the triangular system,an adaptive controller is designed.It is shown that all the signals in the resulting closed-loop system are bounded,and the system output can track the reference signal.The simulation results show the effectiveness of the proposed method.

Abstract:This paper is concerned with the stabilization of sampled-data-based unmanned surface vehicle (USV)rudder roll closed-loop control system.Considering the delay in the sampling process,we discuss the dynamic delay interval method and construct a corresponding Lyapunov-Krasovskii function (LKF).This method extends the fixed interval to a dynamic interval,which not only relaxes the restriction of upper and lower bounds to the delay interval but also can obtain a much less conservative delay-dependent stability criterion based on linear matrix inequality (LMI).Therefore,one can obtain a much more relaxed criterion to analyze the stability of a sampled-data-based USV rudder roll closed-loop control system.To this end,an example is given to illustrate the effectiveness of the proposed method.

Abstract:Owing to their rapidly increasingapplicationsin aerospace,service,and other fields,manipulators are an area of active in-depth research.In comparison with the rigid manipulator,the flexible manipulator is light,flexible,and highly efficient.It also consumes less energy.The advantages of the flexible manipulator have made it a subject of in-depth study and further research.However,because of the particularity of the structure and build material,the operation of the flexible manipulator produces elastic deformation and vibration,which make the positioning and tracking of the manipulator difficult.Thus,it is important to study vibration suppression.In this paper,the assumed mode method is used to model the single-link flexible manipulator system andthe Lyapunov direct method is used to realize the stability of the closed-loop system.Giventhe particular constrained targets in practical use,the tangent-function form of the Lyapunov strategy is utilized to deal with the output constraints.The neural network control method is used to approach the uncertainty of the system,and the stability of the closed-loop system is analyzed by the Lyapunov method.The control performance of the controller is verified through simulations in MATLAB and experiments using the Quanser platform.

Abstract:This paper studiesthe distributed state estimation problem of large-scale grid with sensor failures in spoofing attacks.The topological relations are introduced to describe the relationship between distributed sensor nodes.A Bernoulli random sequence is used to describe the spoofing attacks and its random characteristics.This article based on Lyapunov method proves the mean square stability and the H_∞ stability of this system.Based on the analysis results and LMI,a distributed state estimator which satisfies the H_∞ performance constraint is designed.Finally,the validity of the designed estimator is verified by numerical simulation.

Abstract:An event-based security control for networked control systems(NCSs) under two-channel denial of service(DoS) attacks is presented in this paper.First,a model is proposed to describe the DoS attacks with a compensation strategy and applied to the sensor-controller channel and controller-actuator channel of the NCSs.Second,an event-triggered mechanism is proposed to induce the burden of information transmission.By definition,information can transfer only when the trigger condition is met.According to the optimal control theory and linear matrix inequality,sufficient conditions of the closed-loop system with a certain probability of the input-to-state security are derived,and the controller is designed.Finally,the effectiveness of the controller is verified through computer simulation.

Abstract:This paper is concerned with the stability and l₂-gain performance for a class of discrete-time Lur'e systems with an asynchronous controller.A hidden Markov model (HHM) is introduced to describe the asynchronization that appears between the designed controller and the original system.The linear matrix inequality (LMI) approach is utilized to analyze the stability of the closed-loop system and l₂-gain performance.Then a sufficient condition is proposed to guarantee the stochastic stability of the closed-loop system,and to minimize the obtained l₂-gain from the disturbance to output.Thus,an asynchronous controller consisting of both linear state feedback and cone-bounded nonlinear output feedback can be designed by solving the given conditions.A simulation example is given to demonstrate the effectiveness of the proposed method.

Abstract:An event-triggered H_∞ state estimation problem is investigated in this paper for a two-degrees-of-freedom(2-DOF) quarter-car suspension system operated over a switching-channel network environment.First,the channelswitching is governed by a Markov chain.Then,a Markov jump linear system model is adopted to represent the overall networked system in accordance with the event-triggered communication scheme,signal quantization,and random packet losses on account of the limited network bandwidth.Using the Lyapunov functional and linear matrix inequality method,the event-triggered H_∞ state estimation problem is transformed into an optimization problem,theswitching-channel-dependent filters of which are designed such that the filter error system is exponentially stable in the mean square sense and achieves the desired performance level.Finally,a simulation example is used to demonstrate the validity of the proposed design.

Abstract:The problem of output feedback control for the Markovian jump system with stochastic noise is investigated in this paper.Under the stochastic disturbance and input perturbation,the proposed dynamic output feedback resilient controller guarantees that the closed-loop system is asymptotically stable in probability.Combining the stochastic differential equation theory with the linear matrix inequality technique,a sufficient condition is obtained to ensure the asymptotic stability in the probability of the system,and the feasible solution of controller gains is presented simultaneously.Finally,a numerical example and a helicopter linear systemare are given to illustrate the practicality of the proposed method.

Abstract:The UHF-RFID-based moving-target localization problem is investigated in this paper.In addition,a novel localization method,named B-AFK method,which integratesthe adaptive Kalman filter and the conventional BVIRE method,is presented.First,the UHF-RFID system is used to initialize the location of a mobile robot.Then,considering the uncertainties in the model and the statistical properties of the UHF-RFID localization system,an adaptive Kalman filter method is used to predict and update the robot's position.Further,an adaptive factor is introduced to compensate for the uncertainties in the noise covariance.Finally,a UHF-RFID-based localization platform is established and experiments are carried out to show that the proposed method outperforms the conventional BVIRE and linear Kalman filter in terms of precision and robustness.

Abstract:For the problem of how to query,retrieve,and organize music information quickly and efficiently,the performance of the music retrieval system can be improved through automatic music annotation technology.In this study,a multi-label music automatic annotation system based on generative adversarial networks(GANs) is proposed.The LDA model is used to cluster the music tags to obtain thematic categories,and then the mapping relationship between the audio features and the semantic features of the music is found by the generative adversarial network.For experimental verification,when the method proposed in this paper was applied to the CAL500 dataset in five cross-validation experiments,the comprehensive performance index of the method was greatly improved compared with existing methods.

Abstract:The accurate prediction of the number of summer precipitation days has important practical significance for industries such as agriculture,transportation,and electric power supply.The data of summer precipitation during 1951-2012 provided by Lianyungang Meteorological Bureau were used to analyze the interannual characteristics of summer precipitation days,yet no obvious temporal variation trends were found.Thus a model to predict the regularity of precipitation days is established based on analysis of climate factors listed by National Climate Center website,and CART decision tree algorithm.Year with positive/negative anomalies of summer precipitation days in Lianyungang is defined by various climatic factors,which is trained by sample data of 52 years with training accuracy of 90.38%/86.54%.The remaining data of 10 years are used to test the model,resulting in accuracy of 80% for positive/negative anomalies of summer precipitation days prediction.The rule set is provided for meteorological business and decision-making.

Abstract:Based a generic method,a class of linear codes with two nonzero weights wasconstructed by choosing a new definition set.Utilizing the exponential sums,the weight distribution of the proposed linear code was derived.Furthermore,it was shown that the constructed linear codes were minimum linear codes,and their application in secret sharing schemes was demonstrated.