Adaptive Sensor-Fault Tolerant Control of Unmanned Underwater Vehicles With Input Saturation

This paper investigates the tracking control problem for unmanned underwater vehicles(UUVs)systems with sensor faults,input saturation,and external disturbance caused by waves and ocean currents.An active sensor fault-tolerant control scheme is proposed.First,the developed method only requires the inertia matrix of the UUV,without other dynamic information,and can handle both additive and multiplicative sensor faults.Subsequently,an adaptive fault-tolerant controller is designed to achieve asymptotic tracking control of the UUV by employing robust integral of the sign of error feedback method.It is shown that the effect of sensor faults is online estimated and compensated by an adaptive estimator.With the proposed controller,the tracking error and estimation error can asymptotically converge to zero.Finally,simulation results are performed to demonstrate the effectiveness of the proposed method.

Self-triggered Consensus Control for Linear Multi-agent Systems With Input Saturation

In this paper, we study the consensus problem for a class of linear multi-agent systems(MASs) with consideration of input saturation under the self-triggered mechanism. In the context of discrete-time systems, a self-triggered strategy is developed to determine the time interval between the adjacent triggers. The triggering condition is designed by using the current sampled consensus error. Furthermore, the consensus control protocol is designed by means of a state feedback approach. It is shown that the considered multi-agent systems can reach consensus with the presented algorithm. Some sufficient conditions are proposed in the form of linear matrix inequalities(LMIs) to show the positively invariant property of the domain of attraction(DOA). Moreover, some sufficient conditions of controller synthesis are provided to enlarge the volume of the DOA and obtain the control gain matrix. A numerical example is simulated to demonstrate the effectiveness of the theoretical analysis results.

Biological roles and potential clinical values of circular RNAs in gastrointestinal malignancies

Circular RNAs(circ RNAs),a class of endogenous RNA molecules,are produced by alternative splicing of precursor RNA and are covalently linked at the 5′and 3′ends.Recent studies have revealed that dysregulated circ RNAs are closely related to the occurrence and progression of gastrointestinal malignancies.Accumulating evidence indicates that circ RNAs,including circ PVT1,circ LARP4,circ-SFMBT2,cir-ITCH,circ RNA_100782,circ_100395,circ-DONSON,hsa_circ_0001368,circ NRIP1,circ FAT1(e2),circ CCDC66,circ SMARCA5,circ-ZNF652,and circ_0030235 play important roles in the proliferation,differentiation,invasion,and metastasis of cancer cells through a variety of mechanisms,such as acting as micro RNA sponges,interacting with RNA-binding proteins,regulating gene transcription and alternative splicing,and being translated into proteins.With the characteristics of high abundance,high stability,extensive functions,and certain tissue-,time-and diseasespecific expressions,circ RNAs are expected to provide novel perspectives for the diagnoses and treatments of gastrointestinal malignancies.

Correlation and Significance of Midkine and Estrogen Receptor Beta Protein Expression in Non-Small Cell Lung Cancer

OBJECTIVE Midkine (MK),a new member of the heparin-binding growth factor family,was found recently to have a highexpression level in many carcinoma specimens,including thoseof the esophagus,gall bladder,pancreas,colorectum,breast andlung.Estrogen receptor beta (ER-β),a recently cloned estrogenreceptor subtype,was also found to be highly expressed in lungtumor tissue,in contrast to a lower level of expression in normallung tissue.However,few relevant studies on these proteins havebeen published.The aims of our study were to investigate theexpression of midkine and ER-β proteins in non-small cell lungcancer (NSCLC) and to examine the relationship between theirexpression and the clinicopathologic data as well as to analyse thecorrelation of their expression in NSCLC.METHODS By immunohistochemistry,MK and ER-β were ex-amined in 24 surgically resected cases of NSCLC with their corre-sponding paraneoplastic and normal lung tissues.RESULTS MK and ER-β were overexpressed in NSCLC.Thelevels of MK and ER-β expression in NSCLC were found to be sig-nificantly negatively correlated with the pathological classification(P=0.042 and 0.021,respectively),and their expression decreasedwith a raise in the classification.Spearman's correlation analysisshowed that the correlation of their expression in NSCLC wasstrong (correlation coefficient[r_s]= 0.535,P=0.007<0.01).CONCLUSION The expression levels of MK and ER-β to someextent reflect the malignant degree of NSCLC,and their combineddetection may be of great value in early diagnosis,treatments ofpatients with NSCLC and can predict the prognoses.

Distributed Asymptotic Consensus in Directed Networks of Nonaffine Systems With Nonvanishing Disturbance

In this paper the distributed asymptotic consensus problem is addressed for a group of high-order nonaffine agents with uncertain dynamics,nonvanishing disturbances and unknown control directions under directed networks.A class of auxiliary variables are first introduced which forms second-order filters and induces all measurable signals of agents’states.In view of this property,a distributed robust integral of the sign of the error(DRISE)design combined with the Nussbaum-type function is presented that guarantees not only the desired asymptotic consensus,but also the uniform boundedness of all closed-loop variables.Compared with the traditional sliding mode control(SMC)technique,the main feature of our approach is that the integral operation in the proposed control algorithm is designed to be adopted in a continuous manner and ensures less chattering behavior.Simulation results for a group of Duffing-Holmes chaotic systems are employed to verify our theoretical analysis.

Adaptive tracking control of high-order MIMO nonlinear systems with prescribed performance

In this paper,an observer-based adaptive prescribed performance tracking control scheme is developed for a class of uncertain multi-input multi-output nonlinear systems with or without input saturation.A novel finite-time neural network disturbance observer is constructed to estimate the system uncertainties and external disturbances.To guarantee the prescribed performance,an error transformation is applied to transfer the time-varying constraints into a constant constraint.Then,by employing a barrier Lyapunov function and the backstepping technique,an observer-based tracking control strategy is presented.It is proven that using the proposed algorithm,all the closedloop signals are bounded,and the tracking errors satisfy the predefined time-varying performance requirements.Finally,simulation results on a quadrotor system are given to illustrate the effectiveness of the proposed control scheme.

Convergence of time-varying net works and its applications

In this study,we present the convergence of time-varying net works.Then,we apply the convergence property to cooperative control of nonlinear multiagent systems(MASs)with unknown control directions(UCDs),and illustrate a new kind of Nussbaum-type function based control algorithms.It is proven that if the time-varying net works are cut-balance,the convergence of nonlinear MASs with nonidentical UCDs is achieved using the presented algorithms.A critical feature of this application is that the designed algorithms can deal with nonidentical UCDs by employing conventional Nussbaum-type functions.Finally,one simulation example is given to illustrate the effectiveness of the presented algorithms.

Robust distributed model predictive consensus of discrete-time multi-agent systems:a self-triggered approach

This study investigates the consensus problem of a nonlinear discrete-time multi-agent system(MAS)under bounded additive disturbances.We propose a self-triggered robust distributed model predictive control consensus algorithm.A new cost function is constructed and MAS is coupled through this function.Based on the proposed cost function,a self-triggered mechanism is adopted to reduce the communication load.Furthermore,to overcome additive disturbances,a local minimum-maximum optimization problem under the worst-case scenario is solved iteratively by the model predictive controller of each agent.Sufficient conditions are provided to guarantee the iterative feasibility of the algorithm and the consensus of the closed-loop MAS.For each agent,we provide a concrete form of compatibility constraint and a consensus error terminal region.Numerical examples are provided to illustrate the effectiveness and correctness of the proposed algorithm.