In time-dependent reliability analysis, the first-passage method has been extensively used to evaluate structural reliability under time-variant service circumstances. To avoid computing the outcrossing rate in this method, surrogate modeling may provide an effective alternative for calculating the time-dependent reliability indices in structural analysis. A novel approach, namely time-dependent reliability analysis with response surface (TRARS), is thus introduced in this paper to estimate the time-dependent reliability for nondeterministic structures under stochastic loads. A Gaussian stochastic process is generated by using the expansion optimal linear estimation (EOLE) method which has proven to be more accurate and efficient than some series expansion discretization techniques. The random variables and maximum responses of uncertain structures are treated as the input and output parameters, respectively. Through introducing the response surface (RS) model, a novel iterative procedure is proposed in this study. A Bucher strategy is adopted to generate the initial sample points, and a gradient projection technique is used to generate new sampling points for updating the RS model in each iteration. The time-dependent reliability indices and probabilities of failure are thus obtained efficiently using the first-order reliability method (FORM) over a certain design lifetime. In this study, four demonstrative examples are provided for illustrating the accuracy and efficiency of the proposed method.

This paper addresses dynamic authority allocation strategy and security-based resilient event triggered output feedback lane keeping control (LKC) problem for human-machine cooperative (HMC) steering intelligent heavy truck subject to non-periodic energy bounded denial-of-service (DoS) attacks. For authority allocation strategy of considered LKC system, designed practical and easily-to-implement cooperative allocation factor is adaptively adjusted by the correlation characteristics between buffered interested states for the first time. For derived fuzzy model of networked LKC system with time-varying cooperative allocation factor and path tracking velocity, a new security-based resilient event triggered output feedback control with communication scheme consisting of adaptive adjusted auxiliary variable and attack-induced random but bounded uncertainty is proposed to reduce communication burden when free of attacks and simultaneously release necessary data for stability maintenance at the cost of predefined allowable control performance degradation when attack-induced uncertainty less than its upper bound. Furthermore, sufficient conditions are derived for exponentially stability of considered LKC system with $H\infty $ performance, gain matrices for controller and trigger are co-designed and obtained by solving certain matrix inequalities. Benefiting from saved communication resource and more sparse but necessary assistant torque commands under resilient event triggered communication scheme, as well as dynamic authority allocation strategy with adaptive cooperative allocation factor, the obtained results illustrating fewer driving conflict, less driver effort and higher resistant ability to attack-induced yaw instability demonstrate the effectiveness of the proposed control strategy.

The uncertain variables of the link dimensions and joint clearances, whose deviation is caused by manufacturing and assembling errors, have a considerable influence on the positioning accuracy of industrial robots. Understanding how these uncertain variables affect the positioning accuracy of industrial robots is very important to select appropriate parameters during design process. In this paper, the positioning accuracy reliability of industrial robots is analyzed considering the influence of uncertain variables. First, the kinematic models of industrial robots are established based on the Denavit–Hartenberg method, in which the link lengths and joint rotation angles are treated as uncertain variables. Second, the Sobol’ method is used to analyze the sensitivity of uncertain variables for the positioning accuracy of industrial robots, by which the sensitive variables are determined to perform the reliability analysis. Finally, in view of the sensitive variables, the first-four order moments and probability density function of the manipulator's positioning point are assessed by the point estimation method (PEM) in three examples. The Monte Carlo simulation method, the maximum entropy problem with fractional order moments (maximum entropy problem with fractional order moments method (ME-FM) method), and the experimental method are also performed as comparative methods. All the results demonstrate that the proposed PEM has a higher accuracy and efficiency to assess the positioning accuracy reliability of industrial robots.