Safety life analysis under the required failure possibility constraint for structure involving fuzzy uncertainty

For ensuring safety service of structure under fuzzy uncertainty, some efficient methods are proposed for analyzing safety life under the constraint that the actual time-dependent failure possibility (TDFP) less than the target failure possibility. The direct dichotomy method is firstly established to solve the safety life. Since the direct dichotomy method needs to iterate the TDFP at all searching points of the safety life and results in large computational cost, the equivalent constraint method (ECM) is established to solve the safety life. In ECM, the equivalence between the constraint of the actual TDFP and the equivalent constraint of the lower boundary of the minimum of the response function is strictly proved by the reduction to absurdity. By equivalently replacing the constraint of the actual TDFP with that of the lower boundary of the minimum of the output response, the computational cost for estimating the safety life is greatly reduced. Two solutions of the safety life based on the ECM are established. One is ECM based dichotomy method. The other is ECM based Newton method, where a simplified derivative solution is deduced to reduce the computational cost. After the implementations of solving the safety life are given in detail, several examples are used to verify the rationality of the established safety life analysis model and the efficiency of the methods for solving safety life.     

AK-SYSi: an improved adaptive Kriging model for system reliability analysis with multiple failure modes by a refined U learning function

Due to multiple implicit limit state functions needed to be surrogated, adaptive Kriging model for system reliability analysis with multiple failure modes meets a big challenge in accuracy and efficiency. In order to improve the accuracy of adaptive Kriging meta-model in system reliability analysis, this paper mainly proposes an improved AK-SYS by using a refined U learning function. The improved AK-SYS updates the Kriging meta-model from the most easily identifiable failure mode among the multiple failure modes, and this strategy can avoid identifying the minimum mode or the maximum mode by the initial and the in-process Kriging meta-models and eliminate the corresponding inaccuracy propagating to the final result. By analyzing three case studies, the effectiveness and the accuracy of the proposed refined U learning function are verified.

     

Adaptive Consensus Quantized Control for a Class of High-Order Nonlinear Multi-Agent Systems With Input Hysteresis and Full State Constraints

For a class of high-order nonlinear multi-agent systems with input hysteresis, an adaptive consensus output-feedback quantized control scheme with full state constraints is investigated. The major properties of the proposed control scheme are: 1) According to the different hysteresis input characteristics of each agent in the multi-agent system, a hysteresis quantization inverse compensator is designed to eliminate the influence of hysteresis characteristics on the system while ensuring that the quantized signal maintains the desired value. 2) A barrier Lyapunov function is introduced for the first time in the hysteretic multi-agent system. By constructing state constraint control strategy for the hysteretic multi-agent system, it ensures that all the states of the system are always maintained within a predetermined range. 3) The designed adaptive consensus output-feedback quantization control scheme allows the hysteretic system to have unknown parameters and unknown disturbance, and ensures that the input signal transmitted between agents is the quantization value, and the introduced quantizer is implemented under the condition that only its sector bound property is required. The stability analysis has proved that all signals of the closed-loop are semi-globally uniformly bounded. The StarSim hardware-in-the-loop simulation certificates the effectiveness of the proposed adaptive quantized control scheme.      

一类非线性系统的神经网络自适应区间观测器设计

本文研究了一类单输入单输出非线性系统的神经网络自适应区间观测器设计问题. 针对由状态和输入所描述的未知非线性函数的界不可测, 现有的区间观测器方法并未有效地处理系统含有参数不确定性的未知非线性函数. 首先, 本文构造两个径向基函数神经网络来逼近未知非线性部分, 进而分别估计系统状态的上下界; 然后, 选择合适的Lyapunov函数, 采用网络权值校正和网络误差选择机制确保所设计的误差动态系统有界和非负性, 并证明了神经网络自适应区间观测器的稳定性; 最后, 通过仿真实例验证了所提出的神经网络自适应区间观测器的有效性.     

Intervening variables and constraint approximations in safety index and failure probability calculations

The emphasis of this paper is on developing suitable intervening variables and constraint approximations for structural reliability analysis. Traditionally, these procedures are used in structural optimization, whereas this research work adopts these concepts to safety index and failure probability computations. The use of these concepts enables the development of an efficient and stable iteration algorithm for identifying the most probable failure points (MPPs) of the limit state functions. An approximate second-order failure probability is calculated at this MPP with no extra computations of the limit state function and gradients. The efficiency and accuracy of the proposed algorithm are demonstrated by several examples with highly nonlinear, complex, explicit/implicit performance functions.     

Design of adaptive block backstepping controllers for uncertain nonlinear dynamic systems with n blocks

Based on the Lyapunov stability theorem, a design methodology of adaptive block backstepping control is proposed in this article for a class of multi-input systems with matched and mismatched perturbations to solve regulation problems. The systems to be controlled contain n blocks' dynamic equations, hence n???1 virtual input controllers are firstly designed so that the state variables of first n???1 blocks are asymptotically stable if each virtual control input is equal to the state variable of next block. Then the control input is designed in the last nth block to ensure asymptotic stability for the whole state variables even if the perturbations exist. In addition, adaptive mechanisms are embedded in each virtual input function and control input, so that the upper bound of perturbations is not required to be known beforehand. Finally, a numerical example is given for demonstrating the feasibility of the proposed control scheme.     

基于双尺度约束模型的BN结构自适应学习算法

在无先验信息的情况下, 贝叶斯网络(Bayesian network, BN)结构搜索空间的规模随节点数目增加呈指数级增长, 造成BN结构学习难度急剧增加. 针对该问题, 提出基于双尺度约束模型的BN结构自适应学习算法. 该算法利用最大互信息和条件独立性测试构建大尺度约束模型, 完成BN结构搜索空间的初始化. 在此基础上设计改进遗传算法, 在结构迭代优化过程中引入小尺度约束模型, 实现结构搜索空间小尺度动态缩放. 同时, 在改进遗传算法中构建变异概率自适应调节函数, 以降低结构学习过程陷入局部最优解的概率. 仿真结果表明, 提出的基于双尺度约束模型的BN结构自适应学习算法能够在无先验信息的情况下保证BN结构学习的精度和迭代寻优的收敛速度.     

A surrogate model based nested optimization framework for inverse problem considering interval uncertainty

The inverse problem is a kind of engineering problem that estimates the input through the given output. In this paper, the given output described as interval uncertainty parameter is concerned, and the interval is formed by the interval midpoint and the interval radius. The two-step framework that estimates the midpoint and the radius separately is used. A novel nested optimization framework is proposed to estimate the input interval radius with more inputs than outputs. The nested framework has two loops: (i) the inner loop quantifies the lower and upper bounds of the output with given interval radiuses of inputs from the outer loop by two optimizations, and the results will be feedback to the outer loop as constraint values of outer loop; (ii) the outer loop maximizes the input interval radiuses to reduce the cost while meeting the constraints transformed from the given interval. The nested framework induces a high number of forward model computations in the loops and may lead to an unacceptable computational burden for most engineering applications. Therefore, the surrogate model is suggested. The Radial Basis Functions (RBF) surrogate model is used to relieve the computational burden. The effectiveness and the accuracy of the framework are verified through a mathematical example, a cantilever tube example and an airfoil example.     

Adaptive fixed‐time fault‐tolerant control for rigid spacecraft using a double power reaching law

In this paper, an adaptive fixed‐time fault‐tolerant control scheme is presented for rigid spacecraft with inertia uncertainties and external disturbances. By using an inverse trigonometric function, a novel double power reaching law is constructed to speed up the state stabilization and reduce the chattering phenomenon simultaneously. Then, an adaptive fixed‐time fault‐tolerant controller is developed for the spacecraft with the actuator faults, such that the fixed‐time convergence of the attitude and angular velocity could be guaranteed, and no prior knowledge on the upper bound of the lumped uncertainties is required anymore in the controller design. Comparative simulations are provided to illustrate the effectiveness and superior performance of the proposed scheme.     

Finite‐time stabilization of stochastic nonlinear systems with SiISS inverse dynamics

This paper investigates the finite‐time control problem for a class of stochastic nonlinear systems with stochastic integral input‐to‐state stablility (SiISS) inverse dynamics. Motivated by finite‐time stochastic input‐to‐state stability and the concept of SiISS using Lyapunov functions, a novel finite‐time SiISS using Lyapunov functions is introduced firstly. Then, by adopting this novel finite‐time SiISS small‐gain arguments, using the backstepping technique and stochastic finite‐time stability theory, a systematic design and analysis algorithm is proposed. Given the control laws that guarantee global stability in probability or asymptotic stability in probability, our design algorithm presents a state‐feedback controller that can ensure the solution of the closed‐loop system to be finite‐time stable in probability. Finally, a simulation example is given to demonstrate the effectiveness of the proposed control scheme. Copyright © 2017 John Wiley & Sons, Ltd.     

An efficient reliability analysis method combining adaptive Kriging and modified importance sampling for small failure probability

A vital challenge problem of structural reliability analysis is how to estimate the small failure probability with a minimum number of model evaluations. The Adaptive Kriging combined with Importance Sampling method (AK-IS) which is developed from the adaptive Kriging combined with Monte Carlo simulation (AK-MCS) is a viable method to deal with this challenge. The aim of this paper is to reduce the number of model evaluations of the existing AK-IS algorithm. Firstly, we use a contributive weight function to divide the candidate samples of model input variables generated in AK-IS. The candidate samples are used to select the best next sample to update the Kriging model in AK-IS. Secondly, select the best next sample only in the important area obtained according to the contributive weight value to failure probability to update the Kriging model until the stopping condition is satisfied. Thirdly, use the Kriging model constructed in the important area to predict the other area and update the important area by adding the point with the maximum contributive weight value in the area except the important area ceaselessly until the probability of the accurate identification on the limit state function’s signs (positive limit state value or negative limit state value) of all the importance sampling points satisfies a criterion. Finally, the updated Kriging model is used to estimate the failure probability especially for the small failure probability. The proposed method uses a thought from local to global in order to reduce the computational cost of AK-IS and simultaneously guarantees the accuracy of estimation. A non-linear oscillator system, a roof truss structure and a planar ten-bar structure are analyzed by the proposed method. The results demonstrate the efficiency and accuracy of the proposed method in structural reliability analysis especially for small failure probability.     

Finite‐time stabilization of stochastic low‐order nonlinear systems with FT‐SISS inverse dynamics

This paper studies finite‐time stabilization problem for stochastic low‐order nonlinear systems with stochastic inverse dynamics. By characterizing unmeasured stochastic inverse dynamics with finite‐time stochastic input‐to‐state stability, combining the Lyapunov function and adding a power integrator technique, and using the stochastic finite‐time stability theory, a state feedback controller is designed to guarantee global finite‐time stability in probability of stochastic low‐order nonlinear systems with finite‐time stochastic input‐to‐state stability inverse dynamics.     

A note on guaranteed cost control for nonlinear stochastic systems with input saturation and mixed time‐delays

ThisIn the system under investigation, the control input is subject to the saturation constraint, and both discrete and distributed time‐delays are taken into consideration. The objective of the addressed problem is to design a state feedback controller such that the resulting closed‐loop system is asymptotically stable in probability and an upper bound is guaranteed on a prespecified quadratic cost function. Sufficient conditions are established for the existence of the desired guaranteed cost control strategy in terms of the solvability of certain Hamilton–Jacobi inequalities. Simulation results demonstrate the correctness and applicability of the proposed control scheme. Copyright © 2017 John Wiley & Sons, Ltd.     

Adaptive state feedback control for switched stochastic high‐order nonlinear systems under arbitrary switchings

This paper studies the adaptive state feedback control for a class of switched time‐varying stochastic high‐order nonlinear systems under arbitrary switchings. Based on the common Lyapunov function and using the inductive method, virtual controllers are designed step by step and the form of the input signal of the system is constructed at the last. The unknown parameters are addressed by the tuning function method. In particular, both the designed state feedback controller and the adaptive law are independent of switching signals. Based on the designed controller, the boundness of the state variables can be guaranteed in probability. Furthermore, without considering the Wiener process or with the known parameter in the assumption, adaptive finite‐time stabilization and finite‐time stabilization in probability can be obtained, respectively. Finally, numerical simulation results are presented to illustrate the effectiveness of the proposed method.     

状态与输入受限的二阶多输入多输出非线性系统自适应控制

针对含有状态和输入受限的二阶多输入多输出非线性系统的控制问题,提出了一种自适应控制策略.通过综合利用障碍Lyapunov函数和动态面控制方法的特性,使得系统的状态满足约束条件而且能够减少计算量.此外,为了处理输入约束和系统中的不确定性的影响,分别设计了辅助系统和自适应算法.通过理论分析表明,闭环系统的所有状态都是有界的,而且系统的状态和输入都满足约束条件.最后,通过一个数值仿真算例和一个实际的航天器姿态控制系统的仿真来验证所提出的自适应控制策略的有效性.     

输出约束的有限时间自适应区间二型模糊输出反馈PMSM伺服控制

针对永磁同步电机驱动的伺服系统在不确定性摩擦和未知负载的影响下难以达到高精度的控制效果,提出一种基于区间二型模糊系统的带有输出约束的有限时间自适应输出反馈控制方案.首先,构建一个基于非线性扰动观测器的区间二型模糊状态观测器,分别完成对于未知扰动和速度的估计,区间二型模糊系统完成对于非线性摩擦的逼近;然后,在此基础上,结合滤波误差补偿机制和有限时间技术,引入障碍Lyapunov函数和反步控制技术设计输出约束的自适应区间二型模糊输出反馈控制器;最后,根据Lyapunov稳定性理论提出严格的稳定性分析,保证闭环系统的所有信号均是有限时间内有界的,并通过数值仿真和实验验证了所提出方法的有效性.     

考虑输入受限的航天器安全接近姿轨耦合控制

针对存在外部扰动和输入受限的航天器安全接近的问题,当扰动上界未知时,基于积分滑模控制理论设计了抗饱和的有限时间自适应姿轨耦合控制器.控制器的设计过程中采用了新型的避碰函数限制追踪航天器运动区域进而保证接近过程中航天器的安全性,同时通过辅助系统和自适应算法分别处理了输入受限和扰动上界未知.借助李雅普诺夫理论证明了在控制器的作用下系统状态在有限时间内收敛,且能够保证追踪航天器在实现航天器接近的过程中不与目标航天器发生碰撞.最后通过数字仿真进一步验证了所设计控制器的有效性.     

A robust adaptive sliding-mode control for rigid robotic manipulators with arbitrary bounded input disturbances

In this paper, a robust adaptive sliding-mode control scheme for rigid robotic manipulators with arbitrary bounded input disturbances is proposed. It is shown that the prior knowledge on the upper bound of the norm of the input disturbance vector is not required in the sliding-mode controller design. An adaptive mechanism is introduced to estimate the upper bound of the norm of the input disturbance vector. The estimate is then used as a controller gain parameter to guarantee that the output tracking error asymptotically converges to zero and strong robustness with respect to bounded input disturbances can be obtained. A simulation example is given in support of the proposed control scheme.     

复杂负载下数据缓冲区自适应调度方法仿真

传统数据缓冲区调度方法调度时间长、调度结果误差大且不能够完全应对复杂负载问题。因此提出了复杂负载下数据缓冲区自适应调度方法,通过构建模拟数据缓冲区来定义调整的方向,在缓冲数据中,利用操控行为和代替方法之间进行相互不变性推测,获取数据缓冲错失函数;通过引用能力制约条件,将时间分成一些零碎的小片段,利用数据缓冲错失,获取时间约束模型;引入时间约束模型,需要依据时间顺序对事件进行调顺序,结合根据模拟自适应算法所得到的数据,使用雷达资源约束条件能够精准快速地衡量各种数据波束所要求的指令,获取自适应调度模型,为某一个调度间隔选取出最完善的自适应调度方法。通过仿真结果表明:上述方法能够完全应对复杂负载情况的问题,且数据缓冲区自适应调度时间短、调度结果误差小。     

具有未建模动态和输出约束的耦合系统的分散自适应控制

针对一类具有输入、状态未建模动态和非线性输入的耦合系统,提出一种自适应神经网络控制方案.利用径向基函数神经网络逼近未知非线性连续函数;引入动态信号和正则化信号处理状态及输入未建模动态;通过引入非线性映射,将具有时变输出约束的严格反馈系统化为不含约束的严格反馈系统.最后,通过理论分析验证闭环系统中所有信号是半全局一致最终有界的,仿真结果进一步验证了所提出控制方案的有效性.