Experimental analysis of high-temperature creep in FV566 steel based on digital image correlation.

Digital image correlation (DIC) is an optical measurement method of material strain/displacement based on visible light illumination, which can be used for the measurement of long-term mechanical behavior. In this paper, an experimental method for analyzing high-temperature creep in FV566 steel material based on DIC was independently designed. Aiming at the problems of glass observation window medium refraction and thermal airflow disturbance in high-temperature testing, the corresponding correction methods were proposed to improve the measurement accuracy. Based on the above methods, high-temperature creep tests were carried out on three specimens with different shapes, and the strain concentration area at 600°C was calculated. Then, the influences of shape and other properties on material creep failure, stress distribution, and actual strain were investigated. Finally, the DIC calculation results were analyzed and compared with results of finite element analysis and the final fracture position of the specimen. The three results had a high degree of consistency, which verified that the proposed method can accurately measure and analyze the creep behavior of materials.

Binocular DIC system for 3D correlation measurements.

A novel, to the best of our knowledge, mirror-assisted binocular stereo digital image correlation (DIC) system is proposed for the reconstruction of the overall contour, thickness, and strain measurement of the object. First, the angle between the two plane mirrors is adjusted until two virtual images and two real images can be formed in the mirrors. Then, the adjustable speckle size and definition characteristics of the projection speckle technology are fully utilized to realize the precise measurement of the mirror plane. Finally, a 3D contour reconstruction experiment and a dynamic stretching experiment are conducted to verify the proposed method. Experimental results show that the proposed method can achieve a 360° omnidirectional deformation measurement, and the 3D reconstruction of the object with complex contours has a relatively ideal reconstruction effect. According to the virtual image, the thickness of the conventional specimen can be completed easily, and the coordinates of the front and rear surfaces need not be subtracted. The dynamic strain can be calculated separately from the front and rear surfaces of the standard specimen and can be realized in the dynamic tensile experiment. Compared with the existing binocular DIC system, the proposed method can provide more valid data with guaranteed excellent results. It provides a better implementation method for omnidirectional measurement, thickness, and stress-strain calculation of the object.

Method to eliminate thermal disturbance errors in digital image correlation measurement based on projection speckle.

Digital image correlation (DIC) technology is an optical measurement method of material strain displacement based on visible light illumination. With the increasing application of DIC technology in the field of high-temperature measurement, however, the effect of thermal disturbance on measurement accuracy becomes more and more serious. To solve this problem, a method to eliminate thermal disturbance in material measurements based on projection speckle is proposed. First, the gray surface information of two different colors is assigned to the surface of the test piece by artificial splashing and projector projection. The pictures are collected using a color camera, and the channels are separated for each frame of the picture. After that, the displacement field recorded by different channels can be obtained by DIC calculation so the thermal disturbance error can be separated from the real displacement and eliminated. Subsequently, an experimental system is built. Finally, the corrected results are compared with the true displacement value of the stage. The results show that the two sets of values are highly consistent, which verifies the feasibility and accuracy of the proposed method.

The effect of shame on anger at others: awareness of the emotion-causing events matters.

Numerous studies have found that shame increases individuals' anger at others. However, according to recent theories about the social function of shame and anger at others, it is possible that shame controls individuals' anger at others in specific conditions. We replicated previous findings that shame increased individuals' anger at others' unfairness, when others were not aware of the individual's experience of shameful events. We also found for the first time that shame controlled or even decreased individuals' anger at others' unfairness, when others were aware of the individual's experience of shameful events. The results were consistent when shame was induced by either a recall paradigm or an imagination paradigm, and in either the ultimatum game or the dictator game. This suggests that shame strategically controls individuals' anger at others to demonstrate that they are willing to benefit others, when facing the risk of social exclusion. Our findings highlight the interpersonal function of shame and deepen the understanding of the relationship between shame and anger at others.

Full-circle range and microradian resolution angle measurement using the orthogonal mirror self-mixing interferometry.

The self-mixing technique based on the traditional reflecting mirror has been demonstrated with great merit for angle sensing applications. In order to solve the problems of the narrow measurement angle range and low resolution in traditional angle measurement method, we proposed an angle measurement system using orthogonal mirror self-mixing interferometry combine an orthogonal mirror with designed mechanical linkage. It overcomes the shortcomings of traditional angle measurement methods and realized the angle measurement with microradian resolution in a full-circle range of 0 rad to 2π rad. In the experiment, the measurement resolution can reach to 5.27 µrad and the absolute error can lower to ± 0.011µrad, which satisfies the requirements of most high accuracy angle measurement.

Fixed-time command-filtered composite adaptive neural fault-tolerant control for strict-feedback nonlinear systems.

The research investigates the fixed-time command-filtered composite adaptive neural fault-tolerant (FCCANF) control issue of strict-feedback nonlinear systems (SFNSs). There exist unknown functions and bounded disturbances in the considered systems. Radial basis function neural networks (RBFNNs) will be used in the estimate of the unknown functions. By the serial-parallel estimation models (SPEMs), the forecast biases and the track biases can change the weights of RBFNNs and the approximate characteristics of RBFNNs will be improved. Then, utilizing the novel fixed-time command filter and adaptive disturbance observers, the issue of complex explosion will be effectively solved and the external disturbance is effectively compensated. Subsequently, by utilizing the adaptive control technique, a novel FCCANF controller is developed. Additionally, we have that the system internal variables are bounded and the output variable inclines to a little interval around zero in fixed time which is not determined by the system initial variables. Eventually, numerical and practical examples are shown to prove the availability of the obtained control technique.

Exponential synchronization of delayed coupled neural networks with delay-compensatory impulsive control.

This paper studies the exponential synchronization problem for a class of delayed coupled neural networks with delay-compensatory impulsive control. A Razumikhin-type inequality involving some destabilizing delayed impulse gains and a new idea of delay-compensatory that shows two critical roles for system stability are presented, respectively. Based on the constructed inequality and the presented delay-compensatory idea, sufficient stability and synchronization criteria for globally exponential synchronization (GES) of coupled neural networks (CNNs) are presented. Compared with existing results, the uniqueness of the presented results lies in that impulse delays can be fetched and integrated to compensate for instantaneous unstable impulse dynamics caused by destabilizing gains. Moreover, constraints between system delay and impulsive delay are relaxed, and the interval of impulses no longer constrains the system delay. Comparisons and a practical application are given to demonstrate the superior performance of the presented novel control methods.

Finite-time prescribed performance tracking control for nonlinear time-delay systems with state constraints and actuator hysteresis.

In this paper, the problem of adaptive neural network prescribed performance tracking control for a class of non-strict feedback time-delay systems constrained by full-state is studied. Radial basis function (RBF) neural networks (NNs) are integrated into the backstepping medium to deal with the uncertain functions and the barrier Lyapunov function (BLF) technique ensures that the state of the system does not exceed its limits. Subsequently, integrated with the Lyapunov-Krasovskii functional, the proposed control scheme makes the tracking errors converge to the preset region while the state constraint is not violated. Finally, the effectiveness of the scheme is supported by two simulation experiments.

Adaptive Fixed-Time Control for High-Order Stochastic Nonlinear Time-Delay Systems: An Improved Lyapunov-Krasovskii Function.

In this article, the adaptive tracking control problem is considered for high-order stochastic nonlinear time-delay systems in fixed-time. Being different from existing results, an improved Lyapunov-Krasovskii function is designed, which can not only compensate for the time-delay term but also remove the obstacle from the high-order term. Due to the introduction of the Lyapunov-Krasovskii function into the total Lyapunov function, it makes it difficult to stabilize the controlled system within a fixed-time interval. L'Hopital's rule is used to determine the boundedness of the Lyapunov-Krasovskii function, and the fixed-time boundedness of the integral functions can be inferred. By utilizing the fixed-time Lyapunov stability theorem, it is proved that the controlled system is semi-globally practical fixed-time stable (SGPFS), all the closed-loop signals (CLSs) are bounded within the fixed-time interval, and the tracking error converges into a small region around zero. The validity of the designed scheme is substantiated via simulation results.

Enantioselective determination of the insecticide indoxacarb in cucumber and tomato by chiral liquid chromatography-tandem mass spectrometry.

A convenient and precise chiral method was developed and validated for measuring indoxacarb enantiomers in cucumber and tomato using liquid chromatography-tandem mass spectrometry (LC-MS/MS) with a reversed-phase Chiralpak AD-RH column. The target analytes were extracted by acetonitrile and then purified by solid phase extraction (SPE) using NH2 /Carb combined-cartridge. Parameters including the matrix effect, linearity, precision, accuracy, and stability were used. Then the proposed method was successfully applied to investigate the possible enantioselective degradation of rac-indoxacarb in cucumber and tomato under open conditions. The results indicated that the degradation of indoxacarb enantiomers followed first-order kinetics in cucumber and tomato. The half-lives of (+)-S-indoxacarb in cucumber and tomato were 3.0 and 5.9 days, respectively; while the (-)-R-indoxacarb were 7.3 and 12.2 days, respectively. The data of the half-lives showed that (+)-S-indoxacarb was preferentially degraded in cucumber and tomato. Moreover, indoxacarb degraded faster in cucumber than in tomato.

Adaptive composite dynamic surface neural control for nonlinear fractional-order systems subject to delayed input.

In the article, the adaptive composite dynamic surface neural controller design problem for nonlinear fractional-order systems (NFOSs) subject to delayed input is discussed. A fractional-order auxiliary system is first designed to solve the input-delay problem. By using the developed novel estimation models, the defined prediction errors and the states of error system can decide the weights of radial basis function neural networks (RBFNNs). During the dynamic surface controller design process, the developed fractional-order filters are designed to handle the complexity explosion problem when the classical backstepping control technique is utilized. It is shown that the designed adaptive composite neural controller ensures that all the system state variables are bounded and the tracking error of the considered system finally tends to a small neighborhood of zero. Finally, the results of the simulation explain the feasibility of the developed controller. In addition, the developed controller can also be applied to single input and single output(SISO) nonlinear systems subject to a unitary input function.

Command filter-based adaptive fuzzy decentralized control for stochastic nonlinear large-scale systems with saturation constraints and unknown disturbance.

In this article, the problem of decentralized fuzzy adaptive control is addressed for a class of stochastic interconnected nonlinear large-scale systems including saturation and unknown disturbance. Fuzzy logic systems (FLSs) are used to estimate packaged nonlinear uncertainties. The command filter technique is presented to eliminate the "explosion of complexity" obstacle associated with the backstepping procedures and the corresponding error compensation mechanism is constructed to alleviate the effect of the errors generated by command filters. The influence of input saturation is compensated by introducing an auxiliary system. Meanwhile, an improved adaptive fuzzy decentralized controller is developed and it is able to minimize calculation time since there is no need for repeated differentiation for the virtual control laws. The presented control scheme not only assures the semi-global boundedness of all the signals in the closed-loop system, but also makes the output tracking errors reach a small neighborhood around the origin. Finally, both numerical and practical examples are provided to illustrate the efficiency and effectiveness of our theoretic result.

Fast Finite-Time Control for Nonaffine Stochastic Nonlinear Systems Against Multiple Actuator Constraints via Output Feedback.

This research addresses the finite-time control problem for nonaffine stochastic nonlinear systems with actuator faults and input saturation. Specifically, a new finite-time control scheme is constructed based on the adaptive backstepping framework, with the usage of a state observer and taking advantage of the universal approximation capability of the fuzzy-logic system (FLS). The novelty of this work is that it considers the output feedback problem of a completely nonaffine stochastic system and incorporates the idea of the dynamic surface control (DSC) design. By using the Lyapunov stability theory, all the signals of the controlled system can be semiglobal finite-time stable in probability (SGFSP) while the system is imposed with multiple actuator constraints. In the meantime, the problem of "complexity explosion" is avoided. Two simulation examples are given to demonstrate the validity of the presented strategy.

Event-triggered fault-tolerant control for input-constrained nonlinear systems with mismatched disturbances via adaptive dynamic programming.

In this paper, the issue of event-triggered optimal fault-tolerant control is investigated for input-constrained nonlinear systems with mismatched disturbances. To eliminate the effect of abrupt faults and ensure the optimal performance of general nonlinear dynamics, an adaptive dynamic programming (ADP) algorithm is employed to develop a sliding mode fault-tolerant control strategy. When the system trajectories converge to the sliding-mode surface, the equivalent sliding mode dynamics is transformed into a reformulated auxiliary system with a modified cost function. Then, a single critic neural network (NN) is adopted to solve the modified Hamilton-Jacobi-Bellman (HJB) equation. In order to overcome the difficulty that arises from the persistence of excitation (PE) condition, the experience replay technique is utilized to update the critic weights. In this study, a novel control method is proposed, which can effectively eliminate the effects of abrupt faults while achieving optimal control with the minimum cost under a single network architecture. Furthermore, the closed-loop nonlinear system is proved to be uniformly ultimate boundedness based on Lyapunov stability theory. Finally, three examples are presented to verify the validity of the control strategy.

Adaptive Predefined-Time Bipartite Consensus Tracking Control of Constrained Nonlinear MASs: An Improved Nonlinear Mapping Function Method.

This work focuses on the problem of predefined-time bipartite consensus tracking control for a class of nonlinear MASs with asymmetric full-state constraints. A predefined-time bipartite consensus tracking framework is developed, where both cooperative communication and adversarial communication among neighbor agents are implemented. Different from the finite-time and the fixed-time controller design methods for MASs, the prominent advantage of the controller design algorithm presented in this work is that our algorithm can make the followers track either the output or the opposite output of the leader within the predefined time in accordance to the user requirements. In order to obtain the desired control performance, an improved time-varying nonlinear transformed function is skillfully introduced for the first time to handle the asymmetric full-state constraints and radial basis function neural networks (RBF NNs) are employed to deal with the unknown nonlinear functions. Then, the predefined-time adaptive neural virtual control laws are constructed by using the backstepping technique, while their derivatives are estimated by the first-order sliding-mode differentiators. It is theoretically testified that the proposed control algorithm not only guarantees the bipartite consensus tracking performance of the constrained nonlinear MASs in the predefined time but also remains the boundedness of all the resulting closed-loop signals. Finally, the simulation research on a practical example shows the validity of the presented control algorithm.

Predefined-Time Adaptive Neural Tracking Control of Switched Nonlinear Systems.

This article investigates the neural-network-based adaptive predefined-time tracking control problem for switched nonlinear systems. Neural networks are employed to approximate the unknown part of nonlinear functions. The finite-time differentiators are introduced to estimate the first derivative of the virtual controllers. Then, a novel adaptive predefined-time controller is proposed by utilizing the backstepping control technique and the common Lyapunov function (CLF) method. It is explained by the theoretical analysis that the developed controller guarantees that all signals of the switched closed-loop systems are bounded under arbitrary switchings and the tracking error converges to zero within the predefined time. A simulation is shown to verify the validity of the developed predefined-time control approach.

Global Predefined-Time Adaptive Neural Network Control for Disturbed Pure-Feedback Nonlinear Systems With Zero Tracking Error.

This article presents a global adaptive neural-network-based control algorithm for disturbed pure-feedback nonlinear systems to achieve zero tracking error in a predefined time. Different from the traditional works that only solve the semiglobal bounded tracking problem for pure-feedback systems, this work not only achieves that the tracking error globally converges to zero but also guarantees that the convergence time can be predefined according to the user specification. In order to get the desired predefined-time controller, first, a mild semibound assumption for nonaffine functions is skillfully proposed so that the design difficulty caused by the structure of pure feedback can be easily solved. Then, we apply the property of radial basis function (RBF) neural networks (NNs) and Young's inequality to derive the upper bound of the term that contains the unknown nonlinear function and external disturbances, and the designed adaptive parameters decide the derived upper and robust control gain. Finally, the predefined-time virtual control inputs are presented whose derivatives are further estimated by utilizing finite-time differentiators. It is strictly proved that the proposed novel predefined-time controller can guarantee that the tracking error globally converges to zero within predefined time and a practical example is shown to verify the effectiveness and practicability of the proposed predefined-time control method.

Adaptive Neural Control of Nonlinear Nonstrict Feedback Systems With Full-State Constraints: A Novel Nonlinear Mapping Method.

In this work, a neural-networks (NNs)-based adaptive asymptotic tracking control scheme is presented for a class of uncertain nonstrict feedback nonlinear systems with time-varying full-state constraints. First, we construct a novel exponentially decaying nonlinear mapping to map the constrained system states to new system states without constraints. Instead of the traditional barrier Lyapunov function methods, the feasible conditions which require the virtual control signals satisfying the constraint requirements are removed. By employing the Nussbaum design method to eliminate the effect of unknown control gains, the general assumption about the signs of the unknown control gains is relaxed. Then, the nonstrict feedback form of the system can be pulled back to the strict feedback form through the basic properties of radial basis function NNs. Simultaneously, the intermediate control signals and the desired controller are constructed by the backstepping process and the Nussbaum design method. The designed controller can ensure that all signals in the whole closed-loop system are bounded without the violation of the constraints and hold the asymptotic tracking performance. In the end, a practical example about a brush dc motor driving a one-link robot manipulator is given to illustrate the effectiveness of the proposed design scheme.

Switching Event-Triggered Adaptive Resilient Dynamic Surface Control for Stochastic Nonlinear CPSs With Unknown Deception Attacks.

This work concentrates on the adaptive resilient dynamic surface controller design problem for uncertain nonlinear lower triangular stochastic cyber-physical systems (CPSs) subject to unknown deception attacks based on a switching threshold event-triggered mechanism. The adverse effect of deception attacks on the stochastic CPSs is that the exact system state variables become unavailable. Furthermore, it should be emphasized that the coexistence of unknown nonlinearities, stochastic perturbations, and unknown sensor and actuator attacks makes it a very difficult and challenging event to implement the control design. To get the desired controller, radial basis function (RBF) neural networks (NNs) are introduced so that the design obstacle caused from the unknown nonlinearities can be easily solved. On this basis, in order to save resources and effectively transmit, the event-triggered control scheme based on a switching threshold strategy is further considered. In the backstepping design process, the dynamic surface control (DSC) technique is presented to deal with the issue of "explosion of complexity." By skillfully designing a new coordinate transformation and the attack compensators, the problem of unknown deception attacks is successfully handled. Under our proposed control scheme, all the closed-loop signals are bounded in probability and the stabilization errors converge to an adjustable neighborhood of the origin in probability. Finally, the simulation results on the double chemical reactor show the validity of the proposed design scheme.

[Development of glipizide push-pull osmotic pump controlled release tablets by using expert system and artificial neural network].

The purpose of this study is to develop glipizide push-pull osmotic pump (PPOP) tablets by using a formulation design expert system and an artificial neural network (ANN). Firstly, the expert system for the formulation design of osmotic pump of poor water-soluble drug was employed to design the formulation of glipizide PPOP, taking the dissolution test results of Glucotrol XL as the goal. Then glipizide PPOP was prepared according to the designed formulations and the in vitro dissolution was carried out. And in vivo evaluation was carried out between the samples which were similar to Glucotrol XL and the Glucotrol XL in Beagle dogs. The range of the factors of formulation and procedure, which could influence the drug release, was optimized using artificial neural network. Finally, the design space was found. It was found that the target formulation which was similar to Glucotrol XL in dissolution test could be obtained in a short period by using the expert system. The samples which were similar to Glucotrol XL were bio-equivalent to the Glucotrol XL in Beagle dogs. The design space of the key parameter coating weight gain was 9.5%-12.0%. It could be concluded that a well controlled product of glipizide PPOP was developed since the dissolution test standard of our product was more strict than that of Glucotrol XL.