Finite-time adaptive fuzzy control for a class of output constrained nonlinear systems with dead-zone

The article investigates the finite-time adaptive fuzzy control for a class of nonlinear systems with output constraint and input dead-zone. First, by skillfully combining the barrier Lyapunov function, backstepping design method, and finite-time control theory, a novel adaptive state-feedback tracking controller is constructed, and the output constraint of the nonlinear system is not violated. Second, the fuzzy logic system is used to approximate unknown function in the nonlinear system. Third, the finite-time command filter is introduced to avoid the problem of “complexity explosion” caused by repeated differentiations of the virtual control signal in conventional backstepping control schemes. Meanwhile, a new saturation function is added in the compensating signal for filter error to improve control accuracy. Finally, based on Lyapunov stability analysis, all the signals of the closed-loop are proved to be semi-globally uniformly ultimately bounded, and the tracking error converges to a small neighborhood region of the origin in a finite time. A simulation example is presented to demonstrate the effectiveness for the proposed control scheme.     

Acoustic emission analysis using Bayesian model selection for damage characterization in ceramic matrix composites

Acoustic emission (AE) during tensile testing of three-dimensional woven SiC/SiC composites was analyzed by a statistical modeling method based on a Bayesian approach to quantitatively evaluate the fracture process. Gaussian mixture models and Weibull mixture models were utilized as candidate models describing the AE time-series data. After fitting AE time-series data to these models with Markov Chain Monte Carlo (MCMC) methods, the model selection was conducted by stochastic complexity. Among the candidate models, the two-component Weibull mixture model was automatically selected. It was confirmed that the component distributions in the two-component Weibull mixture model were corresponding to the evolution of matrix cracking and fiber breakage, respectively. Since the proposed AE analysis method can determine the number of component distributions without the decision of researchers and inspectors, it is expected to be useful for an understanding of the fracture process in newly developed materials and the reliability assessment in service.     

Integral barrier Lyapunov function-based adaptive fuzzy output feedback control for nonlinear delayed systems with time-varying full-state constraints

In this article, an adaptive fuzzy output feedback control method is presented for nonlinear time-delay systems with time-varying full state constraints and input saturation. To overcome the problem of time-varying constraints, the integral barrier Lyapunov functions (IBLFs) integrating with dynamic surface control (DSC) are applied for the first time to keep the state from violating constraints. The effects of unknown time delays can be removed by using designed Lyapunov-Krasovskii functions (LKFs). An auxiliary design system is introduced to solve the problem of input saturation. The unknown nonlinear functions are approximated by the fuzzy logic systems (FLS), and the unmeasured states are estimated by a designed fuzzy observer. The novel controller can guarantee that all signals remain semiglobally uniformly ultimately bounded and satisfactory tracking performance is achieved. Finally, two simulation examples illustrate the effectiveness of the presented control methods.     

基于模糊多模型的堆芯功率控制

传统的堆芯功率PID控制器是基于单一功率水平处的堆芯局部模型设计的，难以准确描述整个堆芯功率水平范围的控制。因此，本文基于5个不同功率水平下的传递函数模型，通过三角隶属度函数加权，建立堆芯模糊多模型，并依据该模型设计堆芯功率模糊PID控制。以TMI型压水堆堆芯为对象，开展不同初始功率水平下的堆芯功率跟踪、堆芯进口温度扰动的控制仿真。结果表明，基于模糊多模型设计的堆芯功率模糊PID控制器可实现对堆芯功率的良好控制。     

Some picture fuzzy Bonferroni mean operators with their application to multicriteria decision making

In this paper, we extend the Bonferroni mean (BM) operator with the picture fuzzy numbers (PFNs) to propose novel picture fuzzy aggregation operators and demonstrate their application to multicriteria decision making (MCDM). On the basis of the algebraic operational rules of PFNs and BM, we introduce some aggregation operators: the picture fuzzy Bonferroni mean, the picture fuzzy normalized weighted Bonferroni mean, and the picture fuzzy ordered weighted Bonferroni mean. Then, a new picture fuzzy MCDM method is proposed with the help of the proposed operators. Lastly, a practical application of proposed model is given to verify the developed model and related results of the proposed model is compared with the results of the existing models to indicate its applicability.     

Comparative evaluation of different statistical tools for the prediction of uniaxial compressive strength of rocks

In this study, uniaxial compressive strength (UCS), unit weight (UW), Brazilian tensile strength (BTS), Schmidt hardness (SHH), Shore hardness (SSH), point load index (Is₅₀) and P-wave velocity (V_p) properties were determined. To predict the UCS, simple regression (SRA), multiple regression (MRA), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and genetic expression programming (GEP) have been utilized. The obtained UCS values were compared with the actual UCS values with the help of various graphs. Datasets were modeled using different methods and compared with each other. In the study where the performance indice PI_at was used to determine the best performing method, MRA method is the most successful method with a small difference. It is concluded that the mean PI_at equal to 2.46 for testing dataset suggests the superiority of the MRA, while these values are 2.44, 2.33, and 2.22 for GEP, ANFIS, and ANN techniques, respectively. The results pointed out that the MRA can be used for predicting UCS of rocks with higher capacity in comparison with others. According to the performance index assessment, the weakest model among the nine model is P7, while the most successful models are P2, P9, and P8, respectively.     

Kariya (Hildegardia barteri) seed oil extraction: comparative evaluation of solvents,modeling, and optimization techniques

In this work, the effects of solid/solvent ratio (0.10–0.25?g/ml), extraction time (3–8?h), and solvent type (n-hexane, ethyl acetate, and acetone) together with their shared interactions on Kariya seed oil (KSO) yield were investigated. The oil extraction process was modeled via response surface methodology (RSM), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) while the optimization of the three input variables essential to the oil extraction process was carried out by genetic algorithm (GA) and RSM methods. The low mean relative percent deviation (MRPD) of 0.94–4.69% and high coefficient of determination (R²) > 0.98 for the models developed demonstrate that they describe the solvent extraction process with high accuracy in this order: ANFIS, ANN, and RSM. The best operating condition (solid/solvent ratio of 0.1?g/ml, extraction time of 8?h, and acetone as solvent of extraction) that gave the highest KSO yield (32.52?wt.%) was obtained using GA-ANFIS and GA-ANN. Solvent extraction efficiency evaluation showed that ethyl acetate, n-hexane, and acetone gave maximum experimental oil yields of 19.20?±?0.28, 25.11?±?0.01, and 32.33?±?0.04?wt.%, respectively. Properties of the KSO varied based on the type of solvent used. The results of this work showed that KSO could function as raw material in both food and chemical industries.     

Models for circular data from time series spectra

Circular data are those for which the natural support is the unit circle and its toroidal extensions. Numerous constructions have been proposed which can be used to generate models for such data. We propose a new, very general, one based on the normalization of the spectra of complex-valued stationary processes. As illustrations of the new construction's application, we study models for univariate circular data obtained from the spectra of autoregressive moving average models and relate them to existing models in the literature. We also propose and investigate multivariate circular models obtained from the high-order spectra of stationary stochastic processes generated using linear filtering with an autoregressive moving average response function. A new family of distributions for a Markov process on the circle is also introduced. Results for asymptotically optimal inference for dependent observations on the circle are presented which provide a new paradigm for inference with circular models. The application of one of the new families of spectra-generated models is illustrated in an analysis of wind direction data.     

Transport Map sampling with PGD model reduction for fast dynamical Bayesian data assimilation

The motivation of this work is to address real-time sequential inference of parameters with a full Bayesian formulation. First, the proper generalized decomposition (PGD) is used to reduce the computational evaluation of the posterior density in the online phase. Second, Transport Map sampling is used to build a deterministic coupling between a reference measure and the posterior measure. The determination of the transport maps involves the solution of a minimization problem. As the PGD model is quasi-analytical and under a variable separation form, the use of gradient and Hessian information speeds up the minimization algorithm. Eventually, uncertainty quantification on outputs of interest of the model can be easily performed due to the global feature of the PGD solution over all coordinate domains. Numerical examples highlight the performance of the method.