Bayesian recursive parameter estimation for hydrologic models

The uncertainty in a given hydrologic prediction is the compound effect of the parameter, data, and structural uncertainties associated with the underlying model. In general, therefore, the confidence in a hydrologic prediction can be improved by reducing the uncertainty associated with the parameter estimates. However, the classical approach to doing this via model calibration typically requires that considerable amounts of data be collected and assimilated before the model can be used. This limitation becomes immediately apparent when hydrologic predictions must be generated for a previously ungauged watershed that has only recently been instrumented. This paper presents the framework for a Bayesian recursive estimation approach to hydrologic prediction that can be used for simultaneous parameter estimation and prediction in an operational setting. The prediction is described in terms of the probabilities associated with different output values. The uncertainty associated with the parameter estimates is updated (reduced) recursively, resulting in smaller prediction uncertainties as measurement data are successively assimilated. The effectiveness and efficiency of the method are illustrated in the context of two models: a simple unit hydrograph model and the more complex Sacramento soil moisture accounting model, using data from the Leaf River basin in Mississippi

Analyzing the Influence of Various Fuzzification Methods in the Evaluation of Netbeans Java Components’ Interface Complexity for Reusability

The prognostic nature of fuzzy has made it a versatile tool in handling uncertainty problem. One of the major components of fuzzy system that plays an important role in its successful interpretability is fuzzification. While many researches have utilized its different forms in the accomplishment of their evaluations, especially in the domain of component based software development; it remains to be seen, the application and effects of these different membership functions in the assessment of components a singular solution. The research work examined the interface complexity of two NetBeans Java Components in determining their reusability. The result of the experimentation carried using MATLAB as tool, shows that Trapezoidal returned the highest reusability value, indicating that the components are reusable, and Polynomial fuzzification method returning the lowest reusability value and giving a false alarm that the used components were not reusable. The results underline the indispensable role of fuzzification method in the evaluation of component reusability

Modeling the measurement uncertainty with Fuzzy approach

There are several types of uncertainty in a material characterization arisen from different sources of measurement errors, such as methodological, instrumental, and personal. As a reason of the uncertainty in material models, it is plausible to consider model parameters in an interval instead of a singleton. The probability theory is widely known method used for the consideration of uncertainties by means of a certain distribution function and confidence level concept. In this study, fuzzy logic is considered within a material characterization model to deal with the uncertainty coming from random measurement errors. Data points are treated using fuzzy numbers instead of single values to cover random measurement errors. In this context, an illustrative example, prepared with core strength-rebound hammer data obtained from a concrete structure, is solved and evaluated in detail. Results revealed that there is a potential for fuzzy logic to characterize the uncertainty in a material model arisen from measurement errors

Semi-Parametric of Sample Selection Model Using Fuzzy Concepts

The sample selection model has been studied in the context of semi-parametric methods. With the deficiencies of the parametric model, such as inconsistent estimators, semi-parametric estimation methods provide better alternatives. This article focuses on the context of fuzzy concepts as a hybrid to the semiparametric sample selection model. The better approach when confronted with uncertainty and ambiguity is to use the tools provided by the theory of fuzzy sets, which are appropriate for modeling vague concepts. A fuzzy membership function for solving uncertainty data of a semi-parametric sample selection model is introduced as a solution to the problem

Navigation and control based on integral-uncertainty observer for unmanned jet aircraft

A nonlinear integral-uncertainty observer is presented, which can estimate the integral of measurement output signal and the uncertainty in system, synchronously. In order to be satisfied with the existing hardware computational environments and to select the parameters more easily, a simplified linear version of the nonlinear integral-uncertainty observer is also developed. The effectiveness of the proposed observers are verified through the numerical simulations and experiments: i) through the integral-uncertainty observers, the attitude angle and the uncertainties in attitude dynamics are estimated synchronously from the measurements of angular velocity, and the estimate results by the two observers are compared; ii) a control law is designed based on the observers to drive the jet aircraft to track a reference trajectory

Predictive control approaches to fault tolerant control of wind turbines

This thesis focuses on active fault tolerant control (AFTC) of wind turbine systems. Faults in wind turbine systems can be in the form of sensor faults, actuator faults, or component faults. These faults can occur in different locations, such as the wind speed sensor, the generator system, drive train system or pitch system. In this thesis, some AFTC schemes are proposed for wind turbine faults in the above locations. Model predictive control (MPC) is used in these schemes to design the wind turbine controller such that system constraints and dual control goals of the wind turbine are considered. In order to deal with the nonlinearity in the turbine model, MPC is combined with Takagi-Sugeno (T-S) fuzzy modelling. Different fault diagnosis methods are also proposed in different AFTC schemes to isolate or estimate wind turbine faults.The main contributions of the thesis are summarized as follows:A new effective wind speed (EWS) estimation method via least-squares support vector machines (LSSVM) is proposed. Measurements from the wind turbine rotor speed sensor and the generator speed sensor are utilized by LSSVM to estimate the EWS. Following the EWS estimation, a wind speed sensor fault isolation scheme via LSSVM is proposed.A robust predictive controller is designed to consider the EWS estimation error. This predictive controller serves as the baseline controller for the wind turbine system operating in the region below rated wind speed.T-S fuzzy MPC combining MPC and T-S fuzzy modelling is proposed to design the wind turbine controller. MPC can deal with wind turbine system constraints externally. On the other hand, T-S fuzzy modelling can approximate the nonlinear wind turbine system with a linear time varying (LTV) model such that controller design can be based on this LTV model. Therefore, the advantages of MPC and T-S fuzzy modelling are both preserved in the proposed T-S fuzzy MPC.A T-S fuzzy observer, based on online eigenvalue assignment, is proposed as the sensor fault isolation scheme for the wind turbine system. In this approach, the fuzzy observer is proposed to deal with the nonlinearity in the wind turbine system and estimate system states. Furthermore, the residual signal generated from this fuzzy observer is used to isolate the faulty sensor.A sensor fault diagnosis strategy utilizing both analytical and hardware redundancies is proposed for wind turbine systems. This approach is proposed due to the fact that in the real application scenario, both analytical and hardware redundancies of wind turbines are available for designing AFTC systems.An actuator fault estimation method based on moving horizon estimation (MHE) is proposed for wind turbine systems. The estimated fault by MHE is then compensated by a T-S fuzzy predictive controller. The fault estimation unit and the T-S fuzzy predictive controller are combined to form an AFTC scheme for wind turbine actuator faults

Active fault-tolerant control of nonlinear systems with wind turbine application

The thesis concerns the theoretical development of Active Fault-Tolerant Control (AFTC) methods for nonlinear system via T-S multiple-modelling approach. The thesis adopted the estimation and compensation approach to AFTC within a tracking control framework. In this framework, the thesis considers several approaches to robust T-S fuzzy control and T-S fuzzy estimation: T-S fuzzy proportional multiple integral observer (PMIO); T-S fuzzy proportional-proportional integral observer (PPIO); T-S fuzzy virtual sensor (VS) based AFTC; T-S fuzzy Dynamic Output Feedback Control TSDOFC; T-S observer-based feedback control; Sliding Mode Control (SMC). The theoretical concepts have been applied to an offshore wind turbine (OWT) application study. The key developments that present in this thesis are:• The development of three active Fault Tolerant Tracking Control (FTTC) strategies for nonlinear systems described via T-S fuzzy inference modelling. The proposals combine the use of Linear Reference Model Fuzzy Control (LRMFC) with either the estimation and compensation concept or the control reconfiguration concept.• The development of T-S fuzzy observer-based state estimate fuzzy control strategy for nonlinear systems. The developed strategy has the capability to tolerate simultaneous actuator and sensor faults within tracking and regulating control framework. Additionally, a proposal to recover the Separation Principle has also been developed via the use of TSDOFC within the FTTC framework.• The proposals of two FTTC strategies based on the estimation and compensation concept for sustainable OWTs control. The proposals have introduced a significant attribute to the literature of sustainable OWTs control via (1) Obviating the need for Fault Detection and Diagnosis (FDD) unit, (2) Providing useful information to evaluate fault severity via the fault estimation signals.• The development of FTTC architecture for OWTs that combines the use of TSDOFC and a form of cascaded observers (cascaded analytical redundancy). This architecture is proposed in order to ensure the robustness of both the TSDOFC and the EWS estimator against the generator and rotor speed sensor faults.• A sliding mode baseline controller has been proposed within three FTTC strategies for sustainable OWTs control. The proposals utilise the inherent robustness of the SMC to tolerate some matched faults without the need for analytical redundancy. Following this, the combination of SMC and estimation and compensation framework proposed to ensure the close-loop system robustness to various faults.• Within the framework of the developed T-S fuzzy based FTTC strategies, a new perspective to reduce the T-S fuzzy control design conservatism problem has been proposed via the use of different control techniques that demand less design constraints. Moreover, within the SMC based FTTC, an investigation is given to demonstrate the SMC robustness against a wider than usual set of faults is enhanced via designing the sliding surface with minimum dimension of the feedback signals

Fuzzy approach to construction activity estimation

Past experience has shown that variations in production rate value for the same work item is attributed to a wide range of factors. The relationships between these factors and the production rates are often very complex. It is impossible to describe an exact mathematical causal relationship between the qualitative factors(QF) and production rates. Various subjective approaches have been attempted to quantify the uncertainties contained in these causal relationships. This thesis presents one such approach by adopting a fuzzy set theory in conjunction with a fuzzy rule based system that could improve the quantification of the qualitative factors in estimating construction activity durations and costs. A method to generate a Standard Activity Unit Rate(SAUR) is presented. A construction activity can be defined by combining the Design Breakdown Structure, Trade Breakdown Structure and Work Section Breakdown Structure. By establishing the data structure of an activity, it is possible to synthesis the SAUR from published estimating sources in a systematic way. After the SAUR is defined, it is then used as a standard value from which an appropriate Activity Unit Rate(AUR) can be determined. A proto-type fuzzy rule based system called 'Fuzzy Activity Unit Rate Analyser(FAURA)' was developed to formalise a systematic framework for the QF quantification process in determining the most likely activity duration/cost. The compatibility measurement method proposed by Nafarieh and Keller has been applied as an inference strategy for FAURA. A computer program was developed to implement FAURA using Turbo Prolog. FAURA was tested and analysed by using a hypothetical bricklayer's activity in conjunction with five major QF as the input variables. The results produced by FAURA iii show that it can be applied usefully to overcome many of the problems encountered in the QF quantification process. In addition, the analysis shows that a fuzzy rule base approach provides the means to model and study the variability of AUR. Although the domain problem of this research was in estimation of activity duration/cost, the principles and system presented in this study are not limited to this specific area, and can be applied to a wide range of other disciplines involving uncertainty quantification problems. Further, this research highlights how the existing subjective methods in activity duration/cost estimation can be enhanced by utilising fuzzy set theory and fuzzy logic

A Diagnostics Framework for Underground Power Cables Lifetime Estimation Under Uncertainty

Power cables are critical assets for the reliable operation of the grid. The cable lifetime is generally estimated from the conductor temperature and associated lifetime reduction. However, these tasks are intricate due to the complex physicsof-failure (PoF) degradation mechanism of the cable. This is further complicated with the different sources of uncertainty that affect the cable lifetime estimation. Generally, simplified or deterministic PoF models are adopted resulting in non-accurate decision-making under uncertainty. In contrast, the integration of uncertainties leads to a probabilistic decision-making process impacting directly on the flexibility to adopt decisions. Accordingly, this paper presents a novel cable lifetime estimation framework that connects data-driven probabilistic uncertainty models with PoF-based operation and degradation models through Bayesian state-estimation techniques. The framework estimates the cable health state and infers confidence intervals to aid decision-making under uncertainty. The proposed approach is validated with a case study with different configuration parameters and the effect of measurement errors on cable lifetime are evaluated with a sensitivity analysis. Results demonstrate that ambient temperature measurement errors influence more than load measurement errors, and the greater the cable conductor temperature the greater the influence of uncertainties on the lifetime estimate

Expressing Measurement Uncertainty in OCL/UML Datatypes

Uncertainty is an inherent property of any measure or estimation performed in any physical setting, and therefore it needs to be considered when modeling systems that manage real data. Although several modeling languages permit the representation of measurement uncertainty for describing certain system attributes, these aspects are not normally incorporated into their type systems. Thus, operating with uncertain values and propagating uncertainty are normally cumbersome processes, di cult to achieve at the model level. This paper proposes an extension of OCL and UML datatypes to incorporate data uncertainty coming from physical measurements or user estimations into the models, along with the set of operations de ned for the values of these types.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech