Multiple Parameter Uncertainties Research Articles

Dynamic simulation of tank stabilizer based on adaptive control

Tank stabilizer is a control system keeping the bore axis in the desired sighting angle. For the control problem of the stabilizer in the case of uncertain parameters, the adaptive control method is introduced to design the vertical tank stabilizer with the nonlinear dynamic model of vertical stabilizer given in the paper. In the design, the multiple parameter uncertainties of the hydraulic servo system are fully considered and the linear feedback control is used to improve the asymptotic stability of the system. The vertical stabilizer of tank is simulated in MATLAB/Simulink environment. In addition, a dynamic model of tank on the move is established based on multi-body system theory, vehicle terramechanics and gun launch dynamics. Then the established dynamic model is combined with the designed controller based on the RecurDyn/Control module. The numerical calculation results show that the adaptive control method can effectively control the muzzle vibration of tank on the move and it can satisfy the requirement of actual vertical stabilization accuracy. But some nonlinear factors of the gun such as the flexibility of the barrel will seriously influence the desired stabilization accuracy of the vertical tank stabilizer. A comparison experiment between adaptive control and proportional–integral–derivative control is also presented to show the effectiveness of the adaptive control method. The research provides a feasible way to improve the firing accuracy of tank firing on the move.

Impact of Human Actions on Building Energy Performance: A Case Study in the United Arab Emirates (UAE)

There is a growing interest in the literature to understand how actions taken by occupants and facility managers can affect building performance. However, user-centric building energy research: (1) remains understudied compared to design-focused research efforts; (2) overlooks combined effects or uncertainty in multiple parameters; and (3) typically does not cover particular types of buildings (e.g., educational facilities), nor buildings subject to extreme weather conditions. This paper fills an important gap in the literature by proposing a comprehensive energy modeling and analysis framework to quantify the impact of human action on building energy consumption. The framework applies various data analysis methods such as differential, fractional factorial, and Monte Carlo analysis methods, in order to capture potential combined or synergetic effects of human actions on building performance. A case study is then presented on typical educational buildings located in the extreme hot climate of Abu Dhabi, United Arab Emirates (UAE). Results indicate that uncertainty in human actions can lead up to a ±25% variation from average energy consumption levels, confirming the significant role that people have in making their built environment more efficient and sustainable.

Global Sliding Mode Control Via Linear Matrix Inequality Approach for Uncertain Chaotic Systems With Input Nonlinearities and Multiple Delays

This paper considers a global sliding mode control (GSMC) approach for the stabilization of uncertain chaotic systems with multiple delays and input nonlinearities. By designing the global sliding mode surface, the offered scheme eliminates reaching phase problem. The offered control law is formulated based on state estimation, Lyapunov–Krasovskii stability theory, and linear matrix inequality (LMI) technique which present the asymptotic stability conditions. Moreover, the proposed design approach guarantees the robustness against multiple delays, nonlinear inputs, nonlinear functions, external disturbances, and parametric uncertainties. Simulation results for the presented controller demonstrate the efficiency and feasibility of the suggested procedure.

$\mu$ Approach to Robust Stability Domains in the Space of Parametric Uncertainties for a Power System With Ideal CPL

Power electronic systems are prone to instability. The problem, generally attributed to the constant power load (CPL) behavior of their power electronic controlled loads, can become more acute when the systems are subject to parametric uncertainties. The structured singular value (SSV) based $\mu$ method has proven to be a reliable approach for assessing the stability robustness of such uncertain systems. Despite its numerous benefits, the $\mu$ method is not often applied to electrical power systems (EPS) with multiple uncertainties. This may be due to the mathematical complexity underlying the $\mu$ theory. This paper aims to make the $\mu$ approach more application-friendly by providing clearer insights into the meaning and usefulness of the robust stability measure $\mu$ for EPS with multiple parametric uncertainties. This is achieved by presenting a methodology for translating $\mu$ analysis results from the frequency domain to the more perceivable uncertain parameters domain. The method directly demonstrates dependences of system stability on uncertain system parameters. Further, it clearly identifies robust stability domains as subsets of the much wider stability domains. The work is based on a representative EPS connected to an ideal CPL. $\mu$ analysis predictions are evaluated and validated against analytical results, for example, CPL system.

Population Screening for Hereditary Haemochromatosis in Australia: Construction and Validation of a State-Transition Cost-Effectiveness Model.

Introduction HFE-associated haemochromatosis, the most common monogenic disorder amongst populations of northern European ancestry, is characterised by iron overload. Excess iron is stored in parenchymal tissues, leading to morbidity and mortality. Population screening programmes are likely to improve early diagnosis, thereby decreasing associated disease. Our aim was to develop and validate a health economics model of screening using utilities and costs from a haemochromatosis cohort.MethodsA state-transition model was developed with Markov states based on disease severity. Australian males (aged 30 years) and females (aged 45 years) of northern European ancestry were the target populations. The screening strategy was the status quo approach in Australia; the model was run over a lifetime horizon. Costs were estimated from the government perspective and reported in 2015 Australian dollars ($A); costs and quality-adjusted life-years (QALYs) were discounted at 5% annually. Model validity was assessed using goodness-of-fit analyses. Second-order Monte-Carlo simulation was used to account for uncertainty in multiple parameters.ResultsFor validity, the model reproduced mortality, life expectancy (LE) and prevalence rates in line with published data. LE for C282Y homozygote males and females were 49.9 and 40.2 years, respectively, slightly lower than population rates. Mean (95% confidence interval) QALYS were 15.7 (7.7–23.7) for males and 14.4 (6.7–22.1) for females. Mean discounted lifetime costs for C282Y homozygotes were $A22,737 (3670–85,793) for males and $A13,840 (1335–67,377) for females. Sensitivity analyses revealed discount rates and prevalence had the greatest impacts on outcomes.ConclusionWe have developed a transparent, validated health economics model of C282Y homozygote haemochromatosis. The model will be useful to decision makers to identify cost-effective screening strategies.

An optimization based sampling approach for multiple metrics uncertainty analysis using generalized likelihood uncertainty estimation

This paper investigates the use of an epsilon-dominance non-dominated sorted genetic algorithm II (ɛ-NSGAII) as a sampling approach with an aim to improving sampling efficiency for multiple metrics uncertainty analysis using Generalized Likelihood Uncertainty Estimation (GLUE). The effectiveness of ɛ-NSGAII based sampling is demonstrated compared with Latin hypercube sampling (LHS) through analyzing sampling efficiency, multiple metrics performance, parameter uncertainty and flood forecasting uncertainty with a case study of flood forecasting uncertainty evaluation based on Xinanjiang model (XAJ) for Qing River reservoir, China. Results obtained demonstrate the following advantages of the ɛ-NSGAII based sampling approach in comparison to LHS: (1) The former performs more effective and efficient than LHS, for example the simulation time required to generate 1000 behavioral parameter sets is shorter by 9 times; (2) The Pareto tradeoffs between metrics are demonstrated clearly with the solutions from ɛ-NSGAII based sampling, also their Pareto optimal values are better than those of LHS, which means better forecasting accuracy of ɛ-NSGAII parameter sets; (3) The parameter posterior distributions from ɛ-NSGAII based sampling are concentrated in the appropriate ranges rather than uniform, which accords with their physical significance, also parameter uncertainties are reduced significantly; (4) The forecasted floods are close to the observations as evaluated by three measures: the normalized total flow outside the uncertainty intervals (FOUI), average relative band-width (RB) and average deviation amplitude (D). The flood forecasting uncertainty is also reduced a lot with ɛ-NSGAII based sampling. This study provides a new sampling approach to improve multiple metrics uncertainty analysis under the framework of GLUE, and could be used to reveal the underlying mechanisms of parameter sets under multiple conflicting metrics in the uncertainty analysis process.

An improved nonlinear model for a helicopter and its self-repairing control with multiple faults via quantum information technique

In this paper, an improved nonlinear model for a 3-DOF helicopter and its self-repairing control scheme are investigated via quantum information technique. Firstly, adopting the mechanism analysis, a modified dynamic model is developed, in which the couplings among axes are considered. Such a modeling scheme is useful for applications when the accuracy of original model can not be satisfied. Then, a reconfigurable control scheme is designed for the twin rotor helicopter with multiple faults and parametric uncertainties, which combines active disturbance rejection control method with model reference adaptive control method. In addition, quantum information technique is used to increase the accuracy of self-repairing control of helicopter. Finally, simulation verification is presented in both aspects of modeling and control. The effectiveness and feasibility of the proposed scheme are verified by comparative simulations.

Exponential Stability of Stochastic Genetic Regulatory Networks with Interval Uncertainties and Multiple Delays

In this paper, the delay-dependent exponential stability analysis problem for a class of genetic regulatory networks (GRNs) with multiple time-varying delays and interval parameter uncertainties is studied. By employing a new Lyapunov–Krasovskii functional and employing some free-weighting matrices, we derive sufficient delay-dependent conditions ensuring the exponential stability of interval GRNs with multiple time-varying delays and stochastic perturbations in the form of linear matrix inequalities. Finally, three numerical examples are used to demonstrate and verify the advantages of the main results.

Human birth seasonality: latitudinal gradient and interplay with childhood disease dynamics

More than a century of ecological studies have demonstrated the importance of demography in shaping spatial and temporal variation in population dynamics. Surprisingly, the impact of seasonal recruitment on infectious disease systems has received much less attention. Here, we present data encompassing 78 years of monthly natality in the USA, and reveal pronounced seasonality in birth rates, with geographical and temporal variation in both the peak birth timing and amplitude. The timing of annual birth pulses followed a latitudinal gradient, with northern states exhibiting spring/summer peaks and southern states exhibiting autumn peaks, a pattern we also observed throughout the Northern Hemisphere. Additionally, the amplitude of United States birth seasonality was more than twofold greater in southern states versus those in the north. Next, we examined the dynamical impact of birth seasonality on childhood disease incidence, using a mechanistic model of measles. Birth seasonality was found to have the potential to alter the magnitude and periodicity of epidemics, with the effect dependent on both birth peak timing and amplitude. In a simulation study, we fitted an susceptible-exposed-infected-recovered model to simulated data, and demonstrated that ignoring birth seasonality can bias the estimation of critical epidemiological parameters. Finally, we carried out statistical inference using historical measles incidence data from New York City. Our analyses did not identify the predicted systematic biases in parameter estimates. This may be owing to the well-known frequency-locking between measles epidemics and seasonal transmission rates, or may arise from substantial uncertainty in multiple model parameters and estimation stochasticity.

Energy efficient scheme for DC motor drives in parameter uncertainties

In conventional way of cost effective and easy to implement energy efficient scheme for DC motor drives, load perturbations are only taken into account. But, in real industrial applications the moment of inertia, friction coefficient, armature resistance and armature inductance vary as the operating conditions of the plant change. These parameter uncertainties have immense adverse effects on the system in practical applications with light loads, and load disturbance conditions. The proposed H∞ control–based approach ensures energy efficient operation of DC motor drives along with simultaneous robustness in multiple parameter uncertainties. This result in a profitable robust energy efficient approach of DC motor drive operation which can be used in real time industrial applications as robust and efficient operation ensures less overhead and in turn less overall costs. The success of the approach is demonstrated through results which confirm the system is stable and efficient even in multiple parameter uncertainties and load perturbations.

A systematic review of models used in cost-effectiveness analyses of preventing osteoporotic fractures

This review was aimed at the evolution of health economic models used in evaluations of clinical approaches aimed at preventing osteoporotic fractures. Models have improved, with medical continuance becoming increasingly recognized as a contributor to health and economic outcomes, as well as advancements in epidemiological data. Model-based health economic evaluation studies are increasingly used to investigate the cost-effectiveness of osteoporotic fracture preventions and treatments. The objective of this study was to carry out a systematic review of the evolution of health economic models used in the evaluation of osteoporotic fracture preventions. Electronic searches within MEDLINE and EMBASE were carried out using a predefined search strategy. Inclusion and exclusion criteria were used to select relevant studies. References listed of included studies were searched to identify any potential study that was not captured in our electronic search. Data on country, interventions, type of fracture prevention, evaluation perspective, type of model, time horizon, fracture sites, expressed costs, types of costs included, and effectiveness measurement were extracted. Seventy-four models were described in 104 publications, of which 69% were European. Earlier models focused mainly on hip, vertebral, and wrist fracture, but later models included multiple fracture sites (humerus, pelvis, tibia, and other fractures). Modeling techniques have evolved from simple decision trees, through deterministic Markov processes to individual patient simulation models accounting for uncertainty in multiple parameters. Treatment continuance has been increasingly taken into account in the models in the last decade. Models have evolved in their complexity and emphasis, with medical continuance becoming increasingly recognized as a contributor to health and economic outcomes. This evolution may be driven in part by the desire to capture all the important differentiating characteristics of medications under scrutiny, as well as the advancement in epidemiological data relevant to osteoporosis fractures.

Robust controller design for an electromagnetic active suspension subjected to mixed uncertainties

This article focuses on the modelling and robust control of a DC motor-based electromagnetic active suspension. Due to the small damping of the motor, the moments of inertia of the rotor and the nut can significantly affect the high-frequency performance of the suspension system and so, they are taken into consideration in the system modelling. In addition, parameter variations of the vehicle system and the unmodelled high-order dynamics of the actuator could influence the control effectiveness. Thus, the mixed uncertainties, composed of both multiple parameter uncertainties and the unmodelled high-order dynamics of the suspension and actuator are taken into account in the system model. To ensure the robustness and performance of the uncertain suspension system, a robust controller based on mixed µ-synthesis is designed. An easy method of choosing the weighting functions is proposed. The simulation results show the mixed µ-synthesis controller can meet the requirement of robust performance in the presence of mixed uncertainties and can achieve good ride comfort.

A new robust stability criterion for dynamical neural networks with multiple time delays

This paper investigates the problem of the existence, uniqueness and global asymptotic stability of the equilibrium point for the class of neural networks with multiple time delays and parameter uncertainties. Under the assumption that the activation functions are globally Lipschitz continuous, we derive a new criterion for the robust stability of a class of delayed neural networks by utilizing the Lyapunov stability theorems and the Homomorphic mapping theorem. Different from those previously published conditions in the recent literature, the robust stability result presented in this paper not only establishes a time-independent relationship between the network parameters of the neural network, but also takes into account the number the neurons of the designed neural system. Some illustrative numerical examples are also given to make a detailed comparison between our result and the previously published corresponding results. This comparison proves that our result is new and can be considered an alternative condition to those of the previously reported robust stability results.

A methodology for global-sensitivity analysis of time-dependent outputs in systems biology modelling

One of the main challenges in the development of mathematical and computational models of biological systems is the precise estimation of parameter values. Understanding the effects of uncertainties in parameter values on model behaviour is crucial to the successful use of these models. Global sensitivity analysis (SA) can be used to quantify the variability in model predictions resulting from the uncertainty in multiple parameters and to shed light on the biological mechanisms driving system behaviour. We present a new methodology for global SA in systems biology which is computationally efficient and can be used to identify the key parameters and their interactions which drive the dynamic behaviour of a complex biological model. The approach combines functional principal component analysis with established global SA techniques. The methodology is applied to a model of the insulin signalling pathway, defects of which are a major cause of type 2 diabetes and a number of key features of the system are identified.

μ-Synthesis controller design for a DC-motor-based active suspension with parametric uncertainties

This paper presents a µ-synthesis controller design method for a DC-motor-based active suspension. Considering multiple parametric uncertainties, a full-car suspension model with DC-motor actuator is built up and a µ-synthesis controller is proposed for the perturbed suspension system. Both frequency and time domain simulations are conducted, and results show that the µ-synthesis controller achieves better performance in ride comfort compared with H∞ controller. Analyses of robust stability and robust performance have been done, indicating that the µ-synthesis controller is able to maintain both of them, which is also validated by a case in this paper. Consequently, it can be concluded that the designed µ-synthesis controller can deal with parametric uncertainties for a DC-motor-based active suspension.

Multi-Objective Reliability-Based Optimization with Stochastic Metamodels

This paper addresses continuous optimization problems with multiple objectives and parameter uncertainty defined by probability distributions. First, a reliability-based formulation is proposed, defining the nondeterministic Pareto set as the minimal solutions such that user-defined probabilities of nondominance and constraint satisfaction are guaranteed. The formulation can be incorporated with minor modifications in a multiobjective evolutionary algorithm (here: the nondominated sorting genetic algorithm-II). Then, in the perspective of applying the method to large-scale structural engineering problems--for which the computational effort devoted to the optimization algorithm itself is negligible in comparison with the simulation--the second part of the study is concerned with the need to reduce the number of function evaluations while avoiding modification of the simulation code. Therefore, nonintrusive stochastic metamodels are developed in two steps. First, for a given sampling of the deterministic variables, a preliminary decomposition of the random responses (objectives and constraints) is performed through polynomial chaos expansion (PCE), allowing a representation of the responses by a limited set of coefficients. Then, a metamodel is carried out by kriging interpolation of the PCE coefficients with respect to the deterministic variables. The method has been tested successfully on seven analytical test cases and on the 10-bar truss benchmark, demonstrating the potential of the proposed approach to provide reliability-based Pareto solutions at a reasonable computational cost.

New Criteria on Exponential Stability of Uncertain Delayed Systems with Multiple Delays and Parameter Uncertainty

This paper studies the exponential stability problem of uncertain delayed systems with multiple delays and parameter uncertainty. By employing the Lyapunov-based method and some matrix inequalities techniques, new sufficient conditions of the exponential stability for uncertain delayed systems are derived. Our sufficient conditions are in a form of algebraic matrix inequalities, which can be applied easily. Finally, numerical examples are presented to illustrate the effectiveness of our results.

Robust iterative learning control for linear systems with multiple time-invariant parametric uncertainties

This article presents a novel robust iterative learning control algorithm (ILC) for linear systems in the presence of multiple time-invariant parametric uncertainties.The robust design problem is formulated as a min–max problem with a quadratic performance criterion subject to constraints of the iterative control input update. Then, we propose a new methodology to find a sub-optimal solution of the min–max problem. By finding an upper bound of the worst-case performance, the min–max problem is relaxed to be a minimisation problem. Applying Lagrangian duality to this minimisation problem leads to a dual problem which can be reformulated as a convex optimisation problem over linear matrix inequalities (LMIs). An LMI-based ILC algorithm is given afterward and the convergence of the control input as well as the system error are proved. Finally, we apply the proposed ILC to a generic example and a distillation column. The numerical results reveal the effectiveness of the LMI-based algorithm.

Robust static output feedback controller synthesis using Kharitonov’s theorem and evolutionary algorithms

This paper presents a simple but effective tuning strategy for robust static output feedback (SOF) controllers with minimal quadratic cost in the context of multiple parametric uncertainties. Finding this type of controller is known to be computationally intractable using conventional techniques. This is mainly due to the non-convexity of the resulting control problem, which has a fixed structure. To solve this kind of control problem easily and directly, without using any complicated mathematical manipulations, we utilize Kharitonov’s theorem and an evolutionary algorithm (EA) for the resolution of the underlying constrained optimization problem. Using Kharitonov’s theorem, a family of bounded, robustly stable static output feedback controllers can be defined and EA is used to select the controller that ensures a minimal quadratic cost within this family. The resulting tuning strategy is applicable to both stable and unstable systems, without any limitations on the order of the process to be controlled. A numerical study was conducted to demonstrate the validity of the proposed tuning procedure.

Size of Spectroscopic Calibration Samples for Cosmic Shear Photometric Redshifts

Weak gravitational lensing surveys using photometric redshifts can have their cosmological constraints severely degraded by errors in the photo-z scale. We explore the cosmological degradation vs the size of the spectroscopic survey required to calibrate the photo-z probability distribution. Previous work has assumed a simple Gaussian distribution of photo-z errors; here we describe a method for constraining an arbitrary parametric photo-z error model. As an example we allow the photo-z probability distribution to be the sum of $N_g$ Gaussians. To limit cosmological degradation to a fixed level, photo-z models with multiple Gaussians require up to 5 times larger calibration sample than one would estimate from assuming a single-Gaussian model. This degradation saturates at $N_g\approx 4$. Assuming a single Gaussian when the photo-z distribution has multiple parameters underestimates cosmological parameter uncertainties by up to 35%. The size of required calibration sample also depends upon the shape of the fiducial distribution, even when the RMS photo-z error is held fixed. The required calibration sample size varies up to a factor of 40 among the fiducial models studied, but this is reduced to a factor of a few if the photo-z parameters are forced to be slowly varying with redshift. Finally we show that the size of the required calibration sample can be substantially reduced by optimizing its redshift distribution. We hope this study will help stimulate work on better understanding of photo-z errors.