Multi-source Uncertainties Research Articles

Reliability-constrained optimal attitude-vibration control for rigid-flexible coupling satellite using interval dimension-wise analysis

This study proposes an uncertain optimal attitude–vibration control method for rigid–flexible coupling satellites with reliability constraints based on the interval dimension-wise analysis. To model the complex uncertain control system, multi–source uncertainties in rigid–flexible coupling satellites are quantified to interval parameters. Based on the nominal rigid–flexible coupling satellite dynamic model, the interval attitude–vibration model is derived and the corresponding coupled state equation is established. Considering polynomial chaos expansions in the surrogate model, an interval dimension-wise analysis (IDWA) method is proposed to predict the uncertain state response of the uncertain attitude–vibration control system, which can help solve the overestimation problem of the conventional perturbation method. An interval linear quadratic regulator (LQR) is proposed by combining the IDWA method and deterministic LQR, to accurately optimize the uncertain control input and cost function. With regard to harsh deterministic state response constraints, this study proposes a non-probabilistic time-dependent reliability method, which allows for an effective evaluation by flexibly characterizing the relationship between the uncertain state response and threshold. The reliability-constrained multi–objective optimal attitude–vibration control problem can be solved by considering the nominal cost function and uncertain state response as two optimization objectives. The effectiveness of the proposed method is verified using a rigid–flexible coupling satellite.

Digital Twin-Driven Industrialization Development of Underwater Gliders

Underwater glider (UG) is the only marine equipment in the field of ocean observation that can realize unmanned autonomous, all-weather and wide-area continuous three-dimensional observation. The rapid individualized design (RID) and full lifecycle management (FLM) make two difficulties to be solved in the industrialization development of UGs since the UG system involves interdisciplinary multiphysics and there exists multi-source uncertainties in complex ocean environment. With the application of new information technologies in industry, the above problems can be effectively solved by the digital twin (DT)-driven methodology. In this paper, an architecture of DT-driven RID and FLM for UGs is first put forward based on our previous theoretical research and technique development. Then, the proposed architecture of DT-driven methodology is researched in detail in terms of its digital modeling, design optimization, virtual verification and practical application. Finally, Petrel developed by China is introduced, and a typical preliminary application of DT-driven methodology is presented based on Petrel to verify its feasibility. The architecture proposed in this paper is also appropriate for other types of autonomous underwater vehicles.

The multi-objective reliability-based design optimisation of buffering characteristics of airbag seat in manned airdrop

Considering the influence of multi-source uncertainties on buffering characteristics of airbag seat in manned airdrop, a reliability-based design optimisation (RBDO) method based on probability and interval models is proposed for optimising buffering characteristics of airbag seat. Firstly, the numerical model of the airbag seat is established and the validity is tested by the real equipment airdrop experiment. Secondly, the uncertainties are described by the probability model and the interval model. The multi-objective reliability-based design optimisation problem based on probability and interval hybrid model is also constructed. Thirdly, an efficient decoupling strategy is developed to transform original three-layer RBDO problem to single-layer optimisation problem based on Karush-Kuhn-Tucker (KKT) necessary condition and the second-order fourth moment method. Finally, in order to further improve the efficiency, the approximate models for the airbag seat are established based on radial basis function and the approximate reliability-based design optimisation problem is constructed which is solved by multi-objective genetic algorithm (MOGA). The results show that the proposed method not only obtains the best design parameters for the airbag seat, but also upgrade the safety of astronauts.

Load-independent multi-objective sensor placement method for localization and reconstruction of external excitations under interval uncertainties

The direct measurement of external excitations under operation conditions remains challenging in many engineering applications. This study proposes a method of optimal sensor placement for the best localization and reconstruction of external excitations when considering multi-source uncertainties. The excitation inversion is achieved in the framework of minimizing the discrepancy between the forward and inversed modal loads. To accurately obtain the modal force in the time-domain excitation reconstruction algorithm, three combined indicators of the interested modal matrix are defined, which integrate measures of effectiveness, stability, orthogonality, and robustness of inverse modal transformation. The optimal sensor placement is determined by multi-objective particle swarm optimization and Pareto optimal solution. In addition, to alleviate the computational burden of loading localization, a surrogate model based on an interval kriging interpolation model and sequential sampling strategies is established for the forward modal forces. And a time-dependent interval error of modal forces inspired by the theory of second-order narrow bounds in system reliability is developed to guide the inversion process. Notably, the developed sensor selection and excitation inversion methods are independent of the type, location, and time evolution of external excitations. Eventually, two numerical examples and an experimental case are discussed to demonstrate the validity and feasibility of the developed approach.

A residential load forecasting method for multi-attribute adversarial learning considering multi-source uncertainties

The rapid development of the Internet of Things and device-level meters provides accurate energy consumption data for various household devices, and making full use of these data to achieve higher accuracy of residential load forecasting is an open problem. Moreover, the uncertainties and insufficient feature extraction of variables make it challenging for individual residential load forecasts. In this regard, this paper proposes a novel short-term residential load forecasting method. It has the following aspects: (1) Considering the multi-source uncertainties, a multi-source uncertainties divide-and-conquer mechanism is proposed, which classifies the residential load from the device level according to different sources of uncertainties and forecasts separately. (2) Considering the multiple attributes of influencing variables, a multi-attribute adversarial learning mechanism based on the conditional Wasserstein generative adversarial network with gradient penalty (cWGAN-GP) is designed. In cWGAN-GP, this paper uses a long short-term memory network to implement the generator and uses a convolutional neural network to implement the discriminator. The former is used to extract temporal features, while the latter is used to extract spatial features, and they realize the fusion of multiple attributes through adversarial training to improve prediction accuracy. Compared with existing load forecasting methods, our proposed method’s mean absolute percentage error decreased by 1.5–38.9%, mean square error decreased by 3.5–35.9%, normalized root MSE decreased by 1.7–19.9%, and mean absolute error decreased by 3.7–27.0%.

Selecting Robust Multi-Objective Optimization of a MEMS Electro-Thermal Actuator Considering Multi-Source Uncertainties

The traditional robust multi-objective optimization (RMO) is computationally expensive, making it difficult to apply in engineering. To this end, a selecting RMO (SRMO) framework that considers multi-source uncertainties and the adaptive update of the surrogate model is proposed. The SRMO framework is more efficient and has been applied to optimize a micro-electro mechanical system (MEMS) electro-thermal actuator (ETA). The application of sensitivity analysis and surrogate modeling techniques improves efficiency in terms of reducing the problem dimensionality and the computational cost of fitness values, respectively. A multi-objective particle swarm optimization algorithm is adopted to find the better-performing points where time-consuming finite element analysis (FEA) will be performed, thus ensuring the prediction accuracy of the global surrogate (GS) model around them. A greedy algorithm is employed to select design points from the candidate solutions set to train local surrogate (LS) models that perform the robust evaluation taking into account multi-source uncertainties in dimensions, material parameters, and surface topography features. In the optimization of ETA, only 2.9% and 0.5% of the particles require performing FEA to update the GS model and train LS models, respectively. The final solutions show good robustness, and the effectiveness of the SRMO framework is verified by experiments and simulations.

Gas path deterioration observation based on stochastic dynamics for reliability assessment of aeroengines

Gas path deterioration seriously reduces the reliability of aeroengine systems. Accurate observation of gas path deterioration parameters is the key to ensuring the reliable operation and safety control of aeroengines. It is a significant challenge to accurately observe the deterioration state under multi-source uncertainties. This work provides a novel assessment technique to accurately master the gas path deterioration from the perspective of stochastic dynamics for the first time, in contrast to the previous assessment schemes based on the aerothermodynamics model and filtering theory. Based on the state parameters and stochastic optimal inputs, a full-dimensional performance observation equation is created. The construction of the stochastic optimal inputs ensures the tracking of the gas path deteriorating state through the negative feedback of state parameters and the rectification of measurement data. The observation principle and algorithm are also thoroughly presented. Three common deterioration modes serve as proof of the proposed method's effectiveness. The evaluation accuracy is increased by at least 22.75% when compared to the conventional method, and the maximum improvement rate reaches 52.45%. The proposed method is free from multi-source uncertainty interference and has higher accuracy and robustness. It offers a fresh approach to master gas path deterioration and ensures safe and reliable operation.

μ-Synthesis control with reference model for aeropropulsion system test facility under dynamic coupling and uncertainty

Due to the dynamic coupling and multi-source uncertainties, it is difficult to accurately control the pressure and temperature of the Aeropropulsion System Test Facility (ASTF) in the presence of rapid command and large disturbance. This paper presents the design of μ-synthesis control to solve the problem. By incorporating the pressure ratio into the linear equation of the control valve, the modeling error of ASTF in the low frequency range is effectively reduced. Then, an uncertain model is established by considering various factors, including parameter variations, modeling error in the low frequency range, unmodeled dynamics, and changes in the working point. To address the dynamic coupling, a diagonal reference model with desired performance is incorporated into μ-synthesis. Furthermore, all weighting functions are designed according to the performance requirements. Finally, the μ-controller is obtained by using the standard μ-synthesis method. Simulation results indicate that the μ-controller decouples the pressure and temperature dynamics of ASTF. Compared with the multivariable PI controller, integral-μ controller, and double integral-μ controller, the proposed μ-controller can achieve higher transient accuracy and better disturbance rejection. Moreover, the robustness of the μ-controller is demonstrated by Monte Carlo simulations.

System-level creep-fatigue reliability evaluation by engineering damage mechanics incorporating cumulative damage-damage threshold interference

Reliability assessment for structural system is of great significance in ensuring system safety, providing a scientific guidance for life management. In this work, a creep-fatigue reliability modeling method is developed for system-level perspective on the basis of cumulative damage-damage threshold interference. Probabilistic damage accumulations to given loading cycles are associated with the creep-fatigue models considering multi-source uncertainties and the damage threshold is regarded as a random variable. Particularly, a numerical procedure is proposed for practical application in engineering by combining the Monte Carlo simulation with Gaussian process regression-based surrogate model. Furthermore, a low-pressure turbine disk under creep-fatigue loading condition is taken as an instance to evaluate its reliability considering multiple weakness sites. Results show that the proposed method is better than the independent treatment because it is capable of avoiding the overly-conservative reliability estimation. In addition, the influence of damage summation rules and damage threshold on reliability is quantitatively analyzed in terms of the developed method. The effect of random parameters in structural system on computational efficiency of the proposed numerical procedure is investigated.

Robust Adaptive Composite Learning Integrated Guidance and Control for Skid-to-Turn Interceptors Subjected to Multiple Uncertainties and Constraints

This paper investigates a novel robust adaptive dynamic surface control scheme based on the barrier Lyapunov function (BLF), online composite learning, disturbance observer, and improved saturation function. It is mainly designed for a class of skid-to-turn (STT) interceptor integrated guidance and control (IGC) design problems under multi-source uncertainties, state constraints, and input saturation. The serial-parallel estimation model used in this study estimates the system states and provides “critic” information for the neural network and disturbance observer; then, these three are combined to realize online composite learning of the multiple uncertainties of the system and improve the interception accuracy. In addition, the state and input constraints are resolved by adopting the BLF and the improved saturation function, while the design of the auxiliary system ensures stability. Finally, a series of simulation results show that the proposed IGC scheme with a direct-hit intercept strategy achieves a satisfactory effect, demonstrating the validity and robustness of the scheme.

Quantifying Multi-Source Uncertainties in GRACE-Based Estimates of Groundwater Storage Changes in Mainland China

The Gravity Recovery and Climate Experiment (GRACE) satellites have been widely used to estimate groundwater storage (GWS) changes, yet their uncertainties related to the multi-source datasets used are rarely investigated. This study focuses on quantifying the uncertainties of GRACE GWS estimates in mainland China during 2003–2015, by generating a total of 3456 solutions from the combinations of multiple GRACE products and auxiliary datasets. The Bayesian model averaging (BMA) approach is used to derive the optimal estimates of GWS changes under an uncertainty framework. Ten river basins are further identified to analyze the estimated annual GWS trends and uncertainty magnitudes. On average, our results show that the BMA-estimated annual GWS trend in mainland China is −1.93 mm/yr, whereas its uncertainty reaches 4.50 mm/yr. Albeit the estimated annual GWS trends and uncertainties vary across river basins, we found that the high uncertainties of annual GWS trends are tied to the large differences between multiple GRACE data and soil moisture products used in the GWS solutions. These findings highlight the importance of paying more attention to the existence of multi-source uncertainties when using GRACE data to estimate GWS changes.

Uncertain optimal attitude control for space power satellite based on interval Riccati equation with non-probabilistic time-dependent reliability

Considering the multi-source uncertainties and disturbances in the attitude dynamics of space power satellite (SPS), an interval dynamic model of SPS is proposed based on the non-probabilistic theory, which is controlled by a novel uncertainty-based linear–quadratic regulator (LQR) method with time-dependent reliability. Based on the interval analysis method, an interval time-varying attitude dynamic model of SPS for sun-pointing is established considering the uncertain inertia and initial states as unknown-but-bounded parameters. Feedforward and feedback controllers are designed to offset the disturbance and control attitude, respectively. An interval Riccati equation-based optimal control method is proposed to develop a novel uncertainty-based LQR method. Non-probabilistic time-dependent reliability is constituted based on the interval process and first passage theory to assess attitude states. An uncertain multi-objective optimization of attitude control with reliability constraints is proposed to realize the uncertain LQR method. Verified by a numerical example of SPS, the proposed method takes only 0.6‰∼0.8‰ time consumption of 10,000 Monte Carlo simulations while maintaining a certain accuracy.

Evaluation of the Impact of Multi-Source Uncertainties on Meteorological and Hydrological Ensemble Forecasting

Evaluating the impact of multi-source uncertainties in complex forecasting systems is essential to understanding and improving the systems Previous studies have paid little attention to the influence of multi-source uncertainties in complex meteorological and hydrological forecasting systems. In this study, we developed a general ensemble framework based on Bayesian model averaging (BMA) for evaluating the impact of multi-source uncertainties in complex forecast systems. Based on this framework, we used eight numerical weather prediction products from the International Grand Global Ensemble (TIGGE) dataset, four hydrological models with different structures, and 1000 sets of parameters to comprehensively account for the input, structure, and parameter uncertainties. The framework’s application to the Chitan Basin in China revealed that the numerical weather prediction input uncertainty in the forecasting system was more significant than the hydrological model uncertainty. The hydrological model structure uncertainty was more prominent than the parameter uncertainty. The accuracy of the numerical weather prediction dominates the accuracy of the forecast of high flows. In addition, the structures and parameters of the hydrological model and their interactions contributed to the main uncertainty of the low flow forecasts. The streamflow was more realistically represented when the three uncertainty sources were considered jointly. By accounting for the significant uncertainty sources in complex forecast systems, the BMA ensemble forecasting produces more realistic and reliable predictions and reduces the influences of other incomplete considerations. The developed multi-source uncertainty assessment framework improves our understanding of the complex meteorological and hydrological forecasting system. Therefore, the framework is promising for improving the accuracy and reliability of complex forecasting systems.

A joint chance constrained optimization algorithm with robust reconstruction for multi-echelon and multi-period closed-loop manufacturing adjustable system design under multi-source uncertainty

The complex electronic assembly scheduling problem under uncertainty poses a new challenge. The joint chance constraint programming method based on robust reconstruction is used to hedge the endogenous and external uncertainty in the closed-loop manufacturing scheduling model of multi-echelon and multi-product electronic assembly process under multi-source uncertainty in this study. The numerical cases of electronic factory in assembly and closed-loop manufacturing mode are studied, the sensitivity of cost to different types of uncertain parameters is analyzed. The results show that different types of uncertainty have different effects on economic benefits, and endogenous uncertainty has a greater impact on manufacturing costs. Decision makers can adjust the adjustable parameters according to external policies and economic situation to control the conservatism and economic benefits of the scheme, and at the same time reduce the demand for uncertain data and improve the efficiency of solution. The model provides a new sustainable production method for electronic assembly manufacturing practitioners, and makes the best production decision when weighing economic benefits and risks.

Simulation of Cross-Correlated Random Fields for Transversely Anisotropic Soil Slope by Copulas

Multi-source uncertainties yielded by randomness, spatial variability and cross-correlation of soil parameters severely affect the realization of random fields. However, current studies rarely account for these simultaneously, leading to inevitable bias in random field simulation and subsequent structure analysis. In this paper, copula-based cross-correlated random fields for transversely anisotropic soil slope are proposed. Firstly, based on the traditional probabilistic method and random field theory, the effect of the cross-correlation of soil parameters on the random field is comprehensively analyzed. Then copulas, which mainly characterize the dependent structures of random variables, are further expanded to connect multivariate random fields. Four special algorithms associated with Gaussian, Frank, Plackett and No. 16 copulas are subsequently developed. At last, the performance and effectiveness of copula-based cross-correlated random fields are illustrated by means of assumed and engineering slope cases. The results show that the proposed method is amenable to characterizing spatial variability comprising multiple cross-correlated soil parameters of transversely anisotropic slope. Soil profiles can be represented with a relatively high accuracy. Moreover, the performance of copula-based CCRF is simultaneously governed by margins, cross-correlated coefficients and copulas. The proper selection of these crucial factors can considerably reduce multi-source uncertainties. Overall, the proposed method could provide a useful guideline for accurately modeling cross-correlation random fields of soil slope.

A robust-based configuration design method of piezoelectric materials for mechanical load identification considering structural vibration suppression

Piezoelectric materials have received considerable attention to structural vibration suppression and health monitoring by virtue of their transform ability between electric energy and mechanical energy. This study investigates a novel robust-based configuration design method for piezoelectric materials under the assumption of identical configurations for actuator and sensor layers. Firstly, underlying the inverse piezoelectric effects, a load-independent layout design framework of piezoelectric actuators is developed in the context of topology optimization, in which the desired structural vibration suppression can be achieved and the robustness of system performance index representing actuating energies may be guaranteed once uncertainties are involved. Then, based on the direct piezoelectric effects, some piezoelectric sensors are effectively selected from the optimized distributed piezoelectric elements for mechanical load identification considering inevitable signal noises and material dispersion To improve the accuracy of identified results, an adaptive load identification method and an independence-driven sensor selection strategy are conducted. To strengthen the robustness of identified results, an uncertain-oriented load identification method and variance-driven sensor selection strategy are completed. Moreover, interval quantification and propagation methods are adopted to deal with multi-source uncertainties. Eventually, two numerical examples are discussed to demonstrate the validity and feasibility of the developed approach.

Robust Topology Optimization of Continuum Structures under the Hybrid Uncertainties: A Comparative Study

Due to the inevitable involvement of multisource uncertainties related to the load, material property and geometry in practical engineering designs, robust topology optimization (RTO) has recently attracted increasing attention to account for these uncertain effects. However, the majority of the existing RTO works are concerned with single source uncertainty, and very few studies have considered the multisource (hybrid) uncertainties simultaneously. To this end, a comparative study on the hybrid uncertainties (HU), i.e., material-loading, geometric-loading, material-geometric, and material-geometric-loading uncertainties, for RTO of continuum structures is presented in this paper. A truncated Karhunen-Loeve expansion is adopted for uncertainty representation and a sparse grid collocation method for uncertainty propagation of the objective function and constraints. Effects of the various HU on the compliance and robust design are comprehensively investigated and compared with the RTO models under individual component uncertainty using two continuum benchmarks. An important observation from the results is that the hybrid uncertainty model is a conservative state, and the resulting RTO designs tend towards those with loading uncertainty only.

Multivariable Finite-Time Composite Neural Control via Prescribed Performance for Error Norm

This work investigates finite-time tracking control for a multi-input–multi-output plant with multisource uncertainties. A multivariable finite-time prescribed performance control scheme is proposed, where the norm of the tracking error vector is constrained with a prescribed bound. Due to the positiveness of the norm of the error vector, a novel error transformation is given to transform the “constrained” problem into an equivalent “unconstrained” problem. Meanwhile, the composite neural adaptive law is established to attenuate the effect of multisource uncertainties. The parametric perturbations are counteracted by neural adaptive terms. Time-varying uncertain control gains and external disturbances in the multivariable systems are compensated by adaptively estimating their bounds and applying the Lyapunov control design. To tackle the practical tracking problem, the aforementioned method is integrated into a dynamic-surface-based backstepping framework. Additionally, the practical quaternion-based attitude tracking problem is addressed, in which a quaternion-based form of error-norm constraint is constructed to express the generality and scalability of our proposed.

Unified reliability-based design optimization with probabilistic, uncertain-but-bounded and fuzzy variables

Reliability-based design optimization (RBDO) is critical in improving the design objective and guaranteeing the safety level of mechanical and engineering structures. However, a large number of multi-source uncertainties exist in the real-world, and their applications pose significant challenges owing to the lack of unity and generality of the RBDO theory and high performance computational methods. This study proposes a unified reliability-based design optimization (URBDO) method to deal with multi-source uncertainties, in which probabilistic, uncertain-but-bounded, and fuzzy parameters are simultaneously considered. First, a novel URBDO model is proposed by combining the probabilistic, non-probabilistic, and fuzzy theories, and it consists of nested quadruple optimization loops. Second, the single-loop URBDO method is further developed based on the Lagrangian function to ease the unaffordable computational burden, where the probabilistic analysis, non-probabilistic analysis, fuzzy analysis, and deterministic optimization are simultaneously implemented. Third, the directional properties of non-probabilistic and fuzzy computations are disclosed, and the directional chaos single-loop method (DCSLM) and conjugate gradient single-loop method (CGSLM) are constructed based on the chaos control theory and conjugate gradient method. Finally, three numerical examples and three practical engineering examples are tested to validate the effectiveness of the proposed URBDO model and the high performances of the DCSLM and CGSLM.

Optimization and multi-uncertainty analysis of best management practices at the watershed scale: A reliability-level based bayesian network approach

Best management practices (BMPs) have been widely adopted to mitigate diffuse source pollutants, and the simulated processes of its pollutant reduction effectiveness suffer from manifold uncertainties, such as watershed model parameters and climate change. We presented a novel Bayesian modeling framework for BMPs planning, integrating process-based watershed modeling and Bayesian optimization algorithm to reveal the impact of multiple uncertainties. The proposed framework was applied to a BMPs planning case study in the Erhai watershed, the seventh-largest freshwater lake in China. Firstly, priority management areas (PMAs) were identified for BMPs siting using a simulation-optimization approach. Bayesian networks were subsequently embedded to reveal the multiple uncertainty sources in the optimal planning and the reliability level (RL) is introduced to represent the probability to meet the water quality target with BMPs implementation. The results suggest that ENS of discharge and nutrients concentration simulation by LSPC are both greater than 0.5, which displays satisfactory performance. The identified PMAs account for 0.8% of the total watershed areas while contribute to more than 15% of nutrient loadings reduction. The analysis of multiple uncertainty sources reveals that precipitation is the most influential source of uncertainties in BMP effectiveness. The construction of hedgerows plays an important role in the nutrient reduction. With the improvement of the reliability levels, the cost increases sharply, indicating that the implementation of BMPs has a marginal utility. The study addressed the urgent need for effective and efficient BMPs planning by identifying PMAs and addressing multi-source uncertainties.