Instrumental Variable Quantile Regression of Spatial Dynamic Durbin Panel Data Model with Fixed Effects

This paper studies a quantile regression spatial dynamic Durbin panel data (SDDPD) model with fixed effects. Conventional fixed effects estimators of quantile regression specification are usually biased in the presentation of lagged response variables in spatial and time as regressors. To reduce this bias, we propose the instrumental variable quantile regression (IVQR) estimator with lagged covariates in spatial and time as instruments. Under some regular conditions, the consistency and asymptotic normalityof the estimators are derived. Monte Carlo simulations show that our estimators not only perform well in finite sample cases at different quantiles but also have robustness for different spatial weights matrices and for different disturbance term distributions. The proposed method is used to analyze the influencing factors of international tourism foreign exchange earnings of 31 provinces in China from 2011 to 2017.

Analysis and Prediction for Confirmed COVID-19 Cases in Czech Republic with Uncertain Logistic Growth Model

This paper presents an uncertain logistic growth model to analyse and predict the evolution of the cumulative number of COVID-19 infection in Czech Republic. Some fundamental knowledge about the uncertain regression analysis are reviewed firstly. Stochastic regression analysis is invalid to model cumulative number of confirmed COVID-19 cases in Czech Republic, by considering the disturbance term as random variables, because that the normality test and the identical distribution test of residuals are not passed, and the residual plot does not look like a null plot in the sense of probability theory. In this case, the uncertain logistic growth model is applied by characterizing the disturbance term as uncertain variables. Then parameter estimation, residual analysis, the forecast value and confidence interval are studied. Additionally, the uncertain hypothesis test is proposed to evaluate the appropriateness of the fitted logistic growth model and estimated disturbance term. The analysis and prediction for the cumulative number of COVID-19 infection in Czech Republic can propose theoretical support for the disease control and prevention. Due to the symmetry and similarity of epidemic transmission, other regions of COVID-19 infections, or other diseases can be disposed in a similar theory and method.

Design of a Disturbance Rejection Controller for a Class of Nonholonomic Systems with Uncertainties

This study investigates the global output feedback stabilization problem for one type of the nonholonomic system with nonvanishing external disturbances. An extended state observer (ESO) is constructed in order to estimate the external disturbance and unmeasurable system states, in which the external disturbance term is seen as a general state. Thus, a new generalized error dynamic system is obtained. Accordingly, a disturbance rejection controller is designed by making use of the backstepping technique. A control law is given to ensure that all the signals in the closed-loop system are globally bounded, while the system states converge to an equilibrium point. The simulation example is proposed to verify that the control algorithm is effective.

Design of an integral super twisting sliding mode controller for position control and extending the traveling range of initially curved micro-beams

Based on the super twisting concept, this article develops an integral sliding mode controller with nonlinear disturbance observer for position control and extending the traveling range of an initially curved micro-beam. The nonlinear damped model of the curved micro-beam is modeled based on the non-classical continuum theory. The single-mode assumption is used to transform the nonlinear governing PDE of the system into a nonlinear state-space form. The robust controller is designed to overcome the position control problem in the presence of non-parametric uncertainties and unknown non-symmetric input saturation due to the existing constraints in the electrostatic actuation. The effects of the uncertainties are considered as a compound disturbance term which can be estimated in finite time. Furthermore, due to the estimation properties of the observer, knowledge about the bounds of the uncertainties is not required. Extensive simulation results clearly verified the effectiveness of the controller in position tracking and extending the traveling range of the initially curved micro-beam to the unstable zones under smooth control effort.

Regression Model-Based Flux Observer for IPMSM Sensorless Control with Wide Speed Range

A new linear regression form is derived for a flux observer and a position observer is designed. In general, the observability of the permanent-magnet synchronous motor is lost at zero speed. In this work, the proposed regressor vector contains current derivative terms in both directions (dq-axis), and it gives the chance for the model-based flux observer to operate at zero speed. When an excitation signal is injected into d and q axes with the proposed flux observer, it helps to satisfy the persistent excitation condition in the low-speed range. Therefore, the sensorless performance of the model-based is improved greatly, even at zero speed. However, it appears with a disturbance term, which depends on the derivative of the d-axis current. Thus, the disturbance does not vanish when an excitation signal is injected. In this work, the disturbance term is also taken care of in constructing an observer. It results in an observer which allows signal injection. Thus, high frequency signal can be injected in the low speed region and turned off when it is unnecessary as the speed increases. This model-based approach utilizes the signal injection directly without recurring to a separate high frequency model. In other words, it provides a seamless transition without switching to the other algorithm. The validity is demonstrated by simulation and experimental results under various load conditions near zero speed.

SUPPRESSION OF NON-SYNCHRONOUS-VIBRATION THROUGH INTENTIONAL AERODYNAMIC AND STRUCTURAL MISTUNING

Non-synchronous vibrations (NSV) arising near the stall boundary of compressors are a recurring and potentially safety-critical problem in modern axial compressors and fans. Recent research has improved predictive capabilities and physical understanding of NSV but prevention measures are still lacking. This paper addresses this by systematically studying the influence of aerodynamic and structural mistuning on NSV. This is achieved by incorporating mistuning effects in a validated linear model, in which individual blade modes are modelled as single-degree of freedom mass oscillators coupled by a convected aerodynamic disturbance term. The results demonstrate that both structural and aerodynamic mistuning are effective. While structural mistuning improves stability by preventing aero-structure lock-in, aerodynamic mistuning, which locally reduces the tip blockage, attenuates the aerodynamic disturbance causing NSV. In the latter case, the circumferentially-averaged conditions are shown to be most influential, while the pattern plays a minor role. A combination of moderate aerodynamic and structural mistuning (1%) was also found to be effective. These findings are relevant for design decisions, demonstrating that small blade-to-blade variations can suppress NSV.

A time-varying grey Riccati model based on interval grey numbers for China's clean energy generation predicting

PurposeIn order to accurately predict the uncertain and nonlinear characteristics of China's three clean energy generation, this paper presents a novel time-varying grey Riccati model (TGRM(1,1)) based on interval grey number sequences.Design/methodology/approachBy combining grey Verhulst model and a special kind of Riccati equation and introducing a time-varying parameter and random disturbance term the authors advance a TGRM(1,1) based on interval grey number sequences. Additionally, interval grey number sequences are converted into middle value sequences and trapezoid area sequences by using geometric characteristics. Then the predicted formula is obtained by using differential equation principle. Finally, the proposed model's predictive effect is evaluated by three numerical examples of China's clean energy generation.FindingsBased on the interval grey number sequences, the TGRM(1,1) is applied to predict the development trend of China's wind power generation, China's hydropower generation and China's nuclear power generation, respectively, to verify the effectiveness of the novel model. The results show that the proposed model has better simulated and predicted performance than compared models.Practical implicationsDue to the uncertain information and continuous changing of clean energy generation in the past decade, interval grey number sequences are introduced to characterize full information of the annual clean energy generation data. And the novel TGRM(1,1) is applied to predict upper and lower bound values of China's clean energy generation, which is significant to give directions for energy policy improvements and modifications.Originality/valueThe main contribution of this paper is to propose a novel TGRM(1,1) based on interval grey number sequences, which considers the changes of parameters over time by introducing a time-varying parameter and random disturbance term. In addition, the model introduces the Riccati equation into classic Verhulst, which has higher practicability and prediction accuracy.

Suppression of Non-Synchronous-Vibration Through Intentional Aerodynamic and Structural Mistuning

Non-synchronous vibrations (NSV) arising near the stall boundary of compressors are a recurring and potentially safety-critical problem in modern axial compressors and fans. Recent research has improved predictive capabilities and physical understanding of NSV but prevention measures are still lacking. This paper addresses this by systematically studying the influence of aerodynamic and structural mistuning on NSV. This is achieved by incorporating mistuning effects in a validated linear model, in which individual blade modes are modelled as single-degree of freedom mass oscillators coupled by a convected aerodynamic disturbance term. The results demonstrate that both structural and aerodynamic mistuning are effective. While structural mistuning improves stability by preventing aero-structure lock-in, aerodynamic mistuning, which locally reduces the tip blockage, attenuates the aerodynamic disturbance causing NSV. In the latter case, the circumferentially-averaged conditions are shown to be most influential, while the pattern plays a minor role. A combination of moderate aerodynamic and structural mistuning (1%) was also found to be effective. These findings are relevant for design decisions, demonstrating that small blade-to-blade variations can suppress NSV.

Novel Two-stage Nonlinear Interconnected Unknown Input Observer Design: Hardware & Experimental Vehicle Validation

This paper concerns both vehicle lateral and longitudinal nonlinear dynamics estimation. Consequently, the interlinked vehicle models dependency and the hurdle coupling features will be overcome here thanks to the NONLINEAR INTERCONNECTED UNKNOWN INPUTS OBSERVER (NI-UIO) framework. This interconnection scheme extends the estimation of the lateral motion to the longitudinal one with the unknown accelerator, brake pedal and driver steering torque inputs, as well as tire-ground pneumatic efforts to reduce conservatism and observability problems. The aspects of immeasurable real-time variation in the forward speed and tire slip velocities in front and rear wheels are taken particularly into account. Consequently, TAKAGI-SUGENO (TS) fuzzy form is undertaken to deal with these nonlinearities in the observer synthesis. The INPUT TO STATE STABILITY (ISS) of the estimation errors is exploited using Lyapunov stability arguments to allow more relaxation and additional robustness guarantee regarding the disturbance term of immeasurable nonlinearities. Therein, sufficient conditions of the ISS property are formulated as an optimization problem in terms of linear matrix inequalities (LMIs). Finally, hardware and experimental validation with robustness test are performed with the well-known SHERPA dynamic interactive driving simulator as well as TWINGO vehicle prototype to highlight performances and applicability of the outlined observer.

Evalaution and Control of Mirror Sliding Friction of Unmanned Ground Vehicle using Radial Basis Neural Network

In order to effectively analyse the mirror sliding friction(MSF) degree of unmanned ground vehicle(UGV) and improve its anti-disturbance performance, a simulation method for MSF degree of UGV based on RBF neural network is proposed. A single-input and double-output RBF neural network is adopted to estimate the uncertain dynamic parameters of the MSF model. The obtained parameters are used to describe the MSF control law based on RBF neural network. An adaptive law based on slow time-varying disturbance characteristics is designed to estimate the total friction disturbance term in the MSF model online. The simulation results show that the proposed method can analyse the MSF degree of unmanned ground vehicle at different speeds and gradients. The influence of gradient on the decline rate of friction degree is greater than that of vehicle speed. The mean error of friction disturbance term calculated by the method is only about 0.9% which has the advantage of low error of friction degree estimation when compared to conventional methods.