Evaluation of Two Momentum Control Variable Schemes and Their Impact on the Variational Assimilation of Radar Wind Data:Case Study of a Squall Line

Different choices of control variables in variational assimilation can bring about different influences on the analyzed atmospheric state. Based on the WRF model＇s three-dimensional variational assimilation system, this study compares the be- havior of two momentum control variable options-streamfunction velocity potential （ψ-χ） and horizontal wind components （U-V）-in radar wind data assimilation for a squall line case that occurred in Jiangsu Province on 24 August 2014. The wind increment from the single observation test shows that the ψ-χ control variable scheme produces negative increments in the neighborhood around the observation point because streamfunction and velocity potential preserve integrals of velocity. On the contrary, the U-V control variable scheme objectively reflects the information of the observation itself. Furthermore, radial velocity data from 17 Doppler radars in eastern China are assimilated. As compared to the impact of conventional observation, the assimilation of radar radial velocity based on the U-V control variable scheme significantly improves the mesoscale dynamic field in the initial condition. The enhanced low-level jet stream, water vapor convergence and low-level wind shear result in better squall line forecasting. However, the ψ-χ control variable scheme generates a discontinuous wind field and unrealistic convergence/divergence in the analyzed field, which lead to a degraded precipitation forecast.

Background Error Covariance Statistics of Hydrometeor Control Variables Based on Gaussian Transform

Use of data assimilation to initialize hydrometeors plays a vital role in numerical weather prediction(NWP).To directly analyze hydrometeors in data assimilation systems from cloud-sensitive observations,hydrometeor control variables are necessary.Common data assimilation systems theoretically require that the probability density functions(PDFs)of analysis,background,and observation errors should satisfy the Gaussian unbiased assumptions.In this study,a Gaussian transform method is proposed to transform hydrometeors to more Gaussian variables,which is modified from the Softmax function and renamed as Quasi-Softmax transform.The Quasi-Softmax transform method then is compared to the original hydrometeor mixing ratios and their logarithmic transform and Softmax transform.The spatial distribution,the non-Gaussian nature of the background errors,and the characteristics of the background errors of hydrometeors in each method are studied.Compared to the logarithmic and Softmax transform,the Quasi-Softmax method keeps the vertical distribution of the original hydrometeor mixing ratios to the greatest extent.The results of the D′Agostino test show that the hydrometeors transformed by the Quasi-Softmax method are more Gaussian when compared to the other methods.The Gaussian transform has been added to the control variable transform to estimate the background error covariances.Results show that the characteristics of the hydrometeor background errors are reasonable for the Quasi-Softmax method.The transformed hydrometeors using the Quasi-Softmax transform meet the Gaussian unbiased assumptions of the data assimilation system,and are promising control variables for data assimilation systems.

Predictive Control Model Based on the MPC Algorithm on Three Different Road Sections

Predictive control is an advanced control algorithm,which is widely used in industrial process control.Among them,model predictive control(MPC)is an important branch of predictive control.Its basic principle is to use the system model to predict future behavior and determine the current control action by optimizing the objective function.This paper discusses the application of MPC in the prediction and control of the speed of vehicles to optimize traffic flow.It is a valuable reference for alleviating traffic congestion and improving travel efficiency and smoothness and provides scientific basis and technical support for future highway traffic management.

Fuzzy variable structure delay control algorithms and their applications to AQM

This paper analyzes the fuzzy variable structure control algorithms for delay systems and describes the compensation mechanism of the integral factor to the effect of the delay. Based on the linearized model of the congestion-avoidance flow-control mode of transmission control protocol (TCP), we present delay control algorithms for active queue management (AQM) and discuss the parameter tuning of the algorithms. The NS (network simulator) simulation results show that the proposed control scheme for the nonlinear TCP/AQM model has good performance and robustness with respect to the uncertainties of the round-trip time (RTT) and the number of active TCP sessions. Compared to other similar schemes, our algorithms perform better in terms of packet loss ratio, throughput and butter fluctuation.

On the Motion of Controllable Mechanical Systems Having Variable Mass

The differential equations of motion of a comtlaint system with parameters and variable mass, of a system with variable mass and servo constraints and those for the control problem on the forced motion of constraint systems with variable mass are given respectively. Finally, an example is presented.

ROBUST STABILIZATING FOR A OLASS OF UNOERTAIN NONLINEAR INTEROONNECTED SYSTEMS USING VARIABLE STRUCTURE CONTROL

The robust stabilizating control problem for a class of uncertain nonlinear large-scale systems is discussed. Based on the theory of both input/output (I/O) linearization and decentralized variable structure control (VSC),two-level and decentralized variable structure control laws for this kind of systems are presented respectively,which achieve asymptotically stabilization despite the uncertainties and disturbances. At last,sirnulation of the disturbed two-pendulum system is given to illustrate the feasibility of proposed technique.

Multivariable Control Performance Assessment Based on Generalized Minimum Variance Benchmark

This paper is concerned with the control performance assessment based on the multivariable generalized minimum variance benchmark.An explicit expression for the feedback controller-invariant(the generalized minimum variance)term of the multivariable control system is obtained,which is used as a standard benchmark for the assessment of the control performance for multi input multi output(MIMO)process.The proposed approach is based on the multivariable minimum variance benchmark.In comparison with the minimum variance benchmark, the developed method is more reasonable and practical for the control performance assessment of multivariable systems.The approach is illustrated by a simulation example and an industrial application.

Adaptive variable structure control based on backstepping for spacecraft with reaction wheels during attitude maneuver

An adaptive variable structure control method based on backstepping is proposed for the attitude maneuver problem of rigid spacecraft with reaction wheel dynamics in the presence of uncertain inertia matrix and external disturbances. The proposed control approach is a combination of the backstepping and the adaptive variable structure control. The cascaded structure of the attitude maneuver control system with reaction wheel dynamics gives the advantage for applying the backstepping method to construct Lyapunov functions. The robust stability to external disturbances and parametric uncertainty is guaranteed by the adaptive variable structure control. To validate the proposed control algorithm, numerical simulations using the proposed approach are performed for the attitude maneuver mission of rigid spacecraft with a configuration consisting of four reaction wheels for actuator and three magnetorquers for momentum unloading. Simulation results verify the effectiveness of the proposed control algorithm.

Approximation law for discrete-time variable structure control systems

Two approximation laws of sliding mode for discrete-time variable structure control systems are proposed to overcome the limitations of the exponential approximation law and the variable rate approximation law. By applying the proposed approximation laws of sliding mode to discrete-time variable structure control systems, the stability of origin can be guaranteed, and the chattering along the switching surface caused by discrete-time variable structure control can be restrained effectively. In designing of approximation laws, the problem that the system control input is restricted is also considered, which is very important in practical systems. Finally a simulation example shows the effectiveness of the two approximation laws proposed.

Adaptive Variable Structure Control of MIMO Nonlinear Systems with Time-varying Delays and Unknown Dead-zones

In this paper, adaptive variable structure neural control is presented for a class of uncertain multi-input multi-output （MIMO） nonlinear systems with state time-varying delays and unknown nonlinear dead-zones. The unknown time-varying delay uncer- tainties are compensated for using appropriate Lyapunov-Krasovskii functionals in the design. The approach removes the assumption of linear function outside the deadband without necessarily constructing a dead-zone inverse as an added contribution. By utilizing the integral-type Lyapunov function and introducing an adaptive compensation term for the upper bound of the residual and optimal approximation error as well as the dead-zone disturbance, the closed-loop control system is proved to be semi-globally uniformly ultimately bounded. In addition, a modified adaptive control algorithm is given in order to avoid the high-frequency chattering phenomenon. Simulation results demonstrate the effectiveness of the approach.

Optimal variable structure control with sliding modes for unstable processes

In this work,a variable structure control(VSC)technique is proposed to achieve satisfactory robustness for unstable processes.Optimal values of unknown parameters of VSC are obtained using Whale optimization algorithm which was recently reported in literature.Stability analysis has been done to verify the suitability of the proposed structure for industrial processes.The proposed control strategy is applied to three different types of unstable processes including non-minimum phase and nonlinear systems.A comparative study ensures that the proposed scheme gives superior performance over the recently reported VSC system.Furthermore,the proposed method gives satisfactory results for a cart inverted pendulum system in the presence of external disturbance and noise.

Sliding Mode Variable Structure Control for Visual Servoing System

A visual servoing tracking controller is proposed based on the sliding mode control theory in order to achieve strong robustness against parameter variations and external disturbances. A sliding plane with time delay compensation is presented by the pre-estimate of states. To reduce the chattering of the sliding mode controller, a modified exponential reaching law and hyperbolic tangent function are applied to the design of visual controller and robot joint controller. Simulation results show that the visual servoing control scheme is robust and has good tracking performance.

Variable structure control with sliding mode prediction for discrete-time nonlinear systems

A new variable structure control algorithm based on sliding mode prediction for a class of discrete-time nonlinear systems is presented. By employing a special model to predict future sliding mode value, and combining feedback correction and receding horizon optimization methods which are extensively applied on predictive control strategy, a discrete-time variable structure control law is constructed. The closed-loop systems are proved to have robustness to uncertainties with unspecified boundaries. Numerical simulation and pendulum experiment results illustrate that the closed-loop systems possess desired performance, such as strong robustness, fast convergence and chattering elimination.

An Approach to Polynomial NARX/NARMAX Systems Identification in a Closed-loop with Variable Structure Control

Many physical processes have nonlinear behavior which can be well represented by a polynomial NARX or NARMAX model. The identification of such models has been widely explored in literature. The majority of these approaches are for the open-loop identification. However, for reasons such as safety and production restrictions, open-loop identification cannot always be done. In such cases, closed-loop identification is necessary. This paper presents a two-step approach to closed-loop identification of the polynomial NARX/NARMAX systems with variable structure control （VSC）. First, a genetic algorithm （GA） is used to maximize the similarity of VSC signal to white noise by tuning the switching function parameters. Second, the system is simulated again and its parameters are estimated by an algorithm of the least square （LS） family. Finally, simulation examples are given to show the validity of the proposed approach.

ADAPTIVE VARIABLE STRUCTURE CONTROLLERS AND APPLICATION TO ENGINE IDLE SPEED CONTROL SIMULATION

A neural-network-based adaptive variable structure control methodology isproposed for the tracking problem of nonlinear discrete-time input-output systems. The unknowndynamics of the system are approximated via radial basis function neural networks. The control lawis based on sliding modes and simple to implement. The discrete-time adaptive law for tuning theweight of neural networks is presented using the adaptive filtering algorithm with residueupper-bound compensation. The application of the proposed controller to engine idle speed controldesign is discussed. The results indicate the validation and effectiveness of this approach.

Fault detection and accommodation via neural network and variable structure control

This paper proposes a novel idea that classifies faults into two different kinds： serious faults and small faults, and treats them with different strategies respectively. A kind of artificial neural network （ANN） is proposed for detecting serious faults, and variable structure （VS） model-following control is constructed for accommodating small faults. The proposed framework takes both advantages of qualitative way and quantitative way of fault detection and accommodation. Moreover, the uncertainty case is investigated and the VS controller is modified. Simulation results of a remotely piloted aircraft with control actuator failures illustrate the performance of the developed algorithm.

Compensation for secondary uncertainty in electro-hydraulic servo system by gain adaptive sliding mode variable structure control

Based on consideration of the differential relations between the immeasurable variables and measurable variables in electro-hydraulic servo system,adaptive dynamic recurrent fuzzy neural networks(ADRFNNs) were employed to identify the primary uncertainty and the mathematic model of the system was turned into an equivalent linear model with terms of secondary uncertainty.At the same time,gain adaptive sliding mode variable structure control(GASMVSC) was employed to synthesize the control effort.The results show that the unrealization problem caused by some system's immeasurable state variables in traditional fuzzy neural networks(TFNN) taking all state variables as its inputs is overcome.On the other hand,the identification by the ADRFNNs online with high accuracy and the adaptive function of the correction term's gain in the GASMVSC make the system possess strong robustness and improved steady accuracy,and the chattering phenomenon of the control effort is also suppressed effectively.

IMPLICIT ITERATIVE METHODS WITH VARIABLE CONTROL PARAMETERS FOR ILL-POSED OPERATOR EQUATIONS

This paper discusses a kind of implicit iterative methods with some variable parameters, which are called control parameters, for solving ill-posed operator equations. The theoretical results show that the new methods always lead to optimal convergence rates and have some other important features, especially the methods can be implemented parallelly.

Nonlinear and Variable Structure Excitation Controller for Power System Stability

A new nonlinear variable structure excitation controller is proposed. Its design combines the differential geometry theory and the variable structure controlling theory. The mathematical model in the form of ＂an affme nonlinear system＂ is set up for the control of a large-scale power system. The static and dynamic performances of the nonlinear variable structure controller are simulated. The response of system with the controller proposed is compared to that of the nonlinear optimal controller when the system is subjected to a variety of disturbances. Simulation results show that the nonlinear variable structure excitation controller gives more satisfactorily static and dynamic performance and better robustness.

Some new criteria for lag synchronization of chaotic Lur'e systems by replacing variables control

Some new criteria for the chaotic lag synchronization are proposed. At first,lag synchronization scheme for identical master-slave Lur‘ e systems by replacing variables control and the relevant error system are given, and the relations between absolute stability of the error system and the chaotic lag synchronization are described. Then, based on a quadratic Lyapunov function, two new Lur‘ e criteria for the above chaotic lag synchronization are proved. Four corresponding frequency domain criteria are further derived by means of Meyer-Kalman-Yacubovia Lemma. These frequency domain criteria are applied to analyze the lag synchronization of general master-slave Chua's circuits so that some ranges of the parameters in which the master-slave Chua's circuits achieve chaotic lag synchronization by replacing single-variable control are attained. Finally, some examples are given to verify the theoretical results.