Coupled-generalized nonlinear Schr¨odinger equations solved by adaptive step-size methods in interaction picture

We extend two adaptive step-size methods for solving two-dimensional or multi-dimensional generalized nonlinear Schr ¨odinger equation(GNLSE): one is the conservation quantity error adaptive step-control method(RK4IP-CQE), and the other is the local error adaptive step-control method(RK4IP-LEM). The methods are developed in the vector form of fourthorder Runge–Kutta iterative scheme in the interaction picture by converting a vector equation in frequency domain. By simulating the supercontinuum generated from the high birefringence photonic crystal fiber, the calculation accuracies and the efficiencies of the two adaptive step-size methods are discussed. The simulation results show that the two methods have the same global average error, while RK4IP-LEM spends more time than RK4IP-CQE. The decrease of huge calculation time is due to the differences in the convergences of the relative photon number error and the approximated local error between these two adaptive step-size algorithms.

An Adaptive Sequential Replacement Method for Variable Selection in Linear Regression Analysis

With the rapid development of DNA technologies, high throughput genomic data have become a powerful leverage to locate desirable genetic loci associated with traits of importance in various crop species. However, current genetic association mapping analyses are focused on identifying individual QTLs. This study aimed to identify a set of QTLs or genetic markers, which can capture genetic variability for marker-assisted selection. Selecting a set with k loci that can maximize genetic variation out of high throughput genomic data is a challenging issue. In this study, we proposed an adaptive sequential replacement (ASR) method, which is considered a variant of the sequential replacement (SR) method. Through Monte Carlo simulation and comparing with four other selection methods: exhaustive, SR method, forward, and backward methods we found that the ASR method sustains consistent and repeatable results comparable to the exhaustive method with much reduced computational intensity.

New normalized LMS adaptive filter with a variable regularization factor

A new normalized least mean square(NLMS) adaptive filter is first derived from a cost function, which incorporates the conventional one of the NLMS with a minimum-disturbance(MD)constraint. A variable regularization factor(RF) is then employed to control the contribution made by the MD constraint in the cost function. Analysis results show that the RF can be taken as a combination of the step size and regularization parameter in the conventional NLMS. This implies that these parameters can be jointly controlled by simply tuning the RF as the proposed algorithm does. It also demonstrates that the RF can accelerate the convergence rate of the proposed algorithm and its optimal value can be obtained by minimizing the squared noise-free posteriori error. A method for automatically determining the value of the RF is also presented, which is free of any prior knowledge of the noise. While simulation results verify the analytical ones, it is also illustrated that the performance of the proposed algorithm is superior to the state-of-art ones in both the steady-state misalignment and the convergence rate. A novel algorithm is proposed to solve some problems. Simulation results show the effectiveness of the proposed algorithm.

Environmental Adaptive Control of a Snake-like Robot With Variable Stiffness Actuators

This work investigates adaptive stiffness control and motion optimization of a snake-like robot with variable stiffness actuators. The robot can vary its stiffness by controlling magnetorheological fluid(MRF) around actuators. In order to improve the robot's physical stability in complex environments, this work proposes an adaptive stiffness control strategy. This strategy is also useful for the robot to avoid disturbing caused by emergency situations such as collisions. In addition, to obtain optimal stiffness and reduce energy consumption, both torques of actuators and stiffness of the MRF braker are considered and optimized by using an evolutionary optimization algorithm. Simulations and experiments are conducted to verify the proposed adaptive stiffness control and optimization methods for a variable stiffness snake-like robots.

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.

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.

Advanced reliability analysis of slopes in spatially variable soils using multivariate adaptive regression splines

This study aims to extend the multivariate adaptive regression splines(MARS)-Monte Carlo simulation(MCS) method for reliability analysis of slopes in spatially variable soils. This approach is used to explore the influences of the multiscale spatial variability of soil properties on the probability of failure(P_f) of the slopes. In the proposed approach, the relationship between the factor of safety and the soil strength parameters characterized with spatial variability is approximated by the MARS, with the aid of Karhunen-Loeve expansion. MCS is subsequently performed on the established MARS model to evaluate Pf.Finally, a nominally homogeneous cohesive-frictional slope and a heterogeneous cohesive slope, which are both characterized with different spatial variabilities, are utilized to illustrate the proposed approach.Results showed that the proposed approach can estimate the P_f of the slopes efficiently in spatially variable soils with sufficient accuracy. Moreover, the approach is relatively robust to the influence of different statistics of soil properties, thereby making it an effective and practical tool for addressing slope reliability problems concerning time-consuming deterministic stability models with low levels of P_f.Furthermore, disregarding the multiscale spatial variability of soil properties can overestimate or underestimate the P_f. Although the difference is small in general, the multiscale spatial variability of the soil properties must still be considered in the reliability analysis of heterogeneous slopes, especially for those highly related to cost effective and accurate designs.

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.

Discrete-Rate Adaptive Modulation with Variable Threshold for Distributed Antenna System in the Presence of Imperfect CSI

Discrete-rate adaptive modulation （AM） scheme for distributed antenna system （DAS） with imperfect channel state information （CSI） is developed, and the corresponding performance is investigated in composite Rayleigh channel. Subject to target bit error rate （BER） constraint, an improved fixed switching threshold （FST） for the AM scheme is presented by means of tightly-approximate BER expression, and it can avoid the performance loss fxom conventional FST. Based on the imperfect CSI, the variable switching threshold （VST） is derived by utilizing the maximum a posteriori method. This VST includes the improved FST as a special case, and may lower the impact of estimation error on the performance. By the switching thresholds, the spectrum efficiency （SE） and average BER of the system are respectively derived, and resulting closed- form expressions are attained. With these expressions, the system performance can be effectively evaluated. Simulation results show that the derived theoretical SE and BER can match the simulations well. Moreover, the AM with the presented FST has higher SE than that with the conventional one, and the AM with VST can tolerate the large estimation error while maintaining the target BER.

A new variable step-size LMS method and its application in DOA estimation of OFDMA signals

To meet the requirements of quick positioning of mobile terminals from base stations(BSs)or third-party devices,as well as to improve the convergence speed and reduce the steady state maladjustment of the least mean square(LMS)method,a new logarithmic-sigmoid variable step-size LMS(LG-SVSLMS)was proposed and applied to estimate the direction of arrival(DOA)of orthogonal frequency division multiple access(OFDMA)signals.Based on the proposed LG-SVSLMS,a non-blind DOA estimation system for OFDMA signals was constructed.The proposed LG-SVSLMS adopts a new multi-parameter step-size update function which combines the sigmoid function and the logarithmic function.It controls the adjustment magnitude of step-size during the initial and steady state phases of the LMS method to achieve both a high convergence speed and low steady state maladjustment.Finally,simulation was conducted to verify the performance of the LG-SVSLMS.The simulation results show that the non-blind DOA estimation system based on the LG-SVSLMS can accurately estimate the DOA of the target signal in the scenario where interference signals from multi-source and multi-path fading signals arrive at the third-party devices asynchronously with the target signal,and the estimation deviation is within±3°.The non-blind DOA estimation for OFDMA signals with the proposed LG-SVSLMS is of great significance for the instant positioning technology of mobile terminals based on the adaptive antenna array.

Adaptive variable structure control for large-scale time-delayed systems with unknown nonlinear dead-zone

The problem of adaptive fuzzy control for a class of large-scale, time-delayed systems with unknown nonlinear dead-zone is discussed here. Based on the principle of variable structure control, a design scheme of adaptive, decentralized, variable structure control is proposed. The approach removes the conditions that the dead-zone slopes and boundaries are equal and symmetric, respectively. In addition, it does not require that the assumptions that all parameters of the nonlinear dead-zone model and the lumped uncertainty are known constants. The adaptive compensation terms of the approximation errors axe adopted to minimize the influence of modeling errors and parameter estimation errors. By theoretical analysis, the closed-loop control system is proved to be semiglobally uniformly ultimately bounded, with tracking errors converging to zero. Simulation results demonstrate the effectiveness of the approach.

Adaptive tracking algorithm based on 3D variable turn model

Satisfactory results cannot be obtained when three-dimensional (3D) targets with complex maneuvering characteristics are tracked by the commonly used two-dimensional coordinated turn (2DCT) model. To address the problem of 3D target tracking with strong maneuverability, on the basis of the modified three-dimensional variable turn (3DVT) model, an adaptive tracking algorithm is proposed by combining with the cubature Kalman filter (CKF) in this paper. Through ideology of real-time identification, the parameters of the model are changed to adjust the state transition matrix and the state noise covariance matrix. Therefore, states of the target are matched in real-time to achieve the purpose of adaptive tracking. Finally, four simulations are analyzed in different settings by the Monte Carlo method. All results show that the proposed algorithm can update parameters of the model and identify motion characteristics in real-time when targets tracking also has a better tracking accuracy.

Nonlinear combined forecasting model based on fuzzy adaptive variable weight and its application

In order to enhance forecasting precision of problems about nonlinear time series in a complex industry system,a new nonlinear fuzzy adaptive variable weight combined forecasting model was established by using conceptions of the relative error,the change tendency of the forecasted object,gray basic weight and adaptive control coefficient on the basis of the method of fuzzy variable weight.Based on Visual Basic 6.0 platform,a fuzzy adaptive variable weight combined forecasting and management system was developed.The application results reveal that the forecasting precisions from the new nonlinear combined forecasting model are higher than those of other single combined forecasting models and the combined forecasting and management system is very powerful tool for the required decision in complex industry system.

Novel LMS adaptive filtering algorithm with variable step size

By analyzing algorithms available for variable step size least mean square(LMS)adaptive filter,a new modified LMS adaptive filtering algorithm with variable step size is proposed,along with performance analysis based on different parameters.Compared with the existing algorithms through the simulation,the proposed algorithm has faster convergence speed and smaller steady state error.

Adaptive Sliding Mode BTT Autopilot for Cruise Missiles with Variable-Swept Wings

In this paper,an adaptive sliding mode method was proposed for BTT autopilot of cruise missiles with variable-swept wings. To realize the whole state feedback,the roll angle,normal overloads and angular rates were considered as state variables of the autopilot,and a parametric sliding mode controller was designed via feedback linearization. A novel parametric adaptation law was put forward to estimate the nonlinear timevarying parameter perturbations in real time based on Lyapunov stability theory. A sliding mode boundary layer theory was adopted to smooth the discontinuity of control variables and eliminate the control chattering. The simulation was presented for the roll angle and overload commands tracking in different configuration schemes. The results indicated that the controlled system has robust dynamic tracking performance in condition of the large-scale aerodynamic parametric variety resulted from variable-swept wings.

Variable Structure Model Reference Adaptive Control with Unknown High Frequency Gain Sign

可变结构模型引用为有比一个和未知高频率获得符号大的相对的度的工厂的适应控制被建议。一个切换的计划基于为靠近的环系统的第一个辅助错误设计的监视功能被介绍。在至多切换和追踪的错误的一个有限数字收敛到能被适当地选择一些设计参数使任意地小的一个剩余集合以后，在监视函数的管理下面，切换停止，这被显示出在至多切换和追踪的错误的一个有限数字收敛到能被适当地选择一些设计参数使任意地小的一个剩余集合以后。

VARIABLE STRUCTURE MODEL－FOLLOWING ADAPTIVE CONTROL DESIGN FOR ROBOT MANIPULATORS

The increasing demand on robotic system performance leads to the use of advanced control strategies. A variable structure model-following adaptive control design is presented for the nonlinear robot manipulator systems, when subjected to fast and wide ranges of unknown-but-bounded parameter variations and disturbances. The design does not require any knowledge of a nonlinear robotic system. The system is robust and insensitive to the parameter variation, disturbances, as well as to the unmodeled dynamics. This insensitive property enables the elimination of interactions among the various joints of the robotic manipulator. In the closed loop, the robotic system asymptotically converges to the reference trajectory with a Prescribed transient resPOnse. The problem of chattering is discussed with the introduction of the special approaches: boundary layer, smoothing law, and nonlinear compensation.

Research on Variable Structure and Adaptive Control for Strap-on Rocket

Considering the increase of structural disturbance caused by large thrust misalignment and lack of synchronism after installation of the solid booster on the rock,as well as the increase of external disturbance resulting from the installation of the configuration and tail,while also considering the parameter uncertainties,parameter perturbations,unmodeled dynamics and coupling between channels during modeling,this paper proposes the design method for the adaptive control of sliding mode variable structure,based on the model reference. The paper firstly establishes the attitude dynamics model for the solid strap-on launch vehicle; then proposes the design method for the adaptive control of the sliding mode variable structure based on the model reference,implements the design of attitude control system for the three channels respectively,and uses the Lyapunov function to prove the global asymptotic stability; and finally verifies,through numerical simulation,that the control method proposed in this paper can guarantee the attitude stability of rockets in the primary flight phase.

Variable Parameter Self-Adaptive Control Strategy Based on Driving Condition Identification for Plug-In Hybrid Electric Bus

A variable parameter self-adaptive control strategy based on driving condition identification is proposed to take full advantage of the fuel saving potential of the plug-in hybrid electric bus(PHEB).Firstly,the principal component analysis(PCA)and the fuzzy c-means clustering(FCM)algorithm is used to construct the comprehensive driving cycle,congestion driving cycle,urban driving cycle and suburban driving cycle of Chinese urban buses.Secondly,an improved particle swarm optimization(IPSO)algorithm is proposed,and is used to optimize the control parameters of PHEB under different driving cycles,respectively.Then,the variable parameter self-adaptive control strategy based on driving condition identification is given.Finally,for an actual running vehicle,the driving condition is identified by relevance vector machine(RVM),and the corresponding control parameters are selected to control the vehicle.The simulation results show that the fuel consumption of using the variable parameter self-adaptive control strategy is reduced by 4.2% compared with that of the fixed parameter control strategy,and the feasibility of the variable parameter self-adaptive control strategy is verified.

A Variable Structure Model Reference Adaptive Control

A variable structure model reference adaptive control problem for the nonlinear system is studied in this paper. First, according to the relative degree concept of th nonlinear control system an error equation between the outputs of the reference model and the controlled plant is derived. Then, by using the variable structrue control method, an algorithm of variable structure model reference adaptive control is deduced on the basis of a new concept of reaching law. The definition of the SISO system is introduced into the MIMO nonlinear system. Finally, as an example, a pendulum nonlinear control system is simulated to demonstrated the effectiveness of the method. The results show that the method has some advantages: the design is simple, intuitive and easy to be realized in engineering. Besides, it is of practical significance for the synthesis of nonlinear control systems.