Robust Adaptive Gain Higher Order Sliding Mode Observer Based Control-constrained Nonlinear Model Predictive Control for Spacecraft Formation Flying

This work deals with the development of a decentralized optimal control algorithm, along with a robust observer,for the relative motion control of spacecraft in leader-follower based formation. An adaptive gain higher order sliding mode observer has been proposed to estimate the velocity as well as unmeasured disturbances from the noisy position measurements.A differentiator structure containing the Lipschitz constant and Lebesgue measurable control input, is utilized for obtaining the estimates. Adaptive tuning algorithms are derived based on Lyapunov stability theory, for updating the observer gains,which will give enough flexibility in the choice of initial estimates.Moreover, it may help to cope with unexpected state jerks. The trajectory tracking problem is formulated as a finite horizon optimal control problem, which is solved online. The control constraints are incorporated by using a nonquadratic performance functional. An adaptive update law has been derived for tuning the step size in the optimization algorithm, which may help to improve the convergence speed. Moreover, it is an attractive alternative to the heuristic choice of step size for diverse operating conditions. The disturbance as well as state estimates from the higher order sliding mode observer are utilized by the plant output prediction model, which will improve the overall performance of the controller. The nonlinear dynamics defined in leader fixed Euler-Hill frame has been considered for the present work and the reference trajectories are generated using Hill-Clohessy-Wiltshire equations of unperturbed motion. The simulation results based on rigorous perturbation analysis are presented to confirm the robustness of the proposed approach.

Dynamics of a Nonautonomous Plant Disease Model with General Nonlinear Incidence Rate and Time-Varying Impulse

In this paper, for controlling the spread of plant diseases, a nonautonomous SEIS (Susceptible → Exposed → Infectious → Susceptible) epidemic model with a general nonlinear incidence rate and time-varying impulsive control strategy is proposed and investigated. This novel model could result in an objective criterion on how to control plant disease transmission by replanting of healthy plants and removal of infected plants. Using the method of small amplitude perturbation, the sufficient conditions under which guarantee the globally attractive of the disease-free periodic solution and the permanence of the disease are obtained, that is, the disease dies out if R12>1.

A Dual-mode Nonlinear Model Predictive Control with the Enlarged Terminal Constraint Sets

Aiming at a class of nonlinear systems with multiple equilibrium points, we present a dual-mode model predictive control algorithm with extended terminal constraint set combined with control invariant set and gain schedule. Local LQR control laws and the corresponding maximum control invariant sets can be designed for finite equilibrium points. It is guaranteed that control invariant sets are overlapped each other. The union of the control invariant sets is treated as the terminal constraint set of predictive control. The feasibility and stability of the novel dual-mode model predictive control are investigated with both variable and fixed horizon. Because of the introduction of extended terminal constrained set, the feasibility of optimization can be guaranteed with short prediction horizon. In this way, the size of the optimization problem is reduced so it is computationally efficient. Finally, a simulation example illustrating the algorithm is presented.

Translational - torsional coupled model based nonlinear dynamic analysis of an NGW planetary gear train

This paper aims to investigate the nonlinear dynamic behaviors of an NGW planetary gear train with multi-clearances and manufacturing/assembling errors. For this purpose, an analytical translational- torsional coupled dynamic model is developed considering the effects of time-varying stiffness, gear backlashes and component errors. Based on the proposed model, the nonlinear differential equations of motion are derived and solved iteratively by the Runge-Kutta method. An NGW planetary gear reducer with three planets is taken as an example to analyze the effects of nonlinear factors. The results indicate that the backlashes induce complicated nonlinear dynamic behaviors in the gear train. With the increment of the backlashes, the gear system has experienced periodic responses, quasi-periodic response and chaos responses in sequence. When the planetary gear system is in a chaotic motion state, the vibration amplitude increases sharply, causing severe vibration and noise. The present study provides a fundamental basis for design and parameter optimization of NGW planetary gear trains.

Nonlinear System Identification with Unknown Piecewise Time-Varying Delay

Identification of nonlinear systems with unknown piecewise time-varying delay is concerned in this paper.Multiple auto regressive exogenous(ARX) models are identified at different process operating points,and the complete dynamics of the nonlinear system is represented by using a combination of a normalized exponential function as the probability density function with each of the local models.The parameters of the local ARX models and the exponential functions as well as the unknown piecewise time-varying delays are estimated simultaneously under the framework of the expectation maximization(EM) algorithm.A simulation example is applied to demonstrating the proposed identification method.

Development of distributed time-variant gain model for nonlinear hydrological systems

In this paper, a rainfall-runoff modeling system is developed based on a nonlinear Volterra functional series and a hydrological conceptual modeling approach. Two models, i.e. the time-variant gain model (TVGM) and the distributed time-variant gain model (DTVGM) that are built on the platform of Digital Elevation Model (DEM), Remote Sensing (RS) and Unit Hydro-logical Process were proposed. The developed DTVGM model was applied to two cases in the Heihe River Basin that is located in the arid and semiarid region of northwestern China and the Chaobai River basin located in the semihumid region of northern China. The results indicate that, in addition to the classic dynamic differential approach to describe nonlinear processes in hy-drological systems, it is possible to study such complex processes through the proposed sys-tematic approach to identify prominent hydrological relations. The DTVGM, coupling the advan-tages of both nonlinear and distributed hydrological models, can simulate variant hydrological processes under different environment conditions. Satisfactory results were obtained in fore-casting the time-space variations of hydrological processes and the relationships between land use/cover change and surface runoff variation.

基于非线性磁路饱和模型的永磁同步电机增益调度电流控制

随着永磁同步电机(permanent magnet synchronous machine,PMSM)过载能力、功率密度和转矩密度的提高,磁路饱和程度加深且变化剧烈,其非线性限制电流环的动态性能并且降低控制器的鲁棒性。该文提出一种PMSM增益调度电流控制策略,基于以微分电感为参数的非线性磁路饱和模型,设计小信号模型的线性内模控制器,根据电流调度变量插值成非线性控制,非线性耦合的消除保持非线性控制小信号化与线性内模控制的一致性,获得不同形式非线性积分的增益调度电流控制器。采用内模控制维持小信号系统相同的闭环特性,使得该非线性控制也具有相同的线性闭环特性,达到反馈线性化的效果,提高大范围工况的性能。通过仿真和实验验证控制器的有效性。

A Distributed Time-Variant Gain Hydrological Model Based on Remote Sensing

提出了一个完全采用遥感数据驱动的分布式时变增益水文模型,该模型通过融雪、产汇流等水文过程计算,给出流量的雪盖、蒸散发、径流等水文要素。在拉萨河流域,该模型采用遥感USGS-SRTM的3秒DEM、遥感TRMM(The Tropical RainfallMeasuring Mission)降水、Modis-LST(Land Surface Temperature)数据,建立分布式水文模型,模拟了2001-2008年日水文过程。模拟结果效率系数接近0.7,相关系数接近0.8,水量平衡误差5%以内。说明完全依靠遥感驱动水文模型进行水文水资源模拟可行。该模型为解决高寒山区无资料或缺资料地区水文水资源问题提供了一个新方法。

Consensus control of feedforward nonlinear multi-agent systems:a time-varying gain method

In this paper,the leader–follower consensus of feedforward nonlinear multi-agent systems is achieved by designing the distributed output feedback controllers with a time-varying gain.The agents dynamics are assumed to be in upper triangular structure and satisfy Lipschitz conditions with an unknown constant multiplied by a time-varying function.A time-varying gain,which increases monotonously and tends to infinity,is proposed to construct a compensator for each follower agent.Based on a directed communication topology,the distributed output feedback controller with a time-varying gain is designed for each follower agent by only using the output information of the follower and its neighbors.It is proved by the Lyapunov theorem that the leader–follower consensus of the multi-agent system is achieved by the proposed consensus protocol.The effectiveness of the proposed time-varying gain method is demonstrated by a circuit system.

Adaptive control of a class of nonlinear time-varying systems with multiple models

The adaptive control of nonlinear systems that are linear in the unknown but time-varying parameters are treated in this paper. Since satisfactory transient performance is an important factor, multiple models are required as these parameters change abruptly in the parameter space. In this paper we consider both the multiple models with switching and tuning methodology as well as multiple models with second level adaptation for this class of systems. We demonstrate that the latter approach is better than the former.

Enhanced adaptive nonlinear extended state observer for pure feedback systems with matched and mismatched disturbances

In this paper, an enhanced adaptive nonlinear extended state observer (EANESO) for single-input single-output pure feedback systems in the presence of external time-varying disturbances is proposed. In this paper, a nonlinear system with matched and mismatched disturbances is considered. The conventional extended state observer (ESO) can only be applied to systems that are in the form of integral chains. Moreover, this method has limitations in the face of mismatched disturbances. In the presence of time-varying disturbances, the traditional ESOs cannot estimate the disturbances accurately. To overcome this limitation, an EANESO is proposed in this paper. The main idea is to design the nonlinear ESO (NESO) to estimate the states of the system and multiple disturbances simultaneously. The observer gains are considered time-varying and adjusted with adaptation laws to improve the estimation accuracy and overcome the peaking phenomenon. Next, the proposed controller is designed based on output feedback to eliminate the effects of multiple disturbances and stabilize the closed-loop system. Subsequently, the stability analysis of the closed-loop system and convergence of the observer error are discussed. Finally, the proposed method is applied to the inverted pendulum system. The simulated results show good performance of the proposed method as compared with a recently published scheme in the related literature.

非线性时变增益模型在辽西地区水文模拟的应用分析

辽西地区由于近些年来水土保持治理措施,流域下垫面条件尤其是土壤蓄水能力得到改善,传统单一线性产流模型已不太适应于辽西地区的水文模拟。为此引入非线性时变增益模型,结合辽西地区叶柏寿和东白城子两个水文站实测场次洪水数据,对其模型精度进行验证。结果表明:相比于传统单一线性模型,非线性时变增益模型下两个水文站实测场次洪水预测合格率分别提高20%和30%。研究成果对于辽西地区洪水预报方案制定具有重要参考价值。

非线性偏振旋转锁模光纤激光器数值模型(英文)

利用二能级Giles模型来计算掺铒光纤增益,并通过琼斯矩阵描述偏振控制器,提出了一种基于非线性薛定谔方程的非线性偏振旋转锁模光纤激光器的数值模型。该模型具有物理意义更加清晰、便于分析泵浦功率对锁模影响的优点。采用1.0 m长的掺铒光纤、5.8 m长的单模光纤和85:15的输出耦合器,当泵浦功率为25 mW时数值计算得到均方根宽度为0.30 ps的超短脉冲。研究了泵浦功率对锁模脉冲波形和光谱的影响,并讨论了泵浦功率与掺铒光纤中增益分布的关系。通过实验得到了锁模脉冲,其光谱形状与仿真得到的结果相似。测量了不同泵浦功率下脉冲的平均功率,变化趋势也与理论值吻合。

辨识Hammerstein模型的两步法

本文利用稳态和动态信息提出了一种辨识Ｈａｍｍｅｒｓｔｅｉｎ模型的新方法─—两步法．该方法利用稳态信息获取非线性增益的强一致性估计；利用动态信息获取线性子系统未知参数的强一致性估计．该方法具有计算简单和辨识精度高等优点．最后的仿真结果说明了该方法的有效性和实用性．

基于混合神经网络的非线性预测函数控制

针对基本预测函数控制只能用于线性对象的控制这一不足,提出了基于混合神经网络的非线性预测函数控制.混合神经网络由BP网络和线性神经网络串连组成.采用混合神经网络对可用Hammerstein模型描述的非线性对象进行有效的辨识.其中,BP网络反映了非线性静态增益,线性神经网络反映了线性动态子系统.利用BP网络求出非线性静态增益的逆并与非线性对象串联,抵消非线性对象中的非线性静态增益部分,将非线性对象的控制问题转化为对线性对象的控制问题,实现了对非线性对象的预测函数控制.当被控对象的特性发生变化时,可对混合神经网络权值及时进行修正并调整控制器参数使控制系统始终保持良好的控制性能.仿真结果表明,此控制系统具有良好的控制效果.

一类双线性Hammerstein模型的集成辨识方法

对于一类静态非线性增益具有原点对称特性的双线性Hammerstein模型 ,提出了一种稳态与动态辨识相结合的集成辨识方法 .该方法利用稳态信息获取稳态模型的强一致性估计 ,并通过稳态模型获得非线性增益的估计 ,再利用动态信息辨识获取双线性Hammeristein模型的双线性系统未知参数的一致性估计 .仿真结果表明了该方法的有效性和实用性 .

闭环增益成形算法用于船舶非线性模型控制

闭环增益成形算法根据闭环频谱及灵敏度函数所希望的形状,用最大奇异值、带宽频率、频谱峰值和高频渐近线斜率4个具有工程意义的参数构造出鲁棒控制器。采用响应型船舶运动非线性数学模型,设计基于闭环增益成形算法的鲁棒控制器,然后在Matlab的Simulink下进行系统仿真。仿真结果表明,所设计的控制器具有良好的鲁棒性能,施舵合理。

引信指标增益型线性加权评价模型应用分析

在指出普通线性加权评价模型缺点的基础上 ,对所构造的增益型线性加权评价模型进行了结构和实用性分析 ,指出增益型模型较好地体现了奖优罚劣的评价原则 ,适合引信指标的综合评价。对评价模型中各结构参数对评价值的影响及其相互关系作了较深层的论述 ,举例叙述了模型中各有关参数 a、s、k和

静止同步补偿器非线性变增益PI控制

阐述了静止同步补偿器(STATCOM)的基本原理和补偿策略。应用模糊控制理论Takagi-Sugeno型模糊推理模型,设计了STATCOM非线性变增益PI控制器,其控制增益能够根据电力系统当前状态依据规则产生大范围调整,在较大的系统工作空间内均可有效提高电力系统电压调节能力和暂态稳定性能。针对含STAT-COM的单机-无穷大(SMIB)系统进行的电力系统暂态仿真,结果表明:该控制器性能优于常规PI控制器。

SMA迟滞系统的PID控制及参数优化研究

形状记忆合金(shape memory alloy,SMA)是一类新型功能材料,作为电热驱动器,广泛应用于工程领域。但由于材料内在的非线性性质和迟滞效应使其在控制系统的设计和热力学建模上较为困难,不利于简单控制系统和实时控制的实现。基于Preisach理论给出SMA驱动器系统的温度-位移迟滞模型,分析系统的热传导过程,将传统的比例-积分-微分PID(proportional-integral-differential,PID)策略应用于控制器的设计,并引入蚁群和粒子群两类智能算法优化控制器参数,讨论优化过程和控制效果;与传统PID参数整定方法相比,系统的阶跃响应和正弦信号跟踪性能取得比较理想的效果,且智能算法调节控制器参数的实时性更强,克服非线性和迟滞效应的影响。