High-Speed Trains Automatic Operation with Protection Constraints: A Resilient Nonlinear Gain-based Feedback Control Approach

This paper addresses the control design for automatic train operation of high-speed trains with protection constraints.A new resilient nonlinear gain-based feedback control approach is proposed,which is capable of guaranteeing,under some proper non-restrictive initial conditions,the protection constraints control raised by the distance-to-go(moving authority)curve and automatic train protection in practice.A new hyperbolic tangent function-based model is presented to mimic the whole operation process of high-speed trains.The proposed feedback control methods are easily implementable and computationally inexpensive because the presence of only two feedback gains guarantee satisfactory tracking performance and closed-loop stability,no adaptations of unknown parameters,function approximation of unknown nonlinearities,and attenuation of external disturbances in the proposed control strategies.Finally,rigorous proofs and comparative simulation results are given to demonstrate the effectiveness of the proposed approaches.

Compression and stretching of ring vortex in a bulk nonlinear medium

We explore the nonlinear gain coupled Schrödinger system through the utilization of the variables separation method and ansatz technique.By employing these approaches,we generate hierarchies of explicit dissipative vector vortices(DVVs)that possess diverse vorticity values.Numerous fundamental characteristics of the DVVs are examined,encompassing amplitude profiles,energy fluxes,parameter effects,as well as linear and dynamic stability.

Topological laser on square lattice with gain–loss-induced higher-order corner modes

We investigate the higher-order topological laser in the two-dimensional(2D) coupled-cavity array. By adding staggered on-site gain and loss to the 2D Hermitian array with a trivial phase, the system will emerge degenerate topological corner modes, which are protected by bulk band gap. For such a non-Hermitian model, by adjusting the parameters of the system and introducing the pumping into the cavity at the corner, a single-mode lasing with topological protection emerges.Furthermore, single-mode lasing exists over a wide range of pumping strengths. No matter where the cavity is initially stimulated, after enough time evolution, all the cavities belonging to the topological corner mode can emit a stable laser.

Fuzzy Adaptive Control of Stochastic Nonlinear Systems with Unknown Virtual Control Gain Function

The problem of track control is studied for a class of strict-feedback stochastic nonlinear systems in which unknown virtual control gain function is the main feature. First, the so-called stochastic LaSalle theory is extended to some extent, and accordingly, the results of global ultimate boundedness for stochastic nonlinear systems are developed. Next, a new design scheme of fuzzy adaptive control is proposed. The advantage of it is that it does not require priori knowledge of virtual control gain function sign, which is usually demanded in many designs. At the same time, the track performance of closed-loop systems is improved by adaptive modifying the estimated error upper bound. By theoretical analysis, the signals of closed-loop systems are globally ultimately bounded in probability and the track error converges to a small residual set around the origin in 4th-power expectation.

Fault diagnosis based on high-gain observer with an update law for a class of nonlinear systems

In this paper,by using the well-known high-gain observer design,an update law for the gain and an adaptive estimation of parameters,a new method of fault diagnosis for a class of nonlinear systems is presented.Without resort to any transformation for the parameters,the estimation errors of the states and the parameters are guaranteed to be globally exponentially convergent by a persistent excitation condition.Compared to the existing results,it can be applied to nonlinear systems with nonlinear terms admitting an incremental rate depending on the measured output.A case study further verifies the validity of the proposed research.

Stable adaptive fuzzy control of nonlinear systems using small-gain theorem and LMI approach

A new design scheme of stable adaptive fuzzy control for a class of nonlinear systems is proposed in this paper.The T-S fuzzy model is employed to represent the systems.First,the concept of the so-called parallel distributed compensation （PDC） and linear matrix inequality （LMI） approach are employed to design the state feedback controller without considering the error caused by fuzzy modeling.Sufficient conditions with respect to decay rate α are derived in the sense of Lyapunov asymptotic stability.Finally,the error caused by fuzzy modeling is considered and the input-to-state stable （ISS） method is used to design the adaptive compensation term to reduce the effect of the modeling error.By the small-gain theorem,the resulting closed-loop system is proved to be input-to-state stable.Theoretical analysis verifies that the state converges to zero and all signals of the closed-loop systems are bounded.The effectiveness of the proposed controller design methodology is demonstrated through numerical simulation on the chaotic Henon system.

Adaptive Fuzzy Backstepping Output Feedback Control of Nonlinear Time-delay Systems with Unknown High-frequency Gain Sign

In this paper, an adaptive fuzzy robust feedback control approach is proposed for a class of single-input and singleoutput （SISO） strict-feedback nonlinear systems with unknown nonlinear functions, time delays, unknown high-frequency gain sign, and without the measurements of the states. In the backstepping recursive design, fuzzy logic systems are employed to approximate the unknown smooth nonlinear functions, K-filters is designed to estimate the unmeasured states, and Nussbaum gain functions are introduced to solve the problem of unknown sign of high-frequency gain. By combining adaptive fuzzy control theory and adaptive backstepping design, a stable adaptive fuzzy output feedback control scheme is developed. It has been proven that the proposed adaptive fuzzy robust control approach can guarantee that all the signals of the closed-loop system are uniformly ultimately bounded and the tracking error can converge to a small neighborhood of the origin by appropriately choosing design parameters. Simulation results have shown the effectiveness of the proposed method.

基于深度神经网络的多聚焦红外图像非线性增强

当红外图像中含有多个聚焦目标主体时,会导致红外图像质量下降,部分区域出现模糊,因此,提出基于深度神经网络的多聚焦红外图像非线性增强方法。在引导滤波的作用下,从红外图像中获取细节层图像和背景层图像,在细节层图像中建立局部清晰度评价函数,得到清晰的细节层和背景层并融合二者。利用构建的深度神经网络结构,建立非线性增益函数,通过设定阈值和调整子带系数,实现对多聚焦红外图像的非线性增强。测试结果表明,所提方法在增强图像过程中,并没有改变原始图像的亮度和纹理波动;增强后的图像信息熵比原图像提高了57%左右,图像抗噪性能值更高,平均为7.4 dB,图像更清晰,SSIM值更趋近于1,平均为0.98,增强后的图片与原图片图像相似度更高,更接近于真实图像。

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.

Nonlinear General Integral Control Design via Equal Ratio Gain Technique

This paper proposes two kinds of nonlinear general integral controllers, that is, one is generic and another is practical, for a class of uncertain nonlinear system. By extending equal ratio gain technique to a canonical interval system matrix and using Lyapunov method, theorems to ensure regionally as well as semi-globally asymptotic stability are established in terms of some bounded information. Moreover, for the practical nonlinear integral controller, a real time method to evaluate the equal ratio coefficient is proposed such that its value can be chosen moderately. Theoretical analysis and simulation results demonstrated that not only nonlinear general integral control can effectively deal with the uncertain nonlinear system but also equal ratio gain technique is a powerful and practical tool to solve the control design problem of dynamics with the nonlinear and uncertain actions.

基于TNESO的PMSLM分数阶自抗扰控制

线性自抗扰控制(LADRC)以结构简单、易于实现且抗干扰能力强的特点在永磁同步直线电机(PMSLM)中得到广泛应用,但电机启动和负载突变会导致LADRC系统中的线性扩张状态观测器(LESO)扰动估计精度下降。为此,提出一种基于时变增益非线性扩张状态观测器(TNESO)的PMSLM分数阶自抗扰控制策略。结合LESO和非线性扰动观测器(NDOB),并根据误差与增益的关系构建动态增益函数,设计新型状态观测器TNESO;在此基础上,以分数阶PDμ控制律代替线性状态误差反馈控制律(LSEF),建立改进型自抗扰速度控制系统,以进一步提高电机控制的实时性和鲁棒性。仿真与实验结果表明:所设计的速度控制系统可以有效减少直线电机启动、负载突变时的观测响应时间,抑制观测初始时刻扰动峰值,从而满足高性能的PMSLM速度控制要求。

基于非线性修饰和零阶保持器的船舶航向保持控制

[目的]为了解决船舶在海上航行时控制器舵角输出大、打舵频率高、控制速度较慢以及控制精度较低的问题,利用三阶闭环增益成形算法设计鲁棒控制器。[方法]首先,利用三阶闭环增益成形算法设计出线性鲁棒控制器,然后,在控制策略中加入双曲正切非线性修饰以及零阶保持器,在不同海况下对该控制器的性能进行仿真实验。[结果]仿真结果表明,相比于基于非线性修饰的传统PID控制器,在一般海况下,所提控制器在延迟时间、控制精度以及能量输出上分别改进了36%,14%和32%;在恶劣海况下,分别改进了27%,7%和16%。此外,不同海况下该控制器的仿真结果都具有稳定的舵角输出,并可以较快地稳定在临界值附近,证明了其具有较好的鲁棒性。[结论]改进的控制器符合工程实践,对于智能船舶的控制具有较好的应用参考价值。

一种含扰动离散非线性系统状态观测器的设计方法

针对一类含扰动的非线性离散系统,讨论了系统状态观测器的设计问题,给出了一种离散系统状态观测器的设计方法,并将该方法应用到奇异系统观测器设计中。首先,根据李雅普诺夫稳定性理论,得到离散系统在不含外部扰动下观测器增益矩阵存在的充分条件;其次,考虑到离散系统的扰动项,针对系统中的外部扰动在李雅普诺夫函数设计中纳入性能指标,得到含扰动情况下离散系统观测器增益矩阵存在的充分条件,该方法可以通过设置性能指标减弱扰动对系统状态估计的影响,同时也有利于增益矩阵的设计与求解;最后,将非线性离散系统观测器的设计方法推广到奇异系统中,从而使该方法更具一般性。利用三个数值算例对所提方法进行了检验,仿真结果表明了文中所提方法的可行性与有效性。

一种含扰动非线性离散系统的故障观测器设计方法

针对一类含扰动、传感器故障和执行器故障的非线性离散系统,提出了一种新型的故障观测器设计方法,该方法既考虑到了故障的类型,又考虑到了故障估计误差的收敛速度,故障估计方法适用性更强。首先,构造出含系统状态和传感器故障的增广向量,得到一个等价的广义系统;其次,考虑到系统中的执行器故障及故障估计的速度,构建同时含系统状态、执行器故障和传感器故障的故障观测器,并给出故障观测器存在的充分条件;最后,通过某飞机的纵向运动系统等非线性离散系统模型,对增广状态向量进行鲁棒估计,故障估计结果验证了该方法的优越性。

用于多模态非线性光学显微成像的增益管理放大全光纤飞秒激光技术

基于增益管理非线性放大的机制,求解非线性薛定谔方程和速率方程对脉冲在增益光纤中的传播过程进行仿真,研究了脉冲在增益光纤中的演化过程。结果表明,优化预啁啾量和种子光强度,可改变脉冲在增益光纤中的增益曲线。在动态增益曲线与非线性效应的共同作用下,输出光脉冲的频谱会迅速展宽数倍并带有线性啁啾,进而脉冲可被压缩至近傅里叶变换极限。为验证该超短脉冲产生方式的可行性,采用仿真结果中的最佳参数设置,成功设计并搭建了一台高能量、短脉宽、高质量的超快光纤光源。该光源可实现中心波长为1060 nm、重复频率为35 MHz、平均功率为267.4 mW以及脉冲宽度仅为39.5 fs的激光输出,与仿真结果高度一致。同现有的超短脉冲产生方式相比,本实验装置的结构更简单、造价更低、装置更紧凑、产生光束品质更佳。

Adaptive adjustment of iterative learning control gain matrix in harsh noise environment

For the robustness problem of open-loop P-type iterative learning control under the influence of measurement noise which is inevitable in actual systems, an adaptive adjustment algorithm of iterative learning nonlinear gain matrix based on error amplitude is proposed and two nonlinear gain functions are given. Then with the help of Bellman-Gronwall lemma, the robustness proof is derived. At last, an example is simulated and analyzed. The results show that when there exists measurement noise, the proposed learning law adjusts the learning gain matrix on line based on error amplitude, thus can make a compromise between learning convergence rate and convergence accuracy to some extent： the fast convergence rate is achieved with high gain in initial learning stage, the strong robustness and high convergence accuracy are achieved at the same time with small gain in the end learning stage, thus better learning results are obtained.

高超声速飞行器指定时间时变高增益反馈跟踪控制

研究了高超声速飞行器控制通道存在未知环境干扰时的指定时间跟踪控制问题.基于高超声速飞行器的输入输出线性化模型,借助参量Lyapunov方程的一些性质,设计一种光滑、有界的时变高增益控制律.相比于现有的高超声速飞行器有限/固定时间控制方法,该算法不会出现抖振现象,同时收敛时间不依赖于初始状态且可以事先设定.当高超声速飞行器存在未知的有界环境匹配干扰时,该控制器能使高度和速度在指定时间跟踪上参考信号.仿真结果验证了方法的有效性.

基于自适应机器学习的视觉机械臂控制方法

针对多输入多输出视觉机械臂系统中控制增益矩阵存在不确定性的问题,提出了一种基于自适应机器学习的控制方法。通过放宽对系统控制增益矩阵已知性的要求,设计了复合能量函数来证明系统的收敛性。通过模拟机械臂系统运动并使用未标定摄像机进行验证,证明了自适应迭代学习方法的有效性。

基于RBFNN的智能车辆转向系统的预设性能控制

针对存在模型非线性和参数不确定性的智能车辆转向系统的预设性能跟踪控制问题,采用径向基函数神经网络对转向系统中的不确定非线性进行在线逼近,结合障碍Lyapunov函数技术为智能车辆的线控转向系统设计预设性能控制器。在控制器设计中,采用动态增益技术补偿控制增益未知对系统控制性能的影响。利用Lyapunov方法分析系统的稳定性,证明在控制器作用下,前轮转角的跟踪误差在预设的时间内收敛至原点预设的邻域;通过数值仿真和整车实验验证了控制方法的合理性。