ROBUST GLOBAL EXPONENTIAL STABILITY OF UNCERTAIN IMPULSIVE SYSTEMS

By using the quasi-Lyapunov function, some sufficient conditions of global exponential stability for impulsive systems are established, which is the basis for the following discussion. Then, by employing Riccati inequality and Hamilton-Jacobi inequality approach, some sufficient conditions of robust exponential stability for uncertain linear/nonlinear impulsive systems are derived, respectively. Finally, some examples are given to illustrate the applications of the theory.

Robust Global Stabilization of Spacecraft Rendezvous System via Gain Scheduling

The problem of robust global stabilization of a spacecraft circular orbit rendezvous system with input saturation and inputadditive uncertainties is studied in this paper. The relative models with saturation nonlinearity are established based on ClohesseyWiltshire equation. Considering the advantages of the recently developed parametric Lyapunov equation-based low gain feedback design method and an existing high gain scheduling technique, a new robust gain scheduling controller is proposed to solve the robust global stabilization problem. To apply the proposed gain scheduling approaches, only a scalar nonlinear equation is required to be solved.Different from the controller design, simulations have been carried out directly on the nonlinear model of the spacecraft rendezvous operation instead of a linearized one. The effectiveness of the proposed approach is shown.

A new result on global exponential robust stability of neural networks with time-varying delays

In this paper, the global exponential robust stability of neural networks with ume-varying delays is investigated. By using nonnegative matrix theory and the Halanay inequality, a new sufficient condition for global exponential robust stability is presented. It is shown that the obtained result is different from or improves some existing ones reported in the literatures. Finally, some numerical examples and a simulation are given to show the effectiveness of the obtained result.

Globally robust nonlinear PID controllers for robot manipulators with an uncertain Jacobian matrix

Based on a continuous piecewise-differentiable increasing functions vector, a class of robust nonlinear PID (RN-PID) controllers is proposed for setpoint control with uncertain Jacobian matrix. Globally asymptotic stability is guaranteed and only position and joint velocity measurements are required. And stability problem arising from integral action and integrator windup, are consequently resolved. Furthermore, RN-PID controllers can be of effective alternative for anti-integrator-wind-up, the control performance would not be very bad in the presence of rough parameter tuning.

HAC-Robust Measurement of the Duration of a Trendless Subsample in a Global Climate Time Series

The IPCC has drawn attention to an apparent leveling-off of globally-averaged temperatures over the past 15 years or so. Measuring the duration of the hiatus has implications for determining if the underlying trend has changed, and for evaluating climate models. Here, I propose a method for estimating the duration of the hiatus that is robust to unknown forms of heteroskedasticity and autocorrelation (HAC) in the temperature series and to cherry-picking of endpoints. For the specific case of global average temperatures I also add the requirement of spatial consistency between hemispheres. The method makes use of the Vogelsang-Franses (2005) HAC-robust trend variance estimator which is valid as long as the underlying series is trend stationary, which is the case for the data used herein. Application of the method shows that there is now a trendless interval of 19 years duration at the end of the HadCRUT4 surface temperature series, and of 16 - 26 years in the lower troposphere. Use of a simple AR1 trend model suggests a shorter hiatus of 14 - 20 years but is likely unreliable.

噪声图像中圆心和半径的Robust估计算法

本文提出了一种在噪声图像中估计圆心和半径的Robust算法,易于处理包括Gaussian噪声和出格点的污染图像。

Robust Video Stabilization Based on Motion Vectors

This paper proposes a new robust video stabilization algorithm to remove unwanted vibrations in video sequences. A complete theoretical analysis is first established for video stabilization, providing a basis for new stabilization algorithm. Secondly, a new robust global motion estimation (GME) algorithm is proposed. Different from classic methods, the GME algorithm is based on spatial-temporal filtered motion vectors computed by block-matching methods. In addition, effective schemes are employed in correction phase to prevent boundary artifacts and error accumulation. Experiments show that the proposed algorithm has satisfactory stabilization effects while maintaining good tradeoff between speed and precision.

Multi-parameter Sensitivity Analysis and Application Research in the Robust Optimization Design for Complex Nonlinear System

The current research of complex nonlinear system robust optimization mainly focuses on the features of design parameters, such as probability density functions, boundary conditions, etc. After parameters study, high-dimensional curve or robust control design is used to find an accurate robust solution. However, there may exist complex interaction between parameters and practical engineering system. With the increase of the number of parameters, it is getting hard to determine high-dimensional curves and robust control methods, thus it＇s difficult to get the robust design solutions. In this paper, a method of global sensitivity analysis based on divided variables in groups is proposed. By making relevant variables in one group and keeping each other independent among sets of variables, global sensitivity analysis is conducted in grouped variables and the importance of parameters is evaluated by calculating the contribution value of each parameter to the total variance of system response. By ranking the importance of input parameters, relatively important parameters are chosen to conduct robust design analysis of the system. By applying this method to the robust optimization design of a real complex nonlinear system-a vehicle occupant restraint system with multi-parameter, good solution is gained and the response variance of the objective function is reduced to 0.01, which indicates that the robustness of the occupant restraint system is improved in a great degree and the method is effective and valuable for the robust design of complex nonlinear system. This research proposes a new method which can be used to obtain solutions for complex nonlinear system robust design.

Robust fuzzy control of Takagi-Sugeno fuzzy neural networks with discontinuous activation functions and time delays

The problem of global robust asymptotical stability for a class of Takagi-Sugeno fuzzy neural networks（TSFNN） with discontinuous activation functions and time delays is investigated by using Lyapunov stability theory.Based on linear matrix inequalities（LMIs）,we originally propose robust fuzzy control to guarantee the global robust asymptotical stability of TSFNNs.Compared with the existing literature,this paper removes the assumptions on the neuron activations such as Lipschitz conditions,bounded,monotonic increasing property or the right-limit value is bigger than the left one at the discontinuous point.Thus,the results are more general and wider.Finally,two numerical examples are given to show the effectiveness of the proposed stability results.

Robust Adaptive Control of Nonholonomic Systems with Nonlinear Parameterization

全球适应的州的反馈控制策略与强壮的非线性的飘移和未知非线性的参数在锁住的形式为 nonholonomic 系统的一个类被介绍。一种参数分离技术被介绍把非线性的 parameterization nonholonomic 系统转变成一个象线性一样 parameterized nonholonomic 系统。然后，反馈支配设计被使用设计一个全球适应稳定控制器，切换的策略被开发消除 uncontrollability 的现象。建议控制器能保证那得到状态全球性集成到起源的所有系统，当另外的信号仍然保持围住时。模拟例子表明有效性和建议控制器的柔韧的特征。

Robust adaptive control for uncertain systems with discrete and distributed delays

In this paper, a robust adaptive control scheme is proposed for the stabilization of uncertain linear systems with discrete and distributed delays and bounded perturbations. The uncertainty is assumed to be an unknown continuous function with norm-bounded restriction. The perturbation is sector-bounded. Combining with the liner matrix inequality method, neural networks and adaptive control, the control scheme ensures the exponential stability of the closed-loop system for any admissible uncertainty.

A Novel Approach Based on Reinforcement Learning for Finding Global Optimum

A novel approach to optimizing any given mathematical function, called the MOdified REinforcement Learning Algorithm (MORELA), is proposed. Although Reinforcement Learning (RL) is primarily developed for solving Markov decision problems, it can be used with some improvements to optimize mathematical functions. At the core of MORELA, a sub-environment is generated around the best solution found in the feasible solution space and compared with the original environment. Thus, MORELA makes it possible to discover global optimum for a mathematical function because it is sought around the best solution achieved in the previous learning episode using the sub-environment. The performance of MORELA has been tested with the results obtained from other optimization methods described in the literature. Results exposed that MORELA improved the performance of RL and performed better than many of the optimization methods to which it was compared in terms of the robustness measures adopted.

四旋翼无人机鲁棒嵌套饱和控制

针对四旋翼无人机在执行任务中,动力系统受到输入饱和限制,传统嵌套饱和控制算法收敛条件严格、收敛速度慢、稳态精度不高、鲁棒性不强等问题,提出了一种四旋翼无人机鲁棒嵌套饱和控制算法。首先,设计了条件相对放松的嵌套饱和控制算法,降低了设计难度,并且给出了闭环系统的稳定性证明。其次,基于条件放松的嵌套饱和控制算法,引入自耦PD和补偿函数观测器,提升系统的响应速度,稳态精度以及鲁棒性,并利用Lyapunov稳定性原理证明了闭环系统的全局稳定性。最后,将所提算法与传统嵌套饱和控制算法和PD控制算法进行了仿真对比,结果表明:本文提出的算法在暂态性能、稳态性能和鲁棒性上都是最优。

四旋翼飞行器的RBF神经网络鲁棒自适应控制

针对具有模型不确定性和有界外部扰动的四旋翼飞行器,提出了一种基于径向基函数神经网络的鲁棒自适应全局控制方法(RRAC)。所提方法结合了神经网络控制对未知非线性的强拟合能力和鲁棒控制的全局稳定性,解决了神经网络控制仅能实现半全局一致最终有界的问题,实现了控制精度和鲁棒性的双重提升。所设计的控制器由在近似域内工作的神经网络控制器和在近似域外工作的鲁棒控制器组成。引入一种新型切换函数来实现两者之间的平滑切换,以保证闭环系统的所有信号是全局一致最终有界的。利用Lyapunov函数和Barbalat引理严格证明了非线性四旋翼飞行器系统的稳定性。仿真表明,所设计的控制器在模型不确定性和有界外部扰动下对参考轨迹依旧保持良好的跟踪性能,且跟踪误差趋近于零。

不确定非线性系统的鲁棒自适应控制

针对一类具有一般不确定性的非线性系统（包括非线性不确定性、有界干扰、时变参数等），利用递推设计方法，设计了一种新的用于输出调节的鲁棒自适应控制器．该控制器对非线性不确定性和有界干扰具有鲁棒性，并能保证闭环系统信号的全局有界性．其优点是对不确定性的知识要求较少．仿真实例表明，设计的鲁棒自适应控制器具有满意的控制效果．

单变量时滞系统优化控制

全面阐述了最新建立的单变量时滞优化控制理论,包含时滞二次优化控制基本原理、鲁棒性、背驰定律以及时滞系统总体满意控制理论,并给出了若干算例。结果表明,应用这些理论,不仅能设计出对控制信号而言具有快速平稳优良性能指标的系统,而且该系统对参数扰动和负荷扰动都有强壮的抗干扰能力,特别适用于不确定的时滞系统的优化设计。

全球航运网络鲁棒性和脆弱性研究

通过对全球航运网络节点度分布、度值相关性和节点度与介数相关性分析发现,度值大的节点优先与度值较大节点连接,度值大的节点介数也较大,因而度值较大的节点对网络影响程度较大.选择度值较大节点为优先攻击目标进行选择删除,随机生成节点序列作为出错目标进行随机删除,进而研究在出错和攻击情况下,全球航运网络聚集系数、平均距离、孤立节点比率和网络效率的变化情况.通过分析发现全球航运网络具有较好的鲁棒性,但网络较脆弱.

带有摩擦的机器人鲁棒控制

根据机械系统中摩擦力的特性,把有关标量情况下摩擦力的结论推广到矢量空间中,使机器人系统中的外部随机干扰控制问题具体化。首先利用反馈控制技术,把基于拉格朗日方程的机器人动力学模型转化成一个线性状态方程,并详细介绍这一转化过程。然后基于此线性状态方程,应用李雅普诺夫函数稳定性理论,针对摩擦力的一般特性,设计鲁棒补偿控制器来抑制摩擦力对机器人控制系统的影响,以使机器人实际运动轨迹能够全局渐近收敛于所给定的期望轨迹。所设计的控制器具有理论清晰、设计简单、鲁棒性好等优点。给予完整的理论证明,最后以两关节机器人为例,给出仿真试验结果。通过对比采用不同控制器所得出的不同仿真结果,可以看出所提出的鲁棒控制器具有较好的实用效果。

一类具有有界扰动的非线性系统的输出反馈自适应控制

该文考虑一类具有有界干扰和未知参数的非线性系统（１），设计出一种用于输出跟踪的输出反馈鲁棒自适应控制器．该控制器能保证闭环系统的全局稳定性，并且解决了ε－跟踪问题．仿真结果表明，所设计的自适应控制器具有良好的跟踪性能．

不确定性机器人的鲁棒控制

本文研究不确定性刚性机器人的鲁棒控制问题。提出了一种新的鲁棒控制方案。控制器由两部分组成：第一部分为基于标称模型设计的计算力矩控制器；第二部分为基于Ｌｙａｐｕｎｏｖ方法设计的鲁棒补偿控制器，其作用是消除不确定性对跟踪性能的影响．本文证明了闭环系统的全局收敛性，仿真结果表明本方法对于存在外扰和模型不确定性的机器人系统是十分有效的。