连续切换LPV系统的鲁棒H∞滤波

探究连续切换线性参数变化(LPV)系统的鲁棒H∞滤波问题.对于整个参数变化空间,传统方法是设计单一LPV滤波器,具有较大的保守性.为此,利用多参数依赖Lyapunov函数设计了切换LPV系统的多参数依赖鲁棒H∞滤波器,以降低设计的保守性.考虑了平均驻留时间切换逻辑,所设计的鲁棒H∞滤波器能够确保滤波误差系统指数稳定且具有一定的H∞扰动抑制水平.数值仿真实例验证了所提出方法的有效性.     

无人机侧向系统LPV 模型降阶

无人机线性参变(LPV)模型能准确描述其非线性动态特性,但初始建立的LPV模型阶数较高,控制过程计算量较大.为此,提出一种基于平衡截断的LPV模型降阶方法.首先给出LPV系统的适定性、稳定性和平衡实现的定义;然后,提出LPV模型的平衡截断降阶方法.针对无人机侧向系统LTI模型,通过多项式拟合来建立LPV模型,并实现模型降阶.仿真结果表明,降阶模型的阶跃响应满足输出响应的精度要求.     

基于LPV的航空发动机压气机健康监测系统建模研究

针对航空发动机健康监测系统对模型的高实时性和准确性要求,提出了一种应用递归最小二乘线性拟合建立压气机非线性过程中的线性变参数(LPV)模型的建模方法。该建模方法通过对模型参数的选择和顶点的选取,结合递归最小二乘法在不同顶点处拟合线性状态空间模型,根据LPV模型凸集特点得到非线性模型的LPV模型表达。最后应用法国ALSTOM公司提供的燃气涡轮模型进行了仿真实验验证,实现了实时非线性过程中模型稳定区域小于1%的动态建模误差,证明了方法的有效性和精确性。     

线性参数变化系统建模与控制研究进展

在描述实际系统的非线性和时变特性方面, 线性参数变化(Linear parameter varying, LPV)模型有着巨大的优越性, 对于使用一些成熟的线性系统控制理论来解决非线性系统的控制问题, 提供了良好的手段.文章对LPV系统的模型结构和建模方法, 模型参数辨识方法, 控制方法以及应用领域等方面的近几年的研究成果, 做了比较全面的总结和概括, 最后对LPV系统建模和控制的未来研究方向进行了展望.     

风力机的线性变参数主动容错控制

针对风力机具有非线性和参数的不确定性的特征,提出了基于线性变参数(linear parameter varying,LPV)增益调度的风力机主动容错控制方法,降低故障对机组动态特性的影响.基于LPV凸分解方法,将风力机的非线性模型转化为具有凸多面体结构LPV模型,利用线性矩阵不等式(linear matrix inequalities,LMIs)技术对凸多面体各个顶点分别设计满足性能要求的控制器,再利用各顶点设计的反馈控制器得到具有凸多面体结构LPV容错控制器.仿真结果表明,LPV增益调度技术可以成功地应用于风力机系统的容错控制.     

超空泡航行体H2/H∞状态反馈鲁棒控制

针对超空泡航行体纵平面运动时受到的强非线性滑行力以及存在外界干扰的问题,提出了一种H2/H∞状态反馈鲁棒控制方法.首先,利用线性变参数(linear parameter variyng,LPV)的方法将航行体模型转换成LPV镇定模型;其次,将LPV镇定模型转换成LPV跟踪模型,在考虑外界干扰的情况下,设计了H2/H∞状态反馈鲁棒控制器;最后进行仿真分析,结果表明,所设计的控制器能够使系统快速、准确地跟踪期望值,同时对外界干扰具有良好的抑制作用.     

风能转换系统的LPV主动容错控制

随着对风力机效率和可靠性要求的提高,现代风力机不再如传统风力机那样一味追求高的产能。本文针对桨距执行器故障的风能转换系统具有非线性性和参数严重不确定性,提出了基于LPV增益调度的风能转换系统的主动容错控制方法,降低故障对机组动态特性的影响。基于LPV凸分解方法,将风能转化系统非线性模型化为具有凸多面体结构LPV模型,利用LMI技术对凸多面体各个顶点分别设计满足性能要求的控制器,再利用各顶点设计的反馈控制器得到具有凸多面体结构LPV容错控制器。仿真结果表明,LPV增益调度技术可以成功地应用于风能转换系统的容错控制,在有故障的情况下,仍能保持系统的稳定和良好的动态性能。     

基于LM I方法的机器人L PV 鲁棒H ∞控制器设计

对平面两关节直接驱动机器人,提出一种同时将闭环极点配置到满足动态响应区域内的变增益LPV鲁棒H∞控制器设计新方法,利用LPV的凸分解方法,将机器人模型化为具有凸多面体结构的LPV模型,然后利用LMI技术对凸多面体各顶点分别设计满足H∞性能和闭环极点配置的反馈增益,再利用各顶点设计的反馈控制器综合得到具有凸多面体结构的LPV控制器,仿真结果验证了该控制器可使机器人随关节位置变化始终具有良好的控制性能。     

约束非线性系统切换鲁棒预测控制

采用“分段蕴含”(PWE)方法, 用一组线性变参数模型(LPV)近似约束非线性系统, 降低模型近似的保守性. 对每个LPV模型引入参数Lyapunov函数, 得到稳定的控制律, 并施加于非线性系统. 当检测到LPV模型发生切换时, 根据可行域的离线设计方法确定适当的切换律, 使系统按照设定的规则切换, 保证切换后的初始状态可行. 在文章最后给出了基于切换策略的控制算法的可行性和稳定性. 与传统非线性预测控制相比, 基于切换策略的鲁棒预测 控制方法保守性更低, 计算量更小.     

LPV鲁棒故障观测器设计

针对线性参数变化（Linear Parameter Varying,LPV）系统的故障检测问题,采用H_- / H_∞混合优化方法,对基于LPV模型的鲁棒故障观测器（RFDO）进行设计,基于离散的参数依赖李亚普诺夫函数,得到了系统的LPV鲁棒故障观测器的综合条件,经过转化,观测器的设计问题被转化为一组线性矩阵不等式的求解问题;利用LMI工具求解线性矩阵不等式,得到了系统的LPV鲁棒故障观测器。最后,通过在一点上对故障输入的非线性仿真,验证了该方法的有效性。     

LPV model development and control of a solution copolymerization reactor

In this paper, linear parameter-varying (LPV) control is considered for a solution copolymerization reactor, which takes into account the time-varying nature of the parameters of the process. The nonlinear model of the process is first converted to an exact LPV model representation in the state-space form that has a large number of scheduling variables and hence is not appropriate for control design purposes due to the complexity of the LPV control synthesis problem. To reduce such complexity, two approaches are proposed in this paper. First, an approximate LPV representation with only one scheduling variable is obtained by means of a parameter set mapping (PSM). The second approach is based on reformulating the nonlinear model so that it provides an LPV model with a fewer number of scheduling parameters but preserves the same input–output behavior. Moreover, in the implementation of the LPV controllers synthesized with the derived models, the unmeasurable scheduling variables are estimated by an extended Kalman filter. Simulation results using the nonlinear model of the copolymerization reactor are provided in order to illustrate the performance of the proposed controllers in reducing the convergence time and the control effort.     

Applying the extended Kalman filter to systems described by nonlinear differential-algebraic equations

This paper describes a method for the state estimation of nonlinear systems described by a class of differential-algebraic equation models using the extended Kalman filter. The method involves the use of a time-varying linearisation of a semi-explicit index one differential-algebraic equation. The estimation technique consists of a simplified extended Kalman filter that is integrated with the differential-algebraic equation model. The paper describes a simulation study using a model of a batch chemical reactor. It also reports a study based on experimental data obtained from a mixing process, where the model of the system is solved using the sequential modular method and the estimation involves a bank of extended Kalman filters.     

非线性动态滤波的迭代算法

阐述了标称状态的线性化方法和扩展的卡尔曼滤波公式及迭代卡尔曼滤波，探讨了非线性动态滤波的近似处理方法，围绕标称状态将非线性模型进行线性化，将标准的卡尔曼滤波扩展到非线性模型，得到扩展的卡尔曼滤波公式，研究了迭代滤波计算方法。扩展的卡尔曼滤波方法已经有效地用于非线性模型。     

基于非线性卡尔曼平滑的GPS定位估计方法

在GPS单机定位中,通常采用卡尔曼滤波作为位置状态解算的方法.文中提出一种将非线性平滑技术用于GPS定位估计的方法,该方法可用于单机GPS接收机的定位解算,在非线性滤波的基础上进一步提高定位精度.提出一种随接收卫星数量而实时改变测量参数的动态测量模型,根据GPS的伪距、多普勒频移和导航信息等原始数据进行定位模型的解析,运用新型的平淡卡尔曼平滑算法求解该动态模型.GPS定位实验结果表明,与通用的最小二乘迭代法和非线性滤波等方法获得的结果相比,所提出的方法能获得更高的定位精度.     

Unscented Kalman filter for continuous‐time nonlinear fractional‐order systems with process and measurement noises

This study proposes the design of unscented Kalman filter for a continuous‐time nonlinear fractional‐order system involving the process noise and the measurement noise. The nonlinear fractional‐order system is discretized to get the difference equation. According to the unscented transformation, the design method of unscented Kalman filter for a continuous‐time nonlinear fractional‐order system is provided. Compared with the extended Kalman filter, the proposed method can obtain a more accurate estimation effect. For fractional‐order systems containing non‐differentiable nonlinear functions, the method proposed in this paper is still effective. The unknown parameters are also discussed by the augmented vector method to achieve the state estimation and parameter identification. Finally, two examples are offered to verify the effectiveness of the proposed unscented Kalman filter for nonlinear fractional‐order systems.     

Exact finite-dimensional nonlinear filters

A new nonlinear filter is derived for continuous-time processes with discrete-time measurements. The filter is exact, and it can be implemented in real time with a computational complexity that is comparable to the Kalman filter. This new filter includes both the Kalman filter and the discrete-time version of the Benes filter as special cases. Moreover, the new theory can handle a large class of nonlinear estimation problems that cannot be solved using the Kalman or discrete-time Benes filters. A simple approximation technique is suggested for practical applications in which the dynamics do not satisfy the required conditions exactly. This approximation is analogous to the so-called "extended Kalman filter" [10], and it represents a generalization of the standard linearization method.     

Actuator fault detection and performance recovery with Kalman filter-based adaptive observer

A novel Kalman filter-based adaptive observer for the sampled-data nonlinear time-varying system is proposed in this paper. With the high gain property of Kalman filter, it is applicable to a large variation of unknown parameters, which can be estimated optimally. Then a method of actuator fault detection is proposed. With the estimated faults, one can use the proposed input compensation method to solve actuator faults. Additionally, the optimal linearization technique is used to obtain the locally optimal linear model for a nonlinear system at each sampled state, so that the actuator fault detection and performance recovery of a sampled-data nonlinear time-varying system is accomplished. In this paper, we also introduce a prediction-based digital redesign method to develop the corresponding sampled-data controller.     

Robust Ensemble Kalman Filter Based on Exponential Cost Function

This paper provides an alternative point of view to the robust estimation technique for nonlinear non Gaussian systems based on exponential quadratic cost function. The proposed method, named the risk sensitive ensemble Kalman filter (RSEnKF), is based on the ensemble Kalman filter (EnKF) which may be thought of as a Monte Carlo implementation of the Kalman filter for nonlinear estimation problems. The theory and formulation of the RSEnKF are presented in this paper. The proposed method is superior to the extended risk sensitive filter (ERSF) and the quadrature based risk sensitive filters in terms of estimation accuracy, and is faster than the risk sensitive particle filter (RSPF).     

基于不确定模型误差系统的变分贝叶斯STCKF

强跟踪容积卡尔曼滤波器在对含有模型误差和时变噪声的非线性系统进行滤波时, 容易出现性能降低甚至发散. 鉴于此, 提出一种基于变分贝叶斯的强跟踪容积卡尔曼滤波算法. 该算法运用虚拟噪声法补偿模型误差, 假设虚拟噪声均值非零, 且满足高斯分布, 虚拟噪声方差服从逆gamma分布, 在强跟踪容积卡尔曼滤波器估计状态的同时, 采用变分贝叶斯推理估计虚拟噪声参数. 仿真结果表明, 所提出算法对含模型误差与时变噪声的非线性系统具有较好的估计精度, 相比于自适应算法具有更强的鲁棒性.