基于PF的EKF算法及应用

针对拦截弹与目标遭遇时间短,制导精度要求高的特点,提出了一种基于预测滤波(PF)的扩展卡尔曼滤波(EKF)算法.PF是在综合了模型预测控制理论和最小模型误差估计法(MME)的基础上提出的一种新的估计方法.该方法最大特点是能实时地估计出校正项(模型误差项)并加到估计模型中,使产生的新模型更能准确地描述实际系统的行为.PF的这一特点,恰恰可以解决EKF无法处理由于模型不准而导致估计精度下降的问题.因此,可以利用PF估计出的模型误差项实时地修正系统模型,令原来的系统模型更加准确,然后再利用EKF对新的、能更准确描述系统行为的模型进行滤波,最后得到更为精确的制导信息估计值.仿真结果表明,该算法与扩展卡尔曼滤波相比,增加的计算量并不明显,却在系统模型不准确的情况下,能够显著提高制导信息的估计精度,有效减少了拦截弹的脱靶量.     

基于PI自适应卡尔曼滤波的无人机姿态解算算法

针对无人机姿态解算过程中,机载加速度计噪声时变,导致姿态估计值出现较大误差的问题,提出了一种基于PI自适应卡尔曼滤波的姿态解算算法,通过监视残差方差的理论值与实际值的差值,利用PI控制算法对观测噪声协方差矩阵进行在线修正,来解决由于噪声估计不准而导致滤波发散的问题。实验结果表明,即使是在无人机的姿态发生剧烈变化时,该算法依然具有良好的精度,鲁棒性较好。     

Constrained output feedback model predictive control for nonlinear systems

A constrained output feedback model predictive control approach for nonlinear systems is presented in this paper. The state variables are observed using an unscented Kalman filter, which offers some advantages over an extended Kalman filter. A nonlinear dynamic model of the system, considered in this investigation, is developed considering all possible effective elements. The model is then adaptively linearized along the prediction horizon using a state-dependent state space representation. In order to improve the performance of the control system as many linearized models as the number of prediction horizons are obtained at each sample time. The optimum results of the previous sample time are utilized for linearization at the current sample time. Subsequently, a linear quadratic objective function with constraints is formulated using the developed governing equations of the plant. The performance and effectiveness of the proposed control approach is validated both in simulation and through real-time experimentation using a constrained highly nonlinear aerodynamic test rig, a twin rotor MIMO system (TRMS).     

High gain observer based extended generic model control with application to a reactive distillation column

This article aims at synthesizing an estimator based hybrid control scheme that consists of a high gain nonlinear observer and the extended generic model controller (EGMC) that is developed by the application of differential geometry theory. The model-based EGMC control system demands the knowledge of some physical state variables of the process and therefore, the development of a suitable algorithm to perform the state estimation has captured the attention. Here, we design a high gain observer so that it estimates a limited number of states which are solely required for the controller simulation. As a consequence, there exists a significant structural discrepancy. Despite this large mismatch, the state observer performs satisfactorily in converging the estimation error for the case of an example ethylene glycol reactive distillation system. With the same reduced-order predictor model, a comparison is also made between the high gain observer and the extended Kalman filter (EKF). Finally, the high gain observer based EGMC control structure shows promising performance in regulating the ethylene glycol column.     

Predictive-repetitive control with constraints: From design to implementation

This paper addresses the design and implementation of predictive-repetitive control systems, including the case when constraints are imposed. The approach is based on a frequency response decomposition technique that detects the dominant frequency components of the reference signals and embeds them in the predictive-repetitive controller. Analysis and design make use of the frequency response of the closed-loop system and the choice of the structure of the repetitive controller is considered as a balance between the reduction of tracking errors and minimization of the effects on performance of unwanted elements, such as model uncertainty. The implementation of operational constraints on the amplitudes of the control signals, their increments and on the outputs is considered using an on-line solution constructed using quadratic programming and the identification of active constraints. Experimental results from application to a 2 degree-of-freedom robotic system are used to illustrate the design and implementation procedures developed.     

航空发动机推力估计新方法

论文针对采用卡尔曼跟踪滤波方法进行航空发动机推力估计时,在非设计点存在稳态跟踪滤波误差的问题,提出了基于控制器跟踪滤波器的推力估计方法.在航空发动机的健康参数中选择一部分作为\"被调\"参数,对\"被调\"参数的蜕化进行估计,并修正机载发动机的实时模型,使模型输出与实际发动机可测参数的输出相一致,达到对推力等不可测性能参数进行准确估计的目的.与卡尔曼跟踪滤波估计方法相比,控制器跟踪滤波方法能利用控制器的积分特性,消除非设计点的稳态跟踪滤波误差,提高推力估计的精度.     

一种新的双环结构机器人无标定自抗扰视觉伺服控制方法

在研究基于自抗扰控制器的机器人无标定视觉伺服方法的基础上,提出了一种新的双环结构机器人无标定自抗扰视觉伺服控制方法．内环采用Kalm an滤波算法进行图像雅可比矩阵的在线辨识,可较好地逼近真实模型;外环采用自抗扰控制器,利用非线性观测器实时估计系统相对于当前估计模型的总扰动,并在控制中加以动态补偿．针对六自由度工业机器人进行了二维运动目标的跟踪实验,实验结果表明了该方法的可行性和有效性．     

基于模型误差的交互式多模型算法

本文考虑了模型与真实模式之间可能存在的差异,分析了由此引发的问题.提出一种基于模型误差的交互式多模型算法,其中模型集合使用最小距离设计方法设计.Monte-Carlo仿真实验表明,新方法比IMM能更好地避免性能恶化,并且当真实模式保持不变时,从全局角度考虑新算法比IMM优越.     

动态矩阵控制中基于Kalman滤波的开环预测方法

用稳态Kalman滤波器来解释双层结构模型预测控制中的开环预测模块,得到的结果与非基于Kalman滤波导出的开环预测对应和等价.将该结果与文献中已有的Kalman滤波解释进行了详细对比,阐述了其中的不同点.针对双层结构预测控制的整体策略进行仿真,并针对积分输出的Kalman滤波进行仿真,验证了所得结论的有效性.     

基于混合H2/H∞ 的鲁棒预测控制综合设计方法

针对具有有界扰动的多胞不确定约束系统,基于混合H2/H∞指标,提出一种鲁棒预测控制器设计方法.该控制器在保证系统对外界扰动的抑制指标前提下,具有较好的控制性能.同时,考虑了该控制器的可行性设计问题,通过附加约束保证了预测控制器的递归可行性.     

Asynchronous control of rotation and translation for a robot vehicle

Robot arms require an ‘arm controller’ to command joint motors to achieve a coordinated motion in an external Cartesian coordinate space. In the same sense, robot vehicles require a ‘vehicle controller’ to command the motors to achieve a coordinated motion specified in terms of an external Cartesian coordinate space. This paper presents the design of a general purpose vehicle controller.

The vehicle controller is designed as a three-layer structure. The top layer is an interpreter which assures a control protocol based on asynhcronous commands and independent control of orientation and forward displacement. The middle layer is a control loop which maintins an estimate of the vehicle's position and orientation, as well as their uncertainties. The control loop generates estimates and commands translation and rotation in terms of a ‘virtual vehicle’. The bottom layer is a translator between the ‘virtual vehicle’ and whatever physical vehicle on which the controller is implemented.     

基于踝关节处人机位姿误差的外骨骼摆动腿控制

提出了一种外骨骼整机运动控制策略,并重点介绍了外骨骼摆动腿的运动规划与PD(比例-微分)控制.作为外骨骼摆动腿运动规划的依据,踝关节处人机位姿误差检测仅需要在腰部与踝足部进行人机绑定,降低了对穿戴者运动的限制与控制系统的复杂性.通过Adams与Matlab验证了作为外骨骼摆动腿PD控制器设计基础的外骨骼动力学模型的正确性.引入卡尔曼滤波器预测穿戴者摆动腿的运动可以在有限的采样频率下改善控制精度.实验结果表明,该方法能有效识别穿戴者摆动腿的运动意图并驱动外骨骼髋、膝关节实现人机踝关节轨迹的跟踪,可用于下肢外骨骼摆动腿的控制.