基于信息融合最优估计的非线性离散系统预测控制

针对非线性离散系统的二次型最优预测控制问题, 提出了一种基于信息融合最优估计的迭代预测控制算法. 通过融合二次型性能指标函数中包含的未来参考轨迹和控制能量的软约束信息, 以及系统状态方程和输出方程的硬约束信息, 获得协状态序列和控制序列的最优估计. 通过二自由度机器人操作手的转移控制仿真, 表明了该控制算法具有良好的稳定性和鲁棒性.     

离散线性信息融合最优跟踪控制

提出一种有限时间离散线性最优跟踪控制问题的新解法--信息融合估计解法.基于信息融合估计理论,推导出协状态融合滤波方程和控制量融合估计值,由此获得最优融合控制律及二次性能指标最小值.从理论上证明了信息融合估计解法与传统解法的等同性,从信息融合的角度建立了有限时间离散线性最优跟踪控制系统,从而统一了最优控制问题和最优估计问题,电机系统的控制仿真结果验证了该解法的有效性以及与传统解法的等同性.     

基于信息融合估计的离散系统最优跟踪控制

针对期望输出和未来干扰无预见的离散线性系统最优跟踪问题,提出一种基于信息融合最优估计的控制方法.若将当前给定值和可测干扰值分别看作系统未来输出和干扰的预见值,则跟踪控制问题可转化为具有无限预见步数的预见控制问题,并将无限预见信息融合成一步等效预见信息,进而获得近似最优融合控制律.对线性直流电机系统和宏观经济系统的仿真结果均验证了该控制器在提高系统跟踪精度和抑制干扰等方面的有效性.

     

基于误差系统的信息融合最优预见跟踪控制

针对期望轨迹和干扰可预见的最优跟踪问题, 提出了一种基于误差系统的信息融合最优控制方法. 将线性伺服系统转化为误差系统; 利用信息融合估计方法, 通过融合期望轨迹、干扰输入、误差系统状态等信息, 获得误差系统协状态以及控制增量的最优估计. 构建了由积分项、状态反馈控制项和预见前馈补偿项组成的最优预见控制系统. 线性直流电机系统的控制仿真结果表明, 信息融合最优预见控制下的位置跟踪精度比传统最优预见控制下的要高.     

基于信息融合的离散系统最优预见重复控制

针对现有控制器中预见补偿器和重复控制器相对独立作用的最优跟踪问题,提出从信息融合的角度构造线性离散系统的最优预见重复控制器的设计方法.首先,在离散系统中引入L阶差分算子,将预见重复控制设计问题转化为调节稳定性问题;然后,使用协状态和信息量的概念获得控制软约束信息来描述最优融合的过程,得到控制增量信息和增广误差系统协状态的最优估计滤波;最后,将有关预见重复控制器控制律所有信息融合,得到由状态反馈、重复控制和预见补偿构成的最优预见重复控制器.数字仿真结果表明,与独立的最优预见重复控制器相比,基于信息融合的最优预见重复控制器在更少的周期内达到稳定,且在有限的步数下,更能有效利用系统的未来有效信息,从而大幅提高跟踪精度.     

时滞离散系统最优输出跟踪控制的灵敏度法

针对具有有限时间二次型性能指标的时滞离散系统,研究了最优输出跟踪控制问题.通过引入一个灵敏度参数,将原最优输出跟踪控制问题转化为不含超前项和时滞项的一族两点边值问题.得到的最优输出跟踪控制律由状态向量的线性解析函数和伴随向量级数形式的补偿项组成,其解析函数由一次性求解R iccati矩阵差分方程和矩阵差分方程得到,补偿项由求解伴随向量差分方程的递推公式得到.仿真结果表明了该方法的有效性.     

MIMO线性离散系统的最优鲁棒跟踪控制

将MIMO线性离散定常系统在指定信号输入下获取最优时域指标的最优跟踪控制器设计问题归纳为求解一个有限维线性规划问题, 并证明这种最优跟踪控制器也是鲁棒稳定控制器.     

非线性离散系统的近似最优跟踪控制

研究非线性离散系统的最优跟踪控制问题. 通过在由最优控制问题所导致的非线性两点边值问题中引入灵敏度参数, 并对它进行Maclaurin级数展开, 将原最优跟踪控制问题转化为一族非齐次线性两点边值问题. 得到的最优跟踪控制由解析的前馈反馈项和级数形式的补偿项组成. 解析的前馈反馈项可以由求解一个Riccati差分方程和一个矩阵差分方程得到. 级数补偿项可以由一个求解伴随向量的迭代算法近似求得. 以连续槽式反应器为例进行仿真验证了该方法的有效性．     

基于信息融合估计的离散线性系统预见控制

针对期望轨迹和干扰可预见的离散线性最优跟踪问题, 提出了一种基于信息融合估计的预见控制方法. 推导了最优预见控制律的融合估计过程和最优预见性能指标. 建立了包含状态反馈项、目标和干扰前馈补偿项的信息融合预见控制系统, 并分析了其渐近特性和稳态跟踪误差问题. 直流电机系统的仿真结果表明, 信息融合预见控制下的系统跟踪性能随着预见步数的增加而迅速提高并趋于稳定, 且综合考虑跟踪误差与能量消耗时要优于传统预见控制.     

Multi-sensor optimal information fusion Kalman filter

This paper presents a new multi-sensor optimal information fusion criterion weighted by matrices in the linear minimum variance sense, it is equivalent to the maximum likelihood fusion criterion under the assumption of normal distribution. Based on this optimal fusion criterion, a general multi-sensor optimal information fusion decentralized Kalman filter with a two-layer fusion structure is given for discrete time linear stochastic control systems with multiple sensors and correlated noises. The first fusion layer has a netted parallel structure to determine the cross covariance between every pair of faultless sensors at each time step. The second fusion layer is the fusion center that determines the optimal fusion matrix weights and obtains the optimal fusion filter. Comparing it with the centralized filter, the result shows that the computational burden is reduced, and the precision of the fusion filter is lower than that of the centralized filter when all sensors are faultless, but the fusion filter has fault tolerance and robustness properties when some sensors are faulty. Further, the precision of the fusion filter is higher than that of each local filter. Applying it to a radar tracking system with three sensors demonstrates its effectiveness.     

Constrained optimal estimation and control

A requirement of many multi-input multi-output control and estimation problems is a special structure of the gain matrix. This paper presents a constrained optimal method by which it is possible to constrain some elements of the gain matrix and to optimize the behaviour using the remaining parameters. Only linear constraints are considered and an algorithm based on a modified Riccati equation is obtained. The asymptotic solution for the steady state can be reduced to a sequence of classical Riccati equations by using an iterative approach. Numerical techniques have been found and computer programmes developed. Numerical examples are given.     

Direct model based visual tracking and pose estimation using mutual information

This paper deals with model-based pose estimation (or camera localization). We propose a direct approach that takes into account the image as a whole. For this, we consider a similarity measure, the mutual information. Mutual information is a measure of the quantity of information shared by two signals (or two images in our case). Exploiting this measure allows our method to deal with different image modalities (real and synthetic). Furthermore, it handles occlusions and illumination changes. Results with synthetic (benchmark) and real image sequences, with static or mobile camera, demonstrate the robustness of the method and its ability to produce stable and precise pose estimations.     

基于模糊控制信息融合方法的机器人导航系统

本文提出了一种基于模糊控制和信息融合的自主式移动机器人导航系统的实现方法。采用表格查询的模糊控制方法,对导航系统中的多传感器信息进行融合,实现了机器人的路径跟踪和自动纠偏的功能。该系统具有实现简单、响应快、鲁棒性好等特点,仿真实验和实际运行结果表明了该系统在具有引导线的环境中能有效实现自主式移动机器人的导航。     

Optimal interval estimation fusion based on sensor interval estimates with confidence degrees

The interval estimation fusion method based on sensor interval estimates and their confidence degrees is developed. When sensor estimates are independent of each other, a combination rule to merge sensor estimates and their confidence degrees is proposed. Moreover, two optimization criteria: minimizing interval length with an allowable minimum confidence degree, or maximizing confidence degree with an allowable maximum interval length are suggested. In terms of the two criteria, an optimal interval estimation fusion can be obtained based on the combined intervals and their confidence degrees. Then we can extend the results on the combined interval outputs and their confidence degrees to obtain a conditional combination rule and the corresponding optimal fault-tolerant interval estimation fusion in terms of the two criteria. It is easy to see that Marzullo's fault-tolerant interval estimation fusion [Marzullo, (1990). Tolerating failures of continuous-valued sensors. ACM Transactions on Computer System, 8(4), 284-304] is a special case of our method.     

High-voltage equipment condition monitoring and diagnosis system based on information fusion

As high-voltage electric equipment has complex structure and works in harsh environments, fiber Bragg grating (FBG) sensors are applied to realize the real-time monitoring of some parameters in which temperature is the main parameter. Using FBG sensors to monitor temperature of high-voltage electric equipment can overcome the disadvantages of harsh monitoring environment such as high-voltage, big current, strong electromagnetic interference and so on. The fault of high-voltage electric equipment is difficult to be distinguished as there may be many different reasons. The traditional or simple methods cannot totally meet the demand of fault diagnosis of high-voltage electric equipment. First, taking neural network as a classifier to distinguish different fault types from complex fault information in the feature layer can supply a good foundation to final information fusion diagnosis. Second, Dempster–Shafer evidence theory is used to make a comprehensive diagnosis of fault information in the decision layer. All the uses above can increase the speed and accuracy of diagnosis and have practical significance. The fault diagnosis system shows good results and provides an effective way to realize the real-time condition monitoring and more accurate fault diagnosis of high-voltage electric equipment.     

Adaptive suboptimal control of a linear system with bounded disturbance

An adaptive suboptimal control of a linear discrete system with unknown parameters is proposed. An additive disturbance v_t acting on the system is supposed to be uniformly bounded. The criterion is sup_vtI(y^∞₁, u^∞₁), where y_t is the output, u_t is the control. The adaptive control law gives almost the same guaranteed value of the criterion as the optimal linear feedback does for a system with known parameters.     

Primary exploration of nonlinear information fusion control theory

By introducing information fusion techniques into a control field, a new theory of information fusion control (IFC) is proposed. Based on the theory of information fusion estimation, optimal control of nonlinear discrete control system is investigated. All information on control strategy, including ideal control strategy, expected object trajectory and dynamics of system, are regarded as measuring information of control strategy. Therefore, the problem of optimal control is transferred into the one of information fusion estimation. Firstly, the nonlinear information fusion estimation theorems are described. Secondly, an algorithm of nonlinear IFC theory is detailedly deduced. Finally, the simulation results of manipulator shift control are given, which show the feasibility and effectiveness of the presented algorithm.     

An optimal control based approach to designing plantwide control system architectures

An approach to designing decentralized plantwide control system architectures is presented. The approach is based on splitting the optimal controller gain matrix that results from solving an output optimal control problem into feedback and feedforward parts. These two parts are then used to design and evaluate decentralized control systems. Results for the application of the methodology to a realistic, 4 by 4 reactor with recycle process are given. For this system, the optimal control based approach suggests feedback pairings that are significantly different than those suggested by the steady state RGA. The approach presented can give an indication if MPC is preferred over a decentralized approach to plantwide control. Comparison of the results produced by the best decentralized plantwide system and a model predictive control system are presented.