连铸方坯的二冷水控制问题

对连铸过程的二冷水控制问题进行了讨论;将二维凝固温度场的边界控制问题,用线性化方法将原问题等价地转化为在几个Ｒａｎｄｏｎ测试空间求最优测度的有限维线性优化问题,从而得到全局最优控制律。数值试验显示此方法是有效的。     

不确定广义系统的弹性H∞保性能控制

利用线性矩阵不等式(LMI)方法,研究被控对象与控制器同时存在摄动的H∞保性能控制问题.针对控制器存在加法式摄动情形,以线性矩阵不等式约束条件给出了广义系统弹性H∞保性能的充分条件,并以线性矩阵不等式的可行解给出了相应的控制器设计方法.通过求解具有线性矩阵不等式约束的凸优化问题,给出了弹性H∞最优保性能及最优H∞性能控制器的设计方法.仿真表明了方法的可行性.     

基于局部自适应逼近的半监督反馈算法

将鉴别信息引入到距离测度中,利用这个新的局部距离测度代替欧氏距离构建k-近邻,提出一种新的局部线性近邻扩展算法.将此用于图像检索的相关反馈机制,产生基于局部自适应逼近的半监督反馈算法FLANNP(feedback locally adaptive nearest neighbor propagation).该方法首先由支持向量机构建的判别函数来确定最优判别方向,基于此方向产生一个局部自适应距离算法,进而确定数据点间的权重.最后,标签信息由全局一致性假设,通过局部最近邻,从有标签数据点开始进行全局扩散标注.该方法使用有鉴别信息的距离测度,提高了图像检索的准确度.     

非线性系统近似最优PD动态补偿控制

本文研究了一类基于动态补偿的非线性系统的近似最优PD控制的问题.用微分方程的逐次逼近理论将非线性系统的最优控制问题转化为求解线性非齐次两点边值序列问题,并提供了从时域最优状态反馈到频域最优PD控制器参数的优化方法,从而获取系统最优的动态补偿网络,设计出最优PD整定参数,给出其实现算法.最后仿真示例将所提出的方法与传统的线性二次型调节器（LQR）逐次逼近方法相比较,表明该方法具有良好的动态性能和鲁棒性.     

基于矩阵测度的非线性广义时滞系统鲁棒模糊控制

研究T-S模糊广义时滞系统的鲁棒控制问题.不同于传统的寻求公共正定矩阵的方法,基于矩阵测度给出保证系统鲁棒稳定的充分条件,并将此条件进一步转化为线性矩阵不等式.通过求解线性矩阵不等式,得到状态反馈控制器和静态输出反馈控制器.最后通过算例仿真验证了方法的有效性.     

基于模糊积分和粒子群算法的多分类器融合

使用模糊积分的方法将多个分类器进行融合可以提高分类精度,但是如何得到最优的模糊测度是一个尚未解决的问题。本文根据模糊测度Sugeno积分的理想特性,用模糊测度代替各个分类器的权值,利用粒子群算法全局搜索的优势,将模糊测度对应于粒子,并随速度和位置并不断调整,从而得到全局最优的模糊测度。通过仿真实例验证了新的多分类器融合模型具有较低的分类错误率,并能有效地提高分类精度。     

有限时间信息融合线性二次型最优控制

针对有限时间线性二次型最优控制问题, 提出了一种新的求解方法—–信息融合估计方法. 基于线性最小方差估计准则下的融合估计理论, 通过融合期望状态轨迹、理想控制策略等软约束信息, 分别采用集中式融合和序贯式融合两种信息处理方法, 求得最优状态调节器问题的最优融合控制序列. 进一步从理论上论证了序贯式融合控制方法与传统最优控制方法的一致性, 并通过直流电机系统的数值仿真也验证了集中式和序贯式融合控制方法 与传统最优控制方法的等效性, 从而统一了最优估计与最优控制问题, 并为最优控制问题提供了一种新的求解方法.     

基于观测器的受扰非线性系统近似最优跟踪控制

研究一类受扰非线性系统的最优输出跟踪控制问题.给出了有限时域最优输出跟踪控制律的近似设计算法.首先将求解受扰非线性系统最优跟踪控制问题转换为求解状态向量与伴随向量耦合的非线性两点边值问题,然后利用逐次逼近方法构造序列将其转化为求解两个解耦的线性微分方程序列问题.通过迭代求解伴随向量的序列,可得到由解析的线性前馈-反馈控制部分和伴随向量的极限形式的非线性补偿部分组成的最优输出跟踪控制律.利用参考输入降维观测器和扰动降维观测器,解决了前馈控制的物理可实现问题.最后仿真结果表明了该方法的有效性.     

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.     

从逆问题指定闭环极点设计最优调节器

本文提出了矩阵主导和的概念;给出了从逆问题指定闭环极点设计最优调节器的方法,试 图从工程角度解决单输入线性定常系统最优调节器的极点配置问题.     

On a sub-optimal control scheme of linear stochastic systems with inaccurate models†

This paper presents a problem of deriving a sub-optimal control scheme of linear, discrete-time systems with inaccurate models. The sub-optimal control policy is determined as a linear combination of an estimate of the state when the expected value of a quadratic performance index is to be minimized. The estimate obtained from the sub-optimal filtering equations is modified by a hypothesis test of statistics for the innovation sequence. The numerical results of digital simulation indicate that this approach improves the system performance.     

基于特征结构配置的二阶线性系统鲁棒容错控制设计

研究基于特征结构配置的二阶线性系统鲁棒容错控制设计问题,目的是重新设计状态反馈控制律,使得故障闭环系统和正常闭环系统具有相同的特征值.两闭环系统的特征向量依最小二乘法接近,而且能通过极小化灵敏度指标提高系统的鲁棒性.基于状态反馈特征结构配置的参数化结果,将系统灵敏度指标优化问题转化为含有约束条件的优化问题,并提出了鲁棒容错控制设计方法.数值算例及其仿真结果验证了所提出设计方法的有效性.     

Observer-based robust control giving consideration to transient behaviour for linear systems with

In this paper, we present an observer-based controller design method which achieves performance robustness together with robust stability for linear multivariable systems with structured uncertainties. The performance robustness means that the deterioration of control performance is suppressed when we compare the transient behaviour for the uncertain system with a desired one generated by using the nominal system directly and at the same time excessive control input is avoided. In this approach, we adopt a similar way to the model-following technique and assume that the control law consists of a state feedback law for the nominal system and a compensation input for the purpose of preventing the error between the transient behaviour for the uncertain system and the desired one. The compensation input is additional modification term given by feedback form of an estimated error signal and is determined so that an upper bound of a quadratic cost function for the error system between the real trajectory for the plant and the desired one is minimized. We show that a condition for the existence of the compensation input which minimizes an upper bound of the quadratic cost function for the error system is given in terms of linear matrix inequalities (LMIs). Finally, numerical examples are presented.     

Output‐feedback model predictive control for stochastic systems with multiplicative and additive uncertainty

This paper studies the output‐feedback model predictive control (MPC) design problem for linear systems with multiplicative and additive random uncertainty. We first present an off‐line optimization algorithm to optimize feedback gains of the observer and the dual‐mode control policy. After that, by defining a cuboid tube whose center and boundary are both time‐varying variables, we develop a set sequence with increased freedom to contain stochastic system trajectories. A quadratic performance function with analytic upper and lower bounds is minimized such that it decreases exponentially to a finite range under the expectation. The resulting MPC algorithms are proved to guarantee practically stochastic input‐to‐state stability. A numerical example of the wind turbine model illustrates the properties of the MPC algorithms.     

Digital optimal reduced-order control of pulse-width-modulated switched linear systems

In this paper digital optimal control of pulse-width-modulated switched linear systems is considered. The equivalent discrete-time system and its deviation around a stationary solution plays an important role. An optimal linear discrete-time deviation controller is developed. The controller is chosen to be one-step-ahead predictive, which is a natural choice for control of digital pulse-modulated systems. The controller is optimal, i.e. a cost criterion is minimized. The theory is generally applicable and gives, in principle, a tool to design a digital optimal controller for an arbitrary pulse-width-modulated switched linear system. The theory is illustrated by examples from the field of switched electrical networks.     

Robust Fault Detection for Switched Linear Systems With State Delays

This correspondence deals with the problem of robust fault detection for discrete-time switched systems with state delays under an arbitrary switching signal. The fault detection filter is used as the residual generator, in which the filter parameters are dependent on the system mode. Attention is focused on designing the robust fault detection filter such that, for unknown inputs, control inputs, and model uncertainties, the estimation error between the residuals and faults is minimized. The problem of robust fault detection is converted into an H _infin-filtering problem. By a switched Lyapunov functional approach, a sufficient condition for the solvability of this problem is established in terms of linear matrix inequalities. A numerical example is provided to demonstrate the effectiveness of the proposed method.     

Bounds of the incremental gain for discrete-time recurrent neural networks

As a nonlinear system, a recurrent neural network generally has an incremental gain different from its induced norm. While most of the previous research efforts were focused on the latter, this paper presents a method to compute an effective upper bound of the former for a class of discrete-time recurrent neural networks, which is not only applied to systems with arbitrary inputs but also extended to systems with small-norm inputs. The upper bound is computed by simple optimizations subject to linear matrix inequalities (LMIs). To demonstrate the wide connections of our results to problems in control, the servomechanism is then studied, where a feedforward neural network is designed to control the output of a recurrent neural network to track a set of trajectories. This problem can be converted into the synthesis of feedforward-feedback gains such that the incremental gain of a certain system is minimized. An algorithm to perform such a synthesis is proposed and illustrated with a numerical example.     

Lower order control for systems with fast and slow modes

Given a stabilizing control for a linear system with fast and slow subsystems, a lower order control as a function of slow states alone is designed to give a first-order approximation of the original quadratic performance index, which is not necessarily minimized by the given stabilizing full-order control.     

The optimal adaptive control law for a linear plant with unknown input gains†

The optimal adaptive control law is determined for a dynamic, linear, discrete-time system with unknown input gains when regulation of the state is desired. The expectation of a quadratic cost is minimized. The solution is obtained by extending some of the results of the dual control theory so as to apply to the given problem.