基于微分方程对称的分布参数系统稳态控制

应用对称群理论中经典对称, 以无穷小生成元为分析工具, 考虑分布参数系统的控制问题已有研究, 在此基础上, 本文给出利用微分方程对称实现分布参数系统稳态控制的方法. 通过求解微分方程的对称, 借助其和无穷小生成元之间的关系, 研究给出符合控制目标稳态要求的分布参数系统边界控制条件. 针对两个例子,说明了利用微分方程对称实现分布参数系统稳态控制的过程, 设计了边界控制条件, 进行了仿真说明. 相较基于经典对称获得分布参数系统无穷小生成元的过程, 利用微分方程对称, 避免了空间延拓过程, 并可能获得与其不同的无穷小生成元形式.     

采用自适应控制的电—气比例／伺服控制系统的研究

电-气比例/伺服控制系统的特性受到运行参数和负载干扰的较大影响,本文研究了该系统的自适应控制方法.根据电-气比例/伺服控制系统的技术特点,作者提出了采用模型参考自适应控制的策略,并解决了控制器设计、实现中的一系列技术问题.实验结果表明,本文的研究取得了显著的效果.     

永磁同步电动机的自适应神经网络动态面控制

研究永磁同步电动机的位置跟踪控制问题.针对参数不确定的永磁同步电动机系统,提出自适应神经网络动态面位置跟踪控制方法.根据Stone Weierstrass逼近定理,利用神经网络逼近电动机系统中的复杂非线性函数.采用动态面技术的自适应反步方法设计电动机的位置跟踪控制器实现电动机的位置跟踪控制.提出的控制策略不仅能够克服电机参数的不确定性和负载扰动,而且避免了传统反步设计方法存在的“复杂性爆炸”问题.根据Lyapunov稳定性理论,证明闭环系统具有半全局稳定性,位置跟踪误差收敛于原点的小邻域内.仿真结果表明了所提控制方法能够使电动机快速、准确地跟踪给定的位置信号;神经网络能够很好地逼近系统中的复杂非线性函数.     

未知参数非线性系统的自适应广义预测控制

研究非线性系统的稳定性和跟踪优化问题,针对未知参数非线性系统的参数辨识和输出跟踪问题,给出参数自适应广义预测控制方法,为使辨识模型能实时反映被控对象特性以及输出对设定值的跟踪有较高精度.提出将非线性系统转化为受控自回归滑动平均模型,根据输入输出数据辨识模型参数.采用广义预测控制滚动优化的策略得出最优控制律,将最优控制律作用于对象实现非线性系统的优化控制以及系统输出对设定值的跟踪控制.明显克服了自适应控制对模型精度要求高的缺陷且具有在线辨识,滚动优化的特点.最后,通过仿真实例验证了方法的有效性.     

控制方向未知的全状态约束非线性系统的鲁棒自适应跟踪控制

针对一类控制方向未知的含有时变不确定参数和未知时变有界扰动的全状态约束非线性系统,本文提出了一种基于障碍Lyapunov函数的反步自适应控制方法.障碍Lyapunov函数保证了系统状态在运行过程中始终保持在约束区间内;Nussbaum型函数的引入解决了系统控制方向未知的问题;光滑投影算法确保了不确定时变参数的有界性.障碍Lyapunov函数、Nussbaum型函数及光滑投影算法与反步自适应方法的有效结合首次解决了控制方向未知的全状态约束非线性系统的跟踪控制问题.所设计的自适应鲁棒控制器能在满足状态约束的前提下确保闭环系统的所有信号有界.通过恰当地选取设计参数,系统的跟踪误差将收敛于0的任意小的邻域内.仿真结果表明了控制方案的可行性.     

Duffing系统的同步化控制研究

本文研究含有未知参数的Duffing系统的同步化控制问题．首先，研究了系统参数已知情况下的同步化控制问题．其次，考虑了系统参数未知情况下的同步化控制问题；由于直接处理该问题比较困难，我们在控制法则中引入了可变参数来代替未知参数，该参数可由完善的自适应法则获得．再次，通过理论证明给出了控制目标，该控制目标可由提出的控制法则来实现．最后，用一些仿真实例证实了该方法的有效性．     

失配参数在线矫正的永磁同步电机预测电流控制

针对永磁同步电机预测电流控制模型参数失配引起的系统性能下降问题,提出一种基于内模控制观测器的应对策略来矫正模型参数.首先,根据旋转坐标系下的永磁同步电机动态模型,设计了d, q轴电流内模控制观测器并进行稳定性推导证明,观测器可以无静差估计d, q轴电流变化率,进而在线估计电机参数;然后,由卡尔曼滤波减弱参数噪声得到最优参数估计,阈值化处理最优参数估计后在线更新失配参数.最后,在稳态和调速阶段两种不同工况中,将所提策略应用于参数失配的永磁同步电机三矢量预测电流控制;实验结果表明,与同类方法相比,所提策略能在线矫正失配参数,在改善电流波动系数及总谐波畸变率方面表现更好.     

再入飞行器带有干扰观测器的有限时间控制

针对模型参数不确定及外界干扰影响下的再入飞行器的姿态控制问题,设计基于干扰观测器的有限时间控制策略.首先建立面向控制模型,并通过多时间尺度原理将面向控制模型分为内、外两环;其次,设计干扰观测器实时观测面向控制模型中的参数不确定及外界干扰,解决滑模控制因参数过大而导致的抖振问题,基于观测值,设计终端滑模控制器,在此基础上,基于Lyapunov理论对控制系统的稳定性进行分析;最后,基于六自由度再入模型,验证所设计的有限时间姿态控制策略的有效性.     

基于Backstepping的临近空间飞行器非线性控制

为解决临近空间飞行器的稳定跟踪控制问题,考虑模型参数不确定和外界未知干扰对跟踪控制性能的影响,基于backstepping方法与非线性控制理论设计稳定跟踪控制算法.将通用临近空间飞行器数学模型转化为严反馈形式,采用backstepping方法递归求取控制信号,并利用非线性方法对控制器进行设计,从而保证了系统的稳定性和鲁棒性.仿真结果表明,给出的控制方法能有效抑制模型参数不确定和外界干扰的影响,对于实现临近空间飞行器的稳定跟踪控制具有较好的应用价值.     

具有时变不确定线性系统的鲁棒无源控制

研究具有时变不确定参数的线性系统在有界能量外部输入作用下的鲁棒无源控制问题. 目的是设计一个反馈控制器使得闭环系统是二次稳定,同时具有严格无源性.研究结果证明,这 一问题可以转化为一个等价的具有某个参数的线性时不变系统的正实控制问题,从而可用现有的 正实控制问题的求解方法解决所研究的问题.     

On design of time-varying feedback control for a class of second-order nonlinear systems

This paper solves the tracking problem by designing the time-varying control for a class of nonlinear pendulum system. First, by analyzing the pendulum dynamics, we get a second-order nonlinear strict-feedback system, and the tracking problem is converted into a stabilizing problem of this nonlinear system. Then, we consider a candidate output feedback control with time-varying parameters, and analyze the system dynamics to get the stabilizing condition through utilizing the backstepping approach. Although this condition is nonlinear, which is not easy to be solved, under some special conditions, the parameters can be calculated. Finally, the simulation on the tracking problem for the pendulum system is presented, which verifies our results.     

基于线性矩阵不等式的不确定关联系统的分散鲁棒镇定

应用线性矩阵不等式（LMI）方法研究不确定性关联大系统的分散便棒镇定问题。系统中不稳定项具有数值界,可不满足匹配条件。基于不确定项的表达形式,给出了其可分散状态反馈镇定的充分条件,即一组LMIs有解。在此基础上,通过求第一凸优化问题,提出了具有较小反馈增益的分散稳定化状态反馈控制律的设计方法。仿真示例说明了该方法的有效性和优越性。     

一类不确定非完整动力学系统的鲁棒镇定及其在移动机器人中的应用

本文讨论了一类带有未知惯性参数、动静态摩擦及外界干扰的非完整动力学系统的控制问题．基于变结构控制的思想，给出了该系统的镇定方法，使得Ｐｏｍｅｔ［３］关于非完整运动学系统的时变光滑镇定律可推广到相应的带有参数不确定和干扰的动力学系统上．最后，将所得结果应用于一类移动机器人的镇定控制，仿真结果验证了该方法的有效性．     

Infinite horizon optimal control of linear discrete time systems with stochastic parameters

The infinite horizon optimal control problem is considered in the general case of linear discrete time systems and quadratic criteria, both with stochastic parameters which are independent with respect to time. A stronger stabilizability property and a weaker observability property than usual for deterministic systems are introduced. It is shown that the infinite horizon problem has a solution if the system has the first property. If in addition the problem has the second property the solution is unique and the control system is stable in the mean square sense. A simple necessary and sufficient condition, explicit in the system matrices, is given for the system to have the stronger stabilizability property. This condition also holds for deterministic systems to be stabilizable in the usual sense. The stronger stabilizability and weaker observability properties coincide with the usual ones if the parameters are deterministic.     

Adaptive containment control of nonlinear multi-agent systems with non-identical agents

This paper addresses the containment control problem for a group of non-identical agents, where the dynamics of agents are supposed to be nonlinear with unknown parameters and parameterised by some functions. In controller design approach for each follower, adaptive control and Lyapunov theory are utilised as the main control strategies to guarantee the convergence of all non-identical followers to the dynamic convex hull formed by the leaders. The design of distributed adaptive controllers is based on the exchange of neighbourhood errors among the agents. For analysis of containment control problem, a new formulation has been developed using M-matrices. The validity of theoretical results are demonstrated through an example.     

基于模糊RBF神经网络的PID及其应用

针对传统的PID控制器参数固定而导致在控制中效果差的问题,提出一种基于模糊RBF神经网络智能PID控制器的设计方法。该方法结合了模糊控制的推理能力强与神经网络学习能力强的特点,将模糊控制与RBF神经网络相结合以在线调整PID控制器参数,整定出一组适合于控制对象的kp, ki, kd参数。将算法运用到电机控制系统的PID参数寻优中,仿真结果表明基于此算法设计的PID控制器改善了电机控制系统的动态性能和稳定性。     

Robust multi-parametric model predictive control for LPV systems with application to anaesthesia

We present a multi-parametric model predictive controller (mpMPC) for discrete-time linear parameter-varying (LPV) systems based on the solution of the mpMPC problem for discrete-time linear time-invariant (LTI) systems. The control method yields a controller that adapts to parameter changes of the LPV system. This is accomplished by an add-on unit to the implementation of the mpMPC for LTI systems. No modification of the optimal mpMPC solution for LTI systems is needed. The mpMPC for LPV systems is entirely based on simple computational steps performed on-line. This control design method could improve the performance and robustness of a mpMPC for LPV systems with slowly varying parameters. We apply this method to process systems which suffer from slow variation of system parameters due, for example, to aging or degradation. As an illustrative example the reference tracking control problem of the hypnotic depth during intravenous anaesthesia is presented: the time varying system matrix mimics an external disturbance on the hypnotic depth. In this example the presented mpMPC for LPV systems shows a reduction of approximately 60% of the reference tracking error compared to the mpMPC for LTI systems.     

具有学习特点的控制器设计算法

对于普遍存在的具有未知参数的随机最优控制问题,本文提出了一种具有学习特点的控制器设计算法.该算法用Kalman滤波估计系统的未知参数,在滚动优化机制下用动态规划获取控制增益,为了赋予控制器的学习特点,在LQG控制律中附加使下一时刻估计方差最小的学习控制分量.仿真结果表明了算法的有效性.     

Risk-sensitive dual control

In this paper, we develop new results concerning the risk-sensitive dual control problem for output feedback nonlinear systems, with unknown time-varying parameters. These results are not merely immediate specializations of known risk-sensitive control theory for nonlinear systems, but rather, are new formulations which are of interest in their own right. A dynamic programming equation solution is given to an optimal risk-sensitive dual control problem penalizing outputs, rather than the states, for a reasonably general class of nonlinear signal models. This equation, in contrast to earlier formulations in the literature, clearly shows the dual aspects of the risk-sensitive controller regarding control and estimation. The computational task to solve this equation, as has been seen for the risk-neutral dual control problem, suffers from the so-called ‘curse of dimensionality’. This motivates our study of the risk-sensitive version for a suboptimal risk-sensitive dual controller. Explicit controllers are derived for a minimum phase single-input, single-output auto-regressive model with exogenous input and unknown time-varying parameters. Also, simulation studies are carried out for an integrator with a time-varying gain. They show that the risk-sensitive suboptimal dual controller is more robust to uncertain noise environments compared with its risk-neutral counterpart. © 1997 by John Wiley & Sons, Ltd.