基于Matlab的线性最优控制系统鲁棒性分析

针对线性最优控制系统的鲁棒性分析问题 ,提出以系统稳定裕度作为计算对象 ,研究系统参数与稳定裕度之间的动态关系 ,从而得出系统鲁棒性的直观描述 .这一方法采用Matlab的控制函数编制程序 ,连续求解代数Riccati方程 ,得出系统稳定裕度随参数扰动的变化趋势 ,再由计算机作图显示 .仿真结果表明 ,它对具有精确数学模型的最优控制系统的分析有实用价值 .

具有模型和实际差异的非线性系统最优控制算法及其收敛性(英文)

针对模型和实际之间的差异，提出了一种基于时变线性二次型问题的动态系统优化和参数估计集成的算法，该算法能逼近实际问题最优解．给出了该算法收敛的一个充分条件，分析了它的最优性．仿真例子说明了该算法的有效性和实用性．

旋臂式倒立摆的控制设计与仿真

针对旋臂式倒立摆的稳定控制问题,建立了二阶旋臂式倒立摆系统的数学模型,运用连续系统线性二次型最优控制理论,设计了旋臂式倒立摆控制系统的线性二次型调节器,使倒立摆系统在闭环状态下稳定。运用Matlab进行仿真,通过与传统的极点配置方法相比较,发现最优控制效果更好。

悬臂式倒立摆H_∞控制设计及仿真

针对旋臂式倒立摆的稳定控制问题,建立了二阶旋臂式倒立摆系统的数学模型,运用连续系统线性鲁棒H∞最优控制理论,通过设计旋臂式倒立摆控制系统的鲁棒调节器,使倒立摆系统在闭环状态下稳定并具有较强的鲁棒稳定性。运用Matlab进行仿真,通过与传统线性二次型最优控制配置方法相比较,结果发现鲁棒H∞最优控制效果更好。

ZigBee-WiFi协同无线传感网络的节能技术

为使无线传感网络实现高效节能,提出一种新颖的基于ZigBee-WiFi协同方式进行的时钟同步机制。ZigBee无线传感节点可以通过内置的接收信号强度寄存器(RSSI),感知同一频段下WiFi周期性发送的信标帧,并用其作为参考时钟,通过补偿校正本地的时钟,但此方法校正后时钟偏差较大;在此基础上提出一种基于状态空间的时钟模型,采用卡尔曼滤波器和离散线性定常系统的二次型最优控制校正算法跟踪并校正状态变量,获得很好的时钟校正精度。分析时钟同步误差与校正周期的关系,综合多方面因素,与工业中常用的异步时钟机制进行对比,比较结果表明,该时钟同步机制使ZigBee网络节能效果显著提高。

Science Letters:A minimax optimal control strategy for uncertain quasi-Hamiltonian systems

A minimax optimal control strategy for quasi-Hamiltonian systems with bounded parametric and/or external disturbances is proposed based on the stochastic averaging method and stochastic differential game. To conduct the system energy control, the partially averaged Ito stochastic differential equations for the energy processes are first derived by using the stochastic averaging method for quasi-Hamiltonian systems. Combining the above equations with an appropriate performance index, the proposed strategy is searching for an optimal worst-case controller by solving a stochastic differential game problem. The worst-case disturbances and the optimal controls are obtained by solving a Hamilton-Jacobi-Isaacs （HJI） equation. Numerical results for a controlled and stochastically excited DulTlng oscillator with uncertain disturbances exhibit the efficacy of the proposed control strategy.

A novel policy iteration based deterministic Q-learning for discrete-time nonlinear systems

In this paper, a novel iterative Q-learning algorithm, called "policy iteration based deterministic Qlearning algorithm", is developed to solve the optimal control problems for discrete-time deterministic nonlinear systems. The idea is to use an iterative adaptive dynamic programming(ADP) technique to construct the iterative control law which optimizes the iterative Q function. When the optimal Q function is obtained, the optimal control law can be achieved by directly minimizing the optimal Q function, where the mathematical model of the system is not necessary. Convergence property is analyzed to show that the iterative Q function is monotonically non-increasing and converges to the solution of the optimality equation. It is also proven that any of the iterative control laws is a stable control law. Neural networks are employed to implement the policy iteration based deterministic Q-learning algorithm, by approximating the iterative Q function and the iterative control law, respectively. Finally, two simulation examples are presented to illustrate the performance of the developed algorithm.

Nonlinear optimal control of rotating flexible shaft in active magnetic bearings

The optimal control of nonlinear systems has been studied for years by many researchers. However, the application of optimal control problem to nonlinear non-affine systems needs more attention. In this paper we propose an optimal control design technique for a class of nonlinear and control non-affine equations. The dynamic equations of a flexible shaft supported by a pair of active magnetic bearings (AMBs) are used as the nonlinear control non-affine equations. Mathematical model for the flexible beam is chosen to be the well known Timoshenko beam model, which takes rotary inertia and shear deformations into account, and it is assumed that the shaft is supported by two frictionless bearings at the ends. The effective control of such systems is extremely important for very high angular velocity shafts which are a feature of many modern machines. The control must be able to cope with unbalanced masses and hence be very robust. We shall approach the problem by discretising the Timoshenko beam model and using standard difference formulae to develop a finite-dimensional model of the system. Then we use a recently developed technique for controlling nonlinear systems by reducing the problem to a sequence of linear time-varying (LTV) systems. An optimal control designed for each approximating linear, time-varying system and recent results show that this method will converge uniformly on compact time intervals to the optimal solution.

OPTIMAL TRACKING FOR BILINEAR STOCHASTIC SYSTEM DRIVEN BY FRACTIONAL BROWNIAN MOTIONS

This paper discusses a problem of optimal tracking for a linear control system driven by fractional Brownian motion.An equation is obtained for the linear Markov feedback control.The existence and uniqueness of the solution to the equation are also studied.