Fabrication uncertainties and yield optimization in MEMS tunable capacitors

Intrinsic uncertainties of MEMS fabrication processes can severely affect the performance of devices because the tolerance ranges of these processes are relatively large and improvement of process accuracy is very expensive. Therefore, the analysis of fabrication uncertainties and their outcome on a device performance is a vital task before finalizing the design. In this paper, the effects of process inaccuracy on the performance of MEMS tunable capacitors are studied. Design parameters such as dimensions of electrodes and the initial gap between them and the stiffness of supporting beams are considered as random variables. The variation of these parameters within tolerance ranges drastically alters the capacitor's actual response from the desired one and results in low yield. Hence, design optimization with the objective of maximizing yield in early steps becomes very important. An effective method for yield optimization of MEMS capacitors under given fabrication uncertainties is introduced. The method utilizes aspects of the advanced first-order second-moment (AFOSM) reliability method to find a linearized feasible region to estimate the yield. The yield is calculated directly using the joint cumulative distribution function (CDF) over the tolerance box requiring no numerical integration and avoiding computational complexity. The optimal design verified by Monte-Carlo (M-C) simulation exhibits a significant increase in the yield. The main advantage of this method comparing to other design optimization methods is that the proposed method does not change the design topology or fabrication accuracy. It increases the yield by finding the optimum design variables as demonstrated in this paper.     

动态不确定非线性系统直接自适应模糊backstepping控制

对一类单输入单输出动态不确定非线性系统,提出一种直接自适应模糊backstepping和小增益相结合的控制方法.设计中,首先用模糊逻辑系统逼近虚拟控制器:其次把自适应模糊控制和backstepping控制设计技术相结合.给出了直接自适应模糊控制设计方法.最后基于Lyapunov函数和小增益方法证明了整个闭环系统的稳定性.仿真实例进一步验证了所提方法的有效性.     

不确定系统的稳定广义预测控制

针对一类有界不确定线性离散被控对象,采用Min-Max优化方法,提出一种新的稳定广义预测控制(MMSGPC)算法.引入内模控制结构,将干扰和不确定性从被控对象中分离出来,并利用局部反环节对其进行补偿;采用Min-Max优化方法,将终端约束条件转化为有界不确定性最差情况时应的线性方程;通过引入矩阵的Moore-Penrose逆,得到了终端约束线性方程的通解,并结合性能指标函数求得了最优控制律.通过仿真实例验证了该方法的稳定效果.     

Delay‐dependent robust stability and stabilization for uncertain discrete singular systems with delays

The robust stability and robust stabilization for time‐delay discrete singular systems with parameter uncertainties is discussed. A delay‐dependent linear matrix inequality (LMI) condition for the time‐delay discrete systems to be nonsingular and stable is given. Based on this condition and the restricted system equivalent transformation, the delay‐dependent LMI condition is proposed for the time‐delay discrete singular systems to be admissible. With this condition, the problems of robust stability and robust stabilization are solved, and the delay‐dependent LMI conditions are obtained. Numerical examples illustrate the effectiveness of the method given in the paper. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

考虑多重不确定性的含需求响应及电碳交易的虚拟电厂优化调度策略

为解决当前对虚拟电厂系统中多重不确定性源的表征考虑不全面且处理方法单一的问题,提出一种基于蒙特卡罗与曼哈顿概率距离的场景生成与削减法来对风光出力、电价进行建模,并利用区间法对碳价、需求响应进行建模;以虚拟电厂运行成本最小化为目标,综合考虑电碳交易及激励型需求响应机制,建立基于区间线性规划的虚拟电厂优化调度模型,采用强区间线性规划法中的两阶段分解算法对模型进行求解,针对不同不确定性源的特点,采用2种方法处理虚拟电厂系统中的多重不确定性。算例结果表明,该模型能够通过指标变化趋势有效地反映多重不确定性因素对系统调度结果的影响,在保证虚拟电厂系统安全运行的基础上提高经济性,区间形式的表示为决策者留出了一定的现实选择空间。     

计及多元不确定性的氢电耦合微电网多阶段动态规划方法

针对传统单阶段规划设计方法存在的初始投资成本高、源荷匹配性弱、经济性不足的缺点,提出了一种氢电耦合微电网多阶段动态规划方法,可确定各设备的最优配置容量和投资时机,并基于混合优化架构增强模型对多元不确定性的适应能力。其中,设备在规划周期内投资成本的波动不确定性特征以随机概率场景描述,可再生分布式电源出力和负荷需求不确定性则以不确定场景集合处理。在此基础上,进一步考虑微电网规划周期内的负荷增长率、设备性能衰退等动态因素,提升规划方案对未来动态信息变化的适用性,并最终构建随机-鲁棒混合整数线性规划模型描述微电网的多阶段动态规划问题。仿真结果表明,与传统的单阶段规划模型相比,所提方法的总成本降低近5.0%,初始投资成本降低近13.9 %,投资回收期缩短近32.9 %,有利于提升微电网规划方案的经济性。     

Adaptive fault-tolerant control for a class of flexible air-breathing hypersonic vehicles

In this paper, a novel adaptive fault-tolerant control (FTC) scheme is proposed for a class of flexible air-breathing hypersonic vehicles with unknown inertial and aerodynamic parameters and even input constraints. The fault model under consideration covers the case that all actuators suffer from unknown time-varying faults. In the controller design and stability analysis, we introduce new Lyapunov functions, some differentiable auxiliary functions, a bound estimation approach, and a Nussbaum function, which help us successfully circumvent the obstacle caused by the faults, input constraints, and flexible modes. In addition to higher reliability, the proposed scheme is able to ensure that all closed-loop signals are globally uniformly bounded and to steer the tracking errors of altitude and velocity into predefined arbitrarily small residual sets. As a result, the tracking accuracy can be designated in advance. Simulation results illustrate the effectiveness of the proposed scheme.     

基于深度强化学习的居民实时自治最优能量管理策略

随着居民分布式资源的普及,如何考虑用户多类型设备的运行特性,满足实时自治能量管理需求以达到用户侧经济性最优成为亟待解决的课题。传统基于模型的最优化方法在模型精准构建和应对多重不确定性等方面存在局限性,为此提出一种无模型的基于深度强化学习的实时自治能量管理优化方法。首先,对用户设备进行分类,采用统一的三元组描述其运行特性,并确定相应的能量管理动作;接着,采用长短期记忆神经网络提取环境状态中多源时序数据的未来走势;进而,基于近端策略优化算法,赋能在多维连续-离散混合的动作空间中高效学习最优能量管理策略,在最小化用电成本的同时提升策略对不确定性的适应性;最后,通过实际情境对比现有方法的优化决策效果,验证所提方法的有效性。     

Preview control of polytopic time-varying systems subject to actuator saturation

In this study, a novel method is proposed to track a previewable reference signal in the polytopic time-varying system with input saturation. Firstly, an augmented model containing future information is constructed using a new formal variable. This leads to the tracking control problem of polytopic time-varying system with input saturation is transformed into a stability problem of augmented error system. Next, the state and static output feedback preview controls are introduced, and the corresponding controller gains are produced by the proposed conditions. Two examples are presented to validate the effectiveness of the proposed preview controller.