Reliability-based robust design optimization: A general methodology using genetic algorithm

In this paper, we present an improved general methodology including four stages to design robust and reliable products under uncertainties. First, as the formulation stage, we consider reliability and robustness simultaneously to propose the new formulation of reliability-based robust design optimization (RBRDO) problems. In order to generate reliable and robust Pareto-optimal solutions, the combination of genetic algorithm with reliability assessment loop based on the performance measure approach is applied as the second stage. Next, we develop two criteria to select a solution from obtained Pareto-optimal set to achieve the best possible implementation. Finally, the result verification is performed with Monte Carlo Simulations and also the quality improvement during manufacturing process is considered by identifying and controlling the critical variables. The effectiveness and applicability of this new proposed methodology is demonstrated through a case study.     

Non-existence of finite-time stable equilibria in fractional-order nonlinear systems

We note that in the literature it is often taken for granted that for fractional-order system without delays, whenever the system trajectory reaches the equilibrium, it will stay there. In fact, this is the well-known phenomenon of finite-time stability. However, in this paper, we will prove that for fractional-order nonlinear system described by Caputo’s or Riemann–Liouville’s definition, any equilibrium cannot be finite-time stable as long as the continuous solution corresponding to the initial value problem globally exists. In addition, some examples of stability analysis are revisited and linear Lyapunov function is used to prove the asymptotic stability of positive fractional-order nonlinear systems.     

Augment low-field intra-operative MRI with preoperative MRI using a hybrid non-rigid registration method

Background

Preoperatively acquired diffusion tensor image (DTI) and blood oxygen level dependent (BOLD) have been proved to be effective in providing more anatomical and functional information; however, the brain deformation induced by brain shift and tumor resection severely impairs the correspondence between the image space and the patient space in image-guided neurosurgery.

Method

To address the brain deformation, we developed a hybrid non-rigid registration method to register high-field preoperative MRI with low-field intra-operative MRI in order to recover the deformation induced by brain shift and tumor resection. The registered DTI and BOLD are fused with low-field intra-operative MRI for image-guided neurosurgery.

Results

The proposed hybrid registration method was evaluated by comparing the landmarks predicted by the hybrid registration method with the landmarks identified in the low-field intra-operative MRI for 10 patients. The prediction error of the hybrid method is 1.92 ± 0.54 mm, and the compensation accuracy is 74.3 ± 5.0%. Compared to the landmarks far from the resection region, those near the resection region demonstrated a higher compensation accuracy (P-value = .003) although these landmarks had larger initial displacements.

Conclusions

The proposed hybrid registration method is able to bring preoperatively acquired BOLD and DTI into the operating room and compensate for the deformation to augment low-field intra-operative MRI with rich anatomical and functional information.     

超磁致伸缩作动器的率相关Hammerstein模型与H∞鲁棒跟踪控制

利用Hammerstein模型对超磁致伸缩作动器（Giant magnetostrictive actuators,GMA）的率相关迟滞非线性进行建模,分别以改进的 Prandtl-Ishlinskii（Modified Prandtl-Ishlinskii）模型和外因输入自回归模型（Autoregressive model with exogenous input,ARX）代表Hammerstein模型中的静态非线性部分和线性动态部分,并给出了模型的辨识方法. 此模型能在1～100Hz频率范围内较好地描述GMA的率相关迟滞非线性. 提出了带有逆补偿器和H∞鲁棒控制器的二自由度跟踪控制策略,实时跟踪控制实验结果证明了所提策略的有效性.     

Robust control of variable speed autonomous underwater vehicle

Set point tracking control of autonomous underwater vehicle (AUV) via robust model predictive control (RMPC) is considered. Input-constrained RMPC with integral action, which has been developed in our previous work, is used to control the AUV in this study. In order to derive a RMPC control rule, non-linear dynamics of AUV with six degree of freedom is linearized at certain operating points. So, horizontal and vertical plane dynamics of system are represented by linear models which have polytopic uncertainties. Since the derived control rule will be used in real time, the computation time should be reduced. To overcome this computational time problem and get rid of trial–error step of Algorithm 1, a new algorithm is proposed here. The simulations are carried out using the control rule based on this algorithm and these results are presented.     

Consistent hierarchical economic NMPC for a class of hybrid systems using neighboring-extremal updates

A hierarchical two-layer control algorithm is developed for a class of hybrid (discrete-continuous dynamic) systems to support economically optimal operation of batch or continuous processes with a predefined production schedule. For this class of hybrid systems, the optimal control moves as well as the controlled switching times between two adjacent modes are determined online. In contrast to closely related schemes for integrated scheduling and control, the sequence of modes is not optimized. On the upper layer, the economic optimal control problem is solved rigorously by a slow hybrid economic model predictive controller at a low sampling rate. On the lower layer, a fast hybrid neighboring-extremal controller is based on the same economic optimal control problem as the slow controller to ensure consistency between both layers. The fast neighboring-extremal controller updates rather than tracks the optimal trajectories from the upper layer to account for disturbances. Consequently, the fast controller steers the process to its operational bounds under disturbances and the economic potential of the process is exploited anytime. The suggested two-layer control algorithm provides fully consistent control action on the fast and slow time-scale and thus avoids performance degradation and even infeasibilities which are commonly encountered if inconsistent optimal control problems are formulated and solved.     

Fast economic model predictive control based on NLP-sensitivities

This study adapts the advanced step NMPC framework to Economic NMPC. Here, sufficient conditions for nominal stability are derived for NMPC controllers that incorporate economic stage costs with appropriate regularization. To guarantee these conditions, we derive a constructive strategy to calculate the regularization term directly. Moreover, we extend the sensitivity components in the advanced step NMPC framework to consider a rigorous path-following algorithm. This approach accounts for active set changes and allows much weaker constraint qualifications. Moreover, using an ℓ₁ formulation of the NMPC problem satisfies these constraint qualifications and allows more reliable solution of the moving horizon optimization problem, even in the presence of noise. Finally, all of these concepts are demonstrated on a detailed case study with a continuously stirred tank reactor.     

基于全网能量均衡的WirelessHART图路由算法

如何均衡使用网络节点能量并生成高可靠路由是WirelessHART网络研究的一个难题,为此提出了一种GRAEB（graph routing algorithm based on energy balancing）图路由算法。首先,网络被初始化为连通的图结构,能够提高丰富的冗余路径。网络管理器根据全网节点剩余能量、通信周期、链路分布等因子生成节点鲁棒系数矩阵,节点通过比较邻居的鲁棒系数来选择最优路径。另外,规定了邻居节点数的上限阈值,每次路由更新只保留鲁棒系数最优的邻居。仿真结果表明GRAEB不仅提高了网络可靠性,而且延长了网络寿命。     

序贯抗差估计在旋转调制陀螺寻北中的应用

基于序贯抗差估计方法针对简化了的旋转调制陀螺寻北仪输出信号进行了寻北仿真研究.介绍了该型寻北仪的基本原理,在经典充贯平差的基础上,结合抗差M估计的原理,得到了序贯抗差估计的计算流程与公式.将寻北仪输出信号划分为2个阶段,对上述方法进行了仿真应用.仿真结果表明：通过该算法所得寻北结果误差均值与标准差均优于其它算法.     

六维腕力传感器非线性正解耦方法设计

多维力传感器利用其多个转换单元完成测量加载于其结构上未知负载的作用效果,解耦是其设计的重要组成部分。针对传统静态线性解耦方法的不足,试图将传统线性解耦方程扩展为多项式结构,受其多元高次方式通解形式的启发,构造了一种多项式非线性静态正解耦方程,该方法无需传统线性解耦方法中的曲线拟合、逆解,不依赖以系统是线性为前提,且方程可以扩展成任意结构的多项式。实验结果表明:该方法能降低输出耦合误差。