The application of parameter sensitivity analysis methods to inverse simulation models

Knowledge of the sensitivity of inverse solutions to variation of parameters of a model can be very useful in making engineering design decisions. This article describes how parameter sensitivity analysis can be carried out for inverse simulations generated through approximate transfer function inversion methods and also through the use of feedback principles. Emphasis is placed on the use of sensitivity models and the article includes examples and a case study involving a model of an underwater vehicle. It is shown that the use of sensitivity models can provide physical understanding of inverse simulation solutions that is not directly available using parameter sensitivity analysis methods that involve parameter perturbations and response differencing.     

An investigation of open-loop and inverse simulation as nonlinear model validation tools for helicopter flight mechanics

The nonlinear model validation techniques of open-loop and inverse simulation are introduced. The methodologies are explained and examples are given. The paper presents the results of an investigation into the use of open-loop and inverse simulation to help in the development of a nonlinear real-time helicopter model. The individual rigid body state equations in the model are simulated with the aim of producing insight into the cause of inaccuracies in the model. A suspected source of inaccuracy is verified using partial open-loop simulation. Unmodelled dynamics are represented by using the relevant flight data as an open-loop input to the simulation thus revealing the effect of incorporating those dynamics. After localising the cause of inaccuracies in the simulation model, modifications and improvements are verified using closed-loop simulation. The improvements are then evaluated by comparing results in normal and inverse simulation modes.     

一类离散系统基于偏差分离的输出反馈控制

针对一类不确定离散系统,提出一种基于偏差分离的输出反馈双控制策略,首先设计动态输出反馈控制器镇定其标称系统(即不确定性为零的情形);然后通过构造降维观测器,在线获取不确定性信息,并采用线性的补偿器实时补偿系统的不确定性.该控制策略采用线性控制,在工程中可以方便地实现,具有好的工程意义.理论分析证明了闭环指数渐近稳定的充分条件.仿真结果表明该双控制策略的有效性.     

On Regularization Methods for Inverse Problems of Dynamic Type

In this paper, we consider new regularization methods for linear inverse problems of dynamic type. These methods are based on dynamic programming techniques for linear quadratic optimal control problems. Two different approaches are followed: a continuous and a discrete one. We prove regularization properties and also obtain rates of convergence for the methods derived from both approaches. A numerical example concerning the dynamic EIT problem is used to illustrate the theoretical results.     

Investigation of inverse simulation for design of feedforward controllers

This paper describes the use of inverse simulation to develop feedforward controllers for model-based output-tracking control system structures, thus avoiding the more complicated techniques of model inversion. Similarities and shortcomings of the inverse simulation and model inversion approaches are explored. It is found that, with suitable values of discretized time interval, the method based on inverse simulation may be preferable for minimum-phase systems. Depending upon zero redistribution within the process of inverse simulation, non-minimum-phase problems for linear systems can also be handled. The conclusions are demonstrated using a non-linear HS125 aircraft model, a linearised Lynx helicopter model and a container ship model for ship steering control and roll stabilization.     

融合神经网络与数值计算的人体逆向运动学求解

人体逆向运动学问题是人体运动合成、人体运动捕获和理解的基本问题.由于人体关节链式系统的复杂性,人体逆向运动学方程往往存在多解或无解的情形.传统的方法通常采用解析或数值迭代方法求解逆向运动学问题,在给定足够多约束的情形下能够得到比较好的解,但无法处理少量约束下生成自然的人体姿态问题.近年来,从大规模数据集中学习统计模型参数的思想被广泛运用,求解人体逆向运动学的机器学习方法中经典工作|混合Gauss逆向运动求解模型(Gaussian mixture model-inverse kinematics,GMM-IK)就提出利用混合Gauss模型建模人体姿态数据分布,并采用期望最大化方法求解参数.随着深度学习技术的发展,本文提出一种自编码神经网络与数值迭代融合的方法,在给定少量约束的情形下依然能够得到自然的人体姿态,相较于GMM-IK方法,本文所提出的方法通过神经网络自动学习姿态分布,省去了模型的假设和特征的设计,且量化实验显示本文方法的关节坐标和角度重建误差相较于GMM-IK模型平均减少了25%和39%.在应用方面,本文方法可处理光学运动捕获数据,也可用于图像视频的人体姿态估计等领域.     

A comprehensive method for comparing mental models of dynamic systems

Mental models are the basis on which managers make decisions even though external decision support systems may provide help. Research has demonstrated that more comprehensive and dynamic mental models seem to be at the foundation for improved policies and decisions. Eliciting and comparing such models can systematically explicate key variables and their main underlying structures. In addition, superior dynamic mental models can be identified. This paper reviews existing studies which measure and compare mental models. It shows that the methods used to compare such models lack to account for relevant aspects of dynamic systems, such as, time delays in causal links, feedback structures, and the polarities of feedback loops. Mental models without those properties are mostly static models. To overcome these limitations of the methods to compare mental models, we enhance the widely used distance ratio approach (Markóczy and Goldberg, 1995) so as to comprehend these dynamic characteristics and detect differences among mental models at three levels: the level of elements, the level of individual feedback loops, and the level of the complete model. Our contribution lies in a new method to compare explicated mental models, not to elicit such models. An application of the method shows that this previously non-existent information is essential for understanding differences between managers’ mental models of dynamic systems. Thereby, a further path is created to critically analyze and elaborate the models managers use in real world decision making. We discuss the benefits and limitations of our approach for research about mental models and decision making and conclude by identifying directions for further research for operational researchers.     

Output feedback control for MIMO nonlinear systems using factorization

A new output feedback adaptive control scheme for multi-input and multi-output (MIMO) nonlinear systems is presented based on the high frequency gain matrix factorization and the backstepping approach with vector form. The only required prior knowledge about the high frequency gain matrix of the linear part of the system is the signs of its leading principal minors. The proposed controller is a dynamic one that only needs the measurement of the system output, and the observer and the filters are introduced in order to construct a virtual estimate of the unmeasured system states. The global stability of the closed-loop systems is guaranteed through this control scheme, and the tracking error converges to zero. Finally, the numerical simulation results illustrate the effectiveness of the proposed scheme.     

Multi-Level Strategic Evaluation of Hospital Plans and Decisions

Strategic decision making in hospitals involves the assessment of linkages between decisions that are typically made in a hierarchical fashion. In hospitals, as in most large organizations, overall system performance is a function of how well the critical decisions are integrated. This paper focuses on the multi-level nature of the decisions and policies that typically need to be evaluated in hospital planning, highlighting that both optimization and simulation approaches may be required. An application involving a large general purpose urban hospital is used to illustrate the interdependency between the levels in the planning hierarchy. An optimization model is formulated to deal with facility layout and capacity allocation while a simulation model is proposed to capture the complexities of hospital operations. The linkages and information feedback between the models are shown to be critical in the design of a system that performs well and facilitates strategic hospital planning.     

多机器人协调吊运系统逆运动学分析及优化

针对紧耦合多机器人协调吊运系统的逆运动学问题进行了分析,首先利用几何关系和力旋量平衡方程建立了系统的运动学模型和动力学模型;然后对系统的逆运动学进行分析,将其分为变柔索长度和固定柔索长度两种情况分别进行分析;随后对运动学逆解在某一时刻存在无穷多解、多组解和无解的情况分别给出了解决方法,对存在多组解的情况,提出一优化目标求解最优解;最后结合软件UG/ADAMS/MATLAB建立了系统的实验平台,通过实例仿真计算验证了方法的有效性,为后续进一步研究系统运动稳定性、优化拉力分布和控制算法奠定了基础.     

RESEARCHES ON INVERSE ORDER RULE FOR WEIGHTED GENERALIZED INVERSE

1 Inttoductlon and Preliminary KnowledgeThe generalized inverse is an important tool for researching the singular matrix problems,ac-POSed problems, optimication and statistics problems. The inverse order rule for generalizedinverse playS an forportant role on the theoretical research and numerical computations in theOf generaled inverse is(see [2) [6][8j). Another sufficient and neceSSary condition isIn this paper we generalize the above resultS to the case of the weighted generalized inv…     

Quasi-likelihood estimation of average treatment effects based on model information

In this paper, the estimation of average treatment effects is considered when we have the model information of the conditional mean and conditional variance for the responses given the covariates. The quasi-likelihood method adapted to treatment effects data is developed to estimate the parameters in the conditional mean and conditional variance models. Based on the model information, we define three estimators by imputation, regression and inverse probability weighted methods. All the estimators are shown asymptotically normal. Our simulation results show that by using the model information, the substantial efficiency gains are obtained which are comparable with the existing estimators.     

Testing for Varying Dispersion in Exponential Family Nonlinear Models

A diagnostic model and several new diagnostic statistics are proposed for testing for varying dispersion in exponential family nonlinear models. A score statistic and an adjusted score statistic based on Cox and Reid (1987, J. Roy. Statist. Soc. Ser. B, 55, 467-471) are derived in normal, inverse Gaussian, and gamma nonlinear models. An adjusted likelihood ratio statistic is also given for normal and inverse Gaussian nonlinear models. The results of simulation studies are presented, which show that the adjusted tests keep their sizes better and are more powerful than the ordinary tests.     

A quasi-static approach for constrained systems in real-time simulation

Detailed multi-body system models in applications like vehicle dynamics, robotics and bio-mechanics are designed for accurate off-line simulation. For real-time applications simplified models are used. The presented quasi-static solution method focuses on accelerated computation of the low frequency parts of the solution of the nonlinear equations of motion. The high frequency parts are eliminated by neglecting some of the inertia forces and torques. This reduces numerical stiffness and allows larger step-sizes for the time-integration. The efficient and real-time capable combination with existing highly efficient algorithms for multi-body dynamics (𝒪(N) multi-body formalisms) requires appropriate integration methods that are adapted to the special structure of the multi-body formalism and solve the non-linear constraints with a small, limited number of calculation steps. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Maximum a Posteriori Sequence Estimation Using Monte Carlo Particle Filters

We develop methods for performing maximum a posteriori (MAP) sequence estimation in non-linear non-Gaussian dynamic models. The methods rely on a particle cloud representation of the filtering distribution which evolves through time using importance sampling and resampling ideas. MAP sequence estimation is then performed using a classical dynamic programming technique applied to the discretised version of the state space. In contrast with standard approaches to the problem which essentially compare only the trajectories generated directly during the filtering stage, our method efficiently computes the optimal trajectory over all combinations of the filtered states. A particular strength of the method is that MAP sequence estimation is performed sequentially in one single forwards pass through the data without the requirement of an additional backward sweep. An application to estimation of a non-linear time series model and to spectral estimation for time-varying autoregressions is described.     

Robust output feedback stabilization for two-dimensional continuous systems in roesser form

This paper considers the problem of robust stabilization via dynamic output feedbackcontrollers for uncertain two-dimensional continuous systems described by the Roesser's state space model. The parameter uncertainties are assumed to be norm-bounded appearing in all the matrices of the system model. A sufficient condition for the existence of dynamic output feedback controllers guaranteeing the asymptotic stability of the closed-loop system for all admissible uncertainties is proposed. A desired dynamic output feedback controller can be constructed by solving a set of linear matrix inequalities. Finally, an illustrative example is provided to demonstrate the applicability and effectiveness of the proposed method.     

离散交通流模型的反馈线性化与拥堵控制

在自动化高速公路环境下,提出一种改进的宏观离散交通流模型密度控制方法.利用反馈线性化方法,将宏观离散交通流模型转换为一般容易处理的线性系统模型,简化了密度控制器的设计.利用线性系统中具有输入变换的跟踪反馈控制方法,对线性化后的系统模型设计控制律.通过控制该线性系统的状态变量,间接稳定离散交通流模型中的交通流密度,达到对道路交通流拥堵的控制.同时给出设计方法和步骤,仿真实例说明了方法的实用性.