There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

There is an increasing demand for dynamic systems to become safer and more reliable This requirement extends beyond the normally accepted safety-critical systems such as nuclear reactors and aircraft, where safety is of paramount importance, to systems such as autonomous vehicles and process control systems where the system availability is vital It is clear that fault diagnosis is becoming an important subject in modern control theory and practice Robust Model-Based Fault Diagnosis for Dynamic Systems presents the subject of model-based fault diagnosis in a unified framework It contains many important topics and methods; however, total coverage and completeness is not the primary concern The book focuses on fundamental issues such as basic definitions, residual generation methods and the importance of robustness in model-based fault diagnosis approaches In this book, fault diagnosis concepts and methods are illustrated by either simple academic examples or practical applications The first two chapters are of tutorial value and provide a starting point for newcomers to this field The rest of the book presents the state of the art in model-based fault diagnosis by discussing many important robust approaches and their applications This will certainly appeal to experts in this field Robust Model-Based Fault Diagnosis for Dynamic Systems targets both newcomers who want to get into this subject, and experts who are concerned with fundamental issues and are also looking for inspiration for future research The book is useful for both researchers in academia and professional engineers in industry because both theory and applications are discussed Although this is a research monograph, it will be an important text for postgraduate research students world-wide The largest market, however, will be academics, libraries and practicing engineers and scientists throughout the world

Robust Model-Based Fault Diagnosis for Dynamic Systems

비만아 부모의 돌봄 경험: q 방법론

The purpose of this paper is to provide a comprehensive presentation and interpretation of the Ensemble Kalman Filter (EnKF) and its numerical implementation. The EnKF has a large user group, and numerous publications have discussed applications and theoretical aspects of it. This paper reviews the important results from these studies and also presents new ideas and alternative interpretations which further explain the success of the EnKF. In addition to providing the theoretical framework needed for using the EnKF, there is also a focus on the algorithmic formulation and optimal numerical implementation. A program listing is given for some of the key subroutines. The paper also touches upon specific issues such as the use of nonlinear measurements, in situ profiles of temperature and salinity, and data which are available with high frequency in time. An ensemble based optimal interpolation (EnOI) scheme is presented as a cost-effective approach which may serve as an alternative to the EnKF in some applications. A fairly extensive discussion is devoted to the use of time correlated model errors and the estimation of model bias.

The Ensemble Kalman Filter: Theoretical formulation and practical implementation

In this paper, we propose a new algorithm of an adaptive actor-critic method with multi-step simulated experiences, as a kind of temporal difference (TD) method. In our approach, the TD-error is composed of two value- functions and m utility functions, where m denotes the number of multi-steps in which the experience should be simulated. The value-function is constructed from the critic formulated by a radial basis function neural network (RBFNN), which has a simulated experience as an input, generated from a predictive model based on a kinematic model. Thus, since our approach assumes that the model is available to simulate the m-step experiences and to design a controller, such a kinematic model is also applied to construct the actor and the resultant model based actor (MBA) is also regarded as a network, i.e., it is just viewed as a resolved velocity control network. We implement this approach to control nonholonomic mobile robot, especially in a trajectory tracking control problem for the position coordinates and azimuth. Some simulations show the effectiveness of the proposed method for controlling a mobile robot with two-independent driving wheels.

An Adaptive Actor-critic Algorithm with Multi-step Simulated Experiences for Controlling Nonholonomic Mobile Robots

This paper presents a method for redundancy resolution of an industrial manipulator in a teleoperated force control task A seven degree-of-freedom (DOF) industrial manipulator manufactured by the Mit-subishi Heavy Industries Ltd. is used for experiments. The task involves obeying a force command sent from a remote computer while autonomously adapting the posture to avoid unexpected obstacles moving toward the manipulator. Redundancy resolution is employed for autonomous adaptation of the configuration to avoid the obstacle while continuing the force control task This self-adaptive skill on the slave manipulator side is very important because teleoperation is often performed in dangerous or partially unknown environments where unexpected changes such as moving obstacles can well be expected. In such situations, the control ability of the master side is very limited due to the practical limitations of vision sensors to capture a comprehensive view of the environment and the limitations of the degrees of freedom on the master ma-nipulator. The proposed method relies on two modules of an intelligent controller on the slave side. The first is an on-line fuzzy neural network (FNN) for intelligent force control, and the second is a configuration controller that works in harmony with the first to exploit redundancy to react to avoid moving obstacles such that the latter does not inhibit the progress of the former. The second controller generates joint velocity commands in null space of the hand Jacobian, so that its activation does not affect the force controller. Here we show that the proposed method can skillfully avoid a moving obstacle without stopping the force control task This skillful adaptation ability can significantly improve the efficiency and safety of teleoperated force control tasks with less burden on the master side. This paper presents some promising experimental results to demonstrate the effectiveness of the proposed method.

Enhancing the Autonomy of Teleoperated Redundant Manipulators Through Fusion of Intelligent Control Modules

Evolutionary Collision Free Optimal Trajectory Planning for Mobile Robots

Motion pattern analysis of semi-looper type robots with four links

Stochastic fuzzy control using stochastic control theory, instead of using conventional fuzzy reasoning, is proposed. We first solve a control problem of one-step predictive output tracking for linear stochastic systems. Next, we consider dynamic multiple model adaptive control(MMAC)for the initial data distribution, under the uncertainties of the initial states. We further consider static MMAC that can be applied for cases of completely unknown plants. It is then shown that a stochastic fuzzy control has some Gaussian potential functions as membership functions and can be used to assign some a priori probabilities to the fuzzy sets or to the control rules, if the probability density function with respect to the output error is replaced by a simple characteristic function. It is also shown that the stochastic fuzzy contol becomes fuzzy control, if all of the a priori probabilities are set to be equal at any control instant.

Keigo Watanabe

Papers

An Adaptive Actor-critic Algorithm with Multi-step Simulated Experiences for Controlling Nonholonomic Mobile Robots

Enhancing the Autonomy of Teleoperated Redundant Manipulators Through Fusion of Intelligent Control Modules

Evolutionary Collision Free Optimal Trajectory Planning for Mobile Robots

Motion pattern analysis of semi-looper type robots with four links

Stochastic Fuzzy Control : Theoretical Derivation