Analysis and tuning of adaptive generalized predictive control

The adaptive Generalized Predictive Controller (Clarke et al., 1987a, b) is capable of controlling plants with variable dead-time, unknown model orders and unstable poles and zeros. This paper shows how the GPC control law can be written in an equivalent general linear transfer function form which simplifies closed-loop (eg. root locus) analysis. Three recommended strategies for selecting the design parameters during the commissioning stage allow the user to adjust the closed-loop speed of response on-line using only a single active tuning parameter. Experimental runs confirm the ability of adaptive GPC to provide a consistent closed-loop response in spite of large process changes.     

水泥回转窑的智能控制

文章介绍了PIT Navigator对水泥熟料生产自动化控制的实现,基于神经网络的自学习、自适应控制元件,将相关的过程数据记录下来,随后借助数据关联性进行分析,在预测和自学习过程模型(模型预测控制)中处理这些数据,并计算过程参数的实时预测,使用这些工具,可以实现更稳定的窑况,同时提高产量和降低能耗。     

水资源适应性利用理论及其在治水实践中的应用前景

水系统受气候变化和人类活动的影响,一直在不断地变化,因此水资源的开发利用也应适应这种变化,而不能按照固定的控制阈值或约束条件来进行。当然,也不是被动的、随意的去适应。在对水系统及其与环境变化关系剖析的基础上,分析了水资源适应性利用的原理与模式;基于对环境变化下水资源适应性利用机理的认识,提出水资源适应性利用理论的框架体系,阐述了其基本理念和关键内容;基于对我国现代治水实践的分析,分析了其暗含的水资源适应性利用理论的内容,以及我国治水实践中存在的问题及应用水资源适应性利用理论解决这些问题的可能途径。研究认为,水资源适应性利用理论对水资源合理开发、综合利用、科学管理具有重要的指导作用,为环境变化下水资源利用研究与实践奠定理论基础。     

基于自适应逆的温度控制系统

介绍了自适应逆控制方案,采用变步长LMS自适应滤波算法,用该算法建立对象的模型、对象逆模型,并设计了自适应逆控制器。将这种控制方案应用到含有大惯性、纯滞后的系统。仿真结果表明,此种方案鲁棒性及动态性能较好。     

Gquest: Modeling patient questionnaires and administering them through a mobile platform application

Background

The use of surveys is becoming popular in the health care industry for acquiring information useful to the accomplishment of several studies. Besides their exploitation on a large scale for conducting epidemiological studies, surveys are being increasingly carried out on a narrower perspective through the administration of questionnaires aimed at assessing the quality of life perceived by patients or their clinical status during mid- or long-term treatments. This is useful for managing resources or optimizing and individualizing treatments.

Objectives

This paper describes Gquest, a platform for modeling, generating and administering questionnaires through mobile devices such as smartphones or tablets. Gquest was motivated by the need of administering questionnaires during home treatments, albeit its applicability is rather general. The main requirement was to have a very simple, clean and easy to use platform able to support (a) physicians in the design and delivery of questionnaires and (b) outpatients in sending self-recorded outcomes to the clinical staff.

Methods

Gquest has two basic components. The first one is a model devised for representing questionnaires which is extremely flexible. It allows the generation of questions and answers of different types, supports adaptivity in the dialog with the user and enforces simple consistency rules for checking his input. The second component is an application able to run instances of those questionnaires. It downloads questionnaires over the air in terms of XML files from a server and stores them locally into the mobile repository. Questionnaires become then available to the user, who in our case is a patient or one of his relatives. The user can select which one to fill-in, according to his needs and/or the specific treatment protocol. The selected questionnaire may be filled-in all at once or be completed in subsequent steps over time since any input provided is persisted on a local database. Finally, when a questionnaire is closed all the answers are transparently synchronized to a server for further evaluation or statistical purposes.

Results

The platform has been exploited and tested in two medical domains where questionnaires have very different purposes; a quality of life pilot survey involving patients with atrial fibrillation within the EU-funded project MobiGuide, and an investigation of the users’ needs during the requirements elicitation phase of a telemedicine project for the safe discharge of fragile patients.

Conclusions

Gquest proved to be a suitable tool for dealing with a wide variety of question types and has been positively accepted by the patients enrolled in the pilot study.     

Modeling and global trajectory tracking control for an over-actuated MAV

This paper deals with an original micro aerial vehicle (MAV) design, the Omnicopter MAV. It has two central coaxial rotors with fixed-pitch propellers and three perimeter mounted ducted fans with servo motors performing thrust vectoring. Compared with traditional rotary wing MAVs that have inherent underactuation, the Omnicopter possesses some advantages in mobility, for example, lateral translation with zero attitude and hover with nonzero attitude. The trajectory tracking control design, global stability analysis, and control allocation are demonstrated through numerical simulation. The advantage of zero attitude translation is illustrated through experimental results.     

HMM-based state classification of a user with a walking support system using visual PCA features

The improvement of safety and dependability in systems that physically interact with humans requires investigation with respect to the possible states of the user’s motion and an attempt to recognize these states. In this study, we propose a method for real-time visual state classification of a user with a walking support system. The visual features are extracted using principal component analysis and classification is performed by hidden Markov models, both for real-time fall detection (one-class classification) and real-time state recognition (multi-class classification). The algorithms are used in experiments with a passive-type walker robot called “RT Walker” equipped with servo brakes and a depth sensor (Microsoft Kinect). The experiments are performed with 10 subjects, including an experienced physiotherapist who can imitate the walking pattern of the elderly and people with disabilities. The results of the state classification can be used to improve fall-prevention control algorithms for walking support systems. The proposed method can also be used for other vision-based classification applications, which require real-time abnormality detection or state recognition.     

Human tracking system based on adaptive multi-feature mean-shift for robot under the double-layer locating mechanism

Human tracking has been a challenging task for robot in the past decades. In this paper, to realize the human following in a cluttered environment, a human tracking system based on adaptive multi-feature mean-shift (AMF-MS) under the double-layer locating mechanism (DLLM) is proposed to solve the problem of distinguishing target, occlusion, and quick turning. The DLLM, considering the course location processing and fine location processing, is designed to estimate the person’s position using the fusion of heterogeneous data. As an ID tag attached on target can be detected by RF antennas, the course locating method can track the target easily and quickly. The Bayes rule is introduced to calculate the probability where the tag exists due to the instability of RF signals. In the fine locating step, the AMF-MS is proposed because it can reduce computational load and represent target by multi-feature histogram function. Meanwhile, we combine extended Kalman filter and AMF-MS to overcome MS’s inability of occlusion. To control the robot following the target person precisely, an intelligent gear shift strategy based on fuzzy control is implemented by analyzing the robot structure. Experiments demonstrate that the proposed approach is robust to handle complex tracking conditions, and show the system has an optimum performance.     

Model reference adaptive impedance control in Cartesian coordinates for physical human–robot interaction

In this paper, a nonlinear model reference adaptive impedance controller is proposed and tested. The controller provides asymptotic tracking of a reference impedance model for the robot end-effector in Cartesian coordinates applicable to rehabilitation robotics or any other human–robot interactions such as haptic systems. The controller uses the parameters of a desired stable reference model which is the target impedance for the robot’s end-effector. It also considers uncertainties in the model parameters of the robot. The asymptotic tracking is proven using Lyapunov stability theorem. Moreover, the adaptation law is proposed in joint space for reducing the complexity of its calculations; however, the controller and the stability proof are all presented in Cartesian coordinates. Using simulations and experiments on a two DOFs robot, the effectiveness of the proposed controller is investigated.     

Noncertainty equivalent adaptive control of robot manipulators without velocity measurements

This paper presents a noncertainty equivalent adaptive motion control scheme for robot manipulators in the absence of link velocity measurements. A new output feedback adaptation algorithm, based on the attractive manifold design approach, is developed. A proportional-integral adaptation is selected for the adaptive parameter estimator to strengthen the passivity of the system. In order to relieve velocity measurements, an observer is designed to estimate the velocities. The controller guarantees semiglobal asymptotic motion tracking and velocity estimation, as well as L_∞ and L₂ bounded parameter estimation error. The effectiveness of the proposed controller is verified by simulations for a two-link robot manipulator and a four-bar linkage. The results are further compared with the earlier certainty-equivalent adaptive partial and full state feedback controller to highlight potential closed-loop performance improvements.