This article surveyed the major results in iterative learning control (ILC) analysis and design over the past two decades. Problems in stability, performance, learning transient behavior, and robustness were discussed along with four design techniques that have emerged as among the most popular. The content of this survey was selected to provide the reader with a broad perspective of the important ideas, potential, and limitations of ILC. Indeed, the maturing field of ILC includes many results and learning algorithms beyond the scope of this survey. Though beginning its third decade of active research, the field of ILC shows no sign of slowing down.

A survey of iterative learning control

Policy search methods can allow robots to learn control policies for a wide range of tasks, but practical applications of policy search often require hand-engineered components for perception, state estimation, and low-level control. In this paper, we aim to answer the following question: does training the perception and control systems jointly end-to-end provide better performance than training each component separately? To this end, we develop a method that can be used to learn policies that map raw image observations directly to torques at the robot's motors. The policies are represented by deep convolutional neural networks (CNNs) with 92,000 parameters, and are trained using a guided policy search method, which transforms policy search into supervised learning, with supervision provided by a simple trajectory-centric reinforcement learning method. We evaluate our method on a range of real-world manipulation tasks that require close coordination between vision and control, such as screwing a cap onto a bottle, and present simulated comparisons to a range of prior policy search methods.

/pdf/end-to-end-training-of-deep-visuomotor-policies-1vqd5uo50d.pdf

End-to-end training of deep visuomotor policies

This tutorial/survey paper: (1) provides a concise point of departure for researchers and practitioners alike wishing to assess the current state of the art in the control and monitoring of civil engineering structures; and (2) provides a link between structural control and other fields of control theory, pointing out both differences and similarities, and points out where future research and application efforts are likely to prove fruitful. The paper consists of the following sections: section 1 is an introduction; section 2 deals with passive energy dissipation; section 3 deals with active control; section 4 deals with hybrid and semiactive control systems; section 5 discusses sensors for structural control; section 6 deals with smart material systems; section 7 deals with health monitoring and damage detection; and section 8 deals with research needs. An extensive list of references is provided in the references section.

Structural control: past, present, and future

Thank you very much for downloading biomechanics and motor control of human movement. Maybe you have knowledge that, people have search hundreds times for their favorite books like this biomechanics and motor control of human movement, but end up in infectious downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they juggled with some malicious virus inside their laptop.

Biomechanics And Motor Control Of Human Movement

Purpose—The aim of this guideline is to provide a synopsis of best clinical practices in the rehabilitative care of adults recovering from stroke. Methods—Writing group members were nominated by th...

Guidelines for Adult Stroke Rehabilitation and Recovery: A Guideline for Healthcare Professionals from the American Heart Association/American Stroke Association

Neural networks for control systems: a survey

A novel technique, directly using artificial neural networks, is proposed for the adaptive control of nonlinear systems. The ability of neural networks to model arbitrary nonlinear functions and their inverses is exploited. The use of nonlinear function inverses raises questions of the existence of the inverse operators. These are investigated and results are given characterising the invertibility of a class of nonlinear dynamical systems. The control structure used is internal model control. It is used to directly incorporate networks modelling the plant and its inverse within the control strategy. The potential of the proposed method is demonstrated by an example.

Neural networks for nonlinear internal model control

Background In the orthopaedic literature, there is a wide range of clinical outcome measurement tools that have been used in evaluating foot and ankle procedures, disorders, and outcomes, with no broadly accepted consensus as to which tools are preferred. The purpose of this study was to determine the frequency and distribution of the various outcome instruments used in the foot and ankle literature, and to identify trends for use of these instruments over time. Methods We conducted a systematic review of all original clinical articles reporting on foot and/or ankle topics in six orthopaedic journals over a ten-year period (2002 to 2011). All clinical patient-reported outcome rating instruments used in these articles were recorded, as were study date, study design, clinical topic, and level of evidence. Results A total of 878 clinical foot and ankle articles that used at least one patient-reported outcome measure were identified among 16,513 total articles published during the ten-year period. There were 139 unique clinical outcome scales used, and the five most popular scales (as a percentage of foot/ankle outcome articles) were the American Orthopaedic Foot & Ankle Society (AOFAS) scales (55.9%), visual analog scale (VAS) for pain (22.9%), Short Form-36 (SF-36) Health Survey (13.7%), Foot Function Index (FFI) (5.5%), and American Academy of Orthopaedic Surgeons (AAOS) outcomes instruments (3.3%). The majority of articles described Level-IV studies (70.1%); only 9.4% reported Level-I studies. Conclusions A considerable variety of outcome measurement tools are used in the foot and ankle clinical literature, with a small proportion used consistently. The AOFAS scales continue to be used at a high rate relative to other scales that have been validated. Data from the present study underscore the need for a paradigm shift toward the use of consistent, valid, and reliable outcome measures for studies of foot and ankle procedures and disorders. It is not clear which existing validated outcome instruments will emerge as widely used and clinically meaningful. Clinical relevance These data support the need for a paradigm shift toward the consistent use of valid and reliable outcome measures for foot and ankle clinical research.

Use of patient-reported outcome measures in foot and ankle research.

Background
In 2012, the American Orthopaedic Foot & Ankle Society® established a national network for collecting and sharing data on treatment outcomes and improving patient care. One of the network’s initiatives is to explore the use of computerized adaptive tests (CATs) for patient-level outcome reporting.

/pdf/validation-of-promis-physical-function-computerized-adaptive-2x760vmpjs.pdf

Validation of PROMIS ® Physical Function Computerized Adaptive Tests for Orthopaedic Foot and Ankle Outcome Research

This paper describes a nonlinear control structure known as a local controller network. The structure consists of a weighted combination of a number of individual controllers, each of which is valid locally in the state space of the plant. Local controller designs are based upon local models valid in operating regimes which do not necessarily contain any physical equilibria. Consequently, the transient performance can be improved. Some 'scheduling' variables determine the current operating regime, and a validity function is assigned to each local controller. A 'feedforward' component may be used in each local controller in order to compensate directly for the operating-point-dependent model offsets. The application of the local controller network approach to a nonlinear control problem, that of longitudinal vehicle dynamics control, is described. A stability analysis for the discrete-time local controller network is given in this paper and the results are compared with known theoretical guidelines for rel...

Kenneth J. Hunt

Papers

Neural networks for control systems: a survey

Neural networks for nonlinear internal model control

Use of patient-reported outcome measures in foot and ankle research.

Validation of PROMIS ® Physical Function Computerized Adaptive Tests for Orthopaedic Foot and Ankle Outcome Research

Design and analysis of gain-scheduled control using local controller networks