Disturbance Observer-based Robust Control and Its Applications: 35th Anniversary Overview

Disturbance Observer has been one of the most widely used robust control tools since it was proposed in 1983. This paper introduces the origins of Disturbance Observer and presents a survey of the major results on Disturbance Observer-based robust control in the last thirty-five years. Furthermore, it explains the analysis and synthesis techniques of Disturbance Observer-based robust control for linear and nonlinear systems by using a unified framework. In the last section, this paper presents concluding remarks on Disturbance Observer-based robust control and its engineering applications.Comment: 12 pages, 4 figure

Reliable and robust detection of freezing of gait episodes with wearable electronic devices

A wearable wireless sensing system for assisting patients affected by Parkinson's disease is proposed. It uses integrated micro-electro-mechanical inertial sensors able to recognize the episodes of involuntary gait freezing. The system operates in real time and is designed for outdoor and indoor applications. Standard tests were performed on a noticeable number of patients and healthy persons and the algorithm demonstrated its reliability and robustness respect to individual specific gait and postural behaviors. The overall performances of the system are excellent with a specificity higher than 97%

A survey on fractional order control techniques for unmanned aerial and ground vehicles

In recent years, numerous applications of science and engineering for modeling and control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems based on fractional calculus have been realized. The extra fractional order derivative terms allow to optimizing the performance of the systems. The review presented in this paper focuses on the control problems of the UAVs and UGVs that have been addressed by the fractional order techniques over the last decade

A Learning-Based Framework for Two-Dimensional Vehicle Maneuver Prediction over V2V Networks

Situational awareness in vehicular networks could be substantially improved utilizing reliable trajectory prediction methods. More precise situational awareness, in turn, results in notably better performance of critical safety applications, such as Forward Collision Warning (FCW), as well as comfort applications like Cooperative Adaptive Cruise Control (CACC). Therefore, vehicle trajectory prediction problem needs to be deeply investigated in order to come up with an end to end framework with enough precision required by the safety applications' controllers. This problem has been tackled in the literature using different methods. However, machine learning, which is a promising and emerging field with remarkable potential for time series prediction, has not been explored enough for this purpose. In this paper, a two-layer neural network-based system is developed which predicts the future values of vehicle parameters, such as velocity, acceleration, and yaw rate, in the first layer and then predicts the two-dimensional, i.e. longitudinal and lateral, trajectory points based on the first layer's outputs. The performance of the proposed framework has been evaluated in realistic cut-in scenarios from Safety Pilot Model Deployment (SPMD) dataset and the results show a noticeable improvement in the prediction accuracy in comparison with the kinematics model which is the dominant employed model by the automotive industry. Both ideal and nonideal communication circumstances have been investigated for our system evaluation. For non-ideal case, an estimation step is included in the framework before the parameter prediction block to handle the drawbacks of packet drops or sensor failures and reconstruct the time series of vehicle parameters at a desirable frequency

The psychophysics of absolute threshold and signal duration: A probabilistic approach

The absolute threshold for a tone depends on its duration; longer tones have lower thresholds. This effect has traditionally been explained in terms of ?temporal integration? involving the summation of energy or perceptual information over time. An alternative probabilistic explanation of the process is formulated in terms of simple equations that predict not only the time=duration dependence but also the shape of the psychometric function at absolute threshold. It also predicts a tight relationship between these two functions. Measurements made using listeners with either normal or impaired hearing show that the probabilistic equations adequately fit observed threshold-duration functions and psychometric functions. The mathematical formulation implies that absolute threshold can be construed as a two-valued function: (a) gain and (b) sensory threshold, and both parameters can be estimated from threshold-duration data. Sensorineural hearing impairment is sometimes associated with a smaller threshold=duration effect and sometimes with steeper psychometric functions. The equations explain why these two effects are expected to be linked. The probabilistic approach has the potential to discriminate between hearing deficits involving gain reduction and those resulting from a raised sensory threshold

Non-linear echo cancellation - a Bayesian approach

Echo cancellation literature is reviewed, then a Bayesian model is introduced and it is shown how how it can be used to model and fit nonlinear channels. An algorithm for cancellation of echo over a nonlinear channel is developed and tested. It is shown that this nonlinear algorithm converges for both linear and nonlinear channels and is superior to linear echo cancellation for canceling an echo through a nonlinear echo-path channel

Use of soil moisture information in yield models

There are no author-identified significant results in this report

Adaptive voting: an empirical analysis of participation and choice

Dynamic models of learning and adaptation have provided realistic predictions in terms of voting behavior. This study aims at contributing to their empirical verification by investigating voting behavior in terms of participation as well as choice. We test through panel data methods an outcome-based learning mechanism based on the following assumptions: (a) people expect that the party they do not support will be unable to bring economic improvements; (b) they receive a feedback whose impact depends on the consistency between their last voting behavior and personal economic improvements (or worsening) from the last election; (c) they tend to discard choices associated to an inconsistent feedback. Results show that feedbacks of this sort affect persistence of voting behavior, interpreted as participation and voting choice. Age and trade union affiliation reinforce this adaptive behavior. The analysis also investigates the intensity of the learning feedback, differentiating between a strong inconsistent feedback, which leads to a vote switch in favor of the opponent party, and a weak inconsistent feedback, which induces just abstention rather than a vote switch.voting, bounded rationality, learning, political accountability