Feedforward Control of Shape Memory Alloy Actuators Using Fuzzy-Based Inverse Preisach Model

This brief investigates a possible application of the inverse Preisach model in combination with the feedforward and feedback control strategies to control shape memory alloy actuators. In the feedforward control design, a fuzzy-based inverse Preisach model is used to compensate for the hysteresis nonlinearity effect. An extrema input history and a fuzzy inference is utilized to replace the inverse classical Preisach model. This work allows for a reduction in the number of experimental parameters and computation time for the inversion of the classical Preisach model. A proportional-integral-derivative (PID) controller is used as a feedback controller to regulate the error between the desired output and the system output. To demonstrate the effectiveness of the proposed controller, real-time control experiment results are presented.

Optimal proportional-integral-derivative control of shape memory alloy actuators using genetic algorithm and the Preisach model

Shape memory alloy (SMA) actuators, which have the ability to return to a predetermined shape when heated, have many potential applications in aeronautics, surgical tools, robotics, and so on. Although the number of applications is increasing, there has been limited success in precise motion control owing to the hysteresis effect of these smart actuators. The present paper proposes an optimization of the proportional-integral-derivative (PID) control method for SMA actuators by using genetic algorithm and the Preisach hysteresis model.

Position control of shape memory alloy actuators using self tuning fuzzy PID controller

Shape Memory Alloy (SMA) actuators, which have the ability to return to a predetermined shape when heated, have many potential applications such as aeronautics, surgical tools, robotics and so on. Although the conventional PID controller can be used with slow response systems, there has been limited success in precise motion control of SMA actuators, since the systems are disturbed by unknown factors beside their inherent nonlinear hysteresis and changes in the surrounding environment of the systems. This paper presents a new development of a SMA position control system by using a self-tuning fuzzy PID controller. This control algorithm is used by tuning the parameters of the PID controller thereby integrating fuzzy inference and producing a fuzzy adaptive PID controller, which can then be used to improve the control performance of nonlinear systems. The experimental results of position control of SMA actuators using conventional and self-tuning fuzzy PID controllers are both included in this paper.

Modelling and Control of Shape Memory Alloy Actuators by using Preisach Model, Genetic Algorithm and Fuzzy Logic

Shapememoryalloy (SMA) actuators, which have the ability to return to a predetermined shape when heated, have many potential applications in aeronautics, surgical tools, robotics and so on. Nonlinearity hysteresis effects existing in SMA actuators present a problem in the motion control of these smart actuators. This paper investigates the control problem of SMA actuators in both simulation and experiment. In the simulation, the numerical Preisachmodel with geometrical interpretation is used for hysteresis modeling of SMA actuators. This model is then incorporated in a closed loop PID control strategy. The optimal values of PID parameters are determined by using geneticalgorithm to minimize the mean squared error between desired output displacement and simulated output. However, the control performance is not good compared with the simulation results when these parameters are applied to the real SMA control since the system is disturbed by unknown factors and changes in the surrounding environment of the system. A further automated readjustment of the PID parameters using fuzzylogic is proposed for compensating the limitation. To demonstrate the effectiveness of the proposed controller, real time control experiment results are presented.

Dual task performance of working memory and postural control in major depressive disorder

Objective: Previous studies with patients diagnosed with Major Depressive Disorder (MDD) revealed deficits in working memory and executive functions. In the present study we investigated whether patients with MDD have the ability to allocate cognitive resources in dual task performance of a highly challenging cognitive task (working memory) and a task that is seemingly automatic in nature (postural control). Method: Fifteen young (18–35 years old) patients with MDD and 24 healthy age-matched controls performed a working memory task and two postural control tasks (standing on a stable or on a moving platform) both separately (single task) and concurrently (dual task). Results: Postural stability under single task conditions was similar in the two groups, and in line with earlier studies, MDD patients recalled fewer working memory items than controls. To equate working memory challenges for patients and controls, task difficulty (number of items presented) in dual task was individually adjusted such that accuracy of working memory performance was similar for the two groups under single task conditions. Patients showed greater postural instability in dual task performance on the stable platform, and more importantly when posture task difficulty increased (moving platform) they showed deficits in both working memory accuracy and postural stability compared with healthy controls. Conclusions: We interpret our results as evidence for executive control deficits in MDD patients that affect their task coordination. In multitasking, these deficits affect not only cognitive but also sensorimotor task performance.

The role of working memory capacity in multiple-cue probability learning

Multiple-cue probability learning (MCPL) involves learning to predict a criterion when outcome feedback is provided for multiple cues. A great deal of research suggests that working memory capacity (WMC) is involved in a wide range of tasks that draw on higher level cognitive processes. In three experiments, we examined the role of WMC in MCPL by introducing measures of working memory capacity, as well as other task manipulations. While individual differences in WMC positively predicted performance in some kinds of multiple-cue tasks, performance on other tasks was entirely unrelated to these differences. Performance on tasks that contained negative cues was correlated with working memory capacity, as well as measures of explicit knowledge obtained in the learning process. When the relevant cues predicted positively, however, WMC became irrelevant. The results are discussed in terms of controlled and automatic processes in learning and judgement. © 2011 The Experimental Psychology Society.

Modified Information Criteria and Selection of Long Memory Time Series Models

The problem of model selection of a univariate long memory time series is investigated once a semi parametric estimator for the long memory parameter has been used. Standard information criteria are not consistent in this case. A Modified Information Criterion (MIC) that overcomes these difficulties is introduced and proofs that show its asymptotic validity are provided. The results are general and cover a wide range of short memory processes. Simulation evidence compares the new and existing methodologies and empirical applications in monthly inflation and daily realized volatility are presented.

Country-of-origin effects on Russian wine consumers

In recent years, Russia has experienced significant economic growth. The wine industry is among those most affected by increases in disposable income. As a consequence, Russian wine importers have widened the range at the upper end of the quality spectrum. In the current scenario, some key questions arise concerning consumer attitudes toward wine and the way it is perceived in this evolving market. This article attempts to investigate such concerns through a choice experiment approach conducted by means of a questionnaire-based survey submitted to 388 Russian households located in the country's three largest cities (Moscow, Saint Petersburg, and Novosibirsk). In the experiment, respondents were asked to choose their favorite wine among seven dry red wines. The stated choices are analyzed using a random utility model to obtain an estimation of the price effect through a triangular distribution. Our results indicate the presence of three distinct market segments in the Russian wine market: a segment with only high-quality, highly priced Italian and French wines, a medium-quality segment currently limited to Spanish wines, and a much lower quality segment of wines in which demand for alcohol is essentially satisfied.