341,496 research outputs found

    MRAC Revisited: Guaranteed Performance with Reference Model Modification

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    This paper presents modification of the conventional model reference adaptive control (MRAC) architecture in order to achieve guaranteed transient performance both in the output and input signals of an uncertain system. The proposed modification is based on the tracking error feedback to the reference model. It is shown that approach guarantees tracking of a given command and the ideal control signal (one that would be designed if the system were known) not only asymptotically but also in transient by a proper selection of the error feedback gain. The method prevents generation of high frequency oscillations that are unavoidable in conventional MRAC systems for large adaptation rates. The provided design guideline makes it possible to track a reference command of any magnitude form any initial position without re-tuning. The benefits of the method are demonstrated in simulations

    Adaptive Gain Control for a Two-Axis, H-Frame-Type, Positioning System

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    XY gantry systems play an important role in many applications in diverse industries, where they are used to position a part or tool along the xy plane within the working area of the system. The increased demand for enhanced performance and low cost of XY gantry systems has driven research to develop alternative structural designs and improve their capabilities. A two-axis, parallel H-frame XY positioning system (H-Bot) is of increasing interest as a candidate for development due to its low number of moving parts, lightweight, low cost and speed of the system. However, the system has an uncertainty of cart or end-effector position when moving at high speed because of the friction and flexibility of the elastic timing belt. The H-bot developed here using an adaptive gain control showed a good repeatability and improved accuracy, reducing the root mean square error between the desired and the actual trajectory of 32.7% and 53.2% on the x-axis and y-axis, respectively, for drawing a 80 mm diameter circle in 36 seconds.XY gantry systems play an important role in many applications in diverse industries, where they are used to position a part or tool along the xy plane within the working area of the system. The increased demand for enhanced performance and low cost of XY gantry systems has driven research to develop alternative structural designs and improve their capabilities. A two-axis, parallel H-frame XY positioning system (H-Bot) is of increasing interest as a candidate for development due to its low number of moving parts, lightweight, low cost and speed of the system. However, the system has an uncertainty of cart or end-effector position when moving at high speed because of the friction and flexibility of the elastic timing belt. The H-bot developed here using an adaptive gain control showed a good repeatability and improved accuracy, reducing the root mean square error between the desired and the actual trajectory of 32.7% and 53.2% on the x-axis and y-axis, respectively, for drawing a 80 mm diameter circle in 36 seconds

    Semi-Adaptive Control Systems on Self-Balancing Robot using Artificial Neural Networks

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    A self-balancing type of robot works on the principle of maintaining the balance of the load's position to remains in the center. As a consequence of this principle, the driver can go forward reverse the vehicle by leaning in a particular direction. One of the factors affecting the control model is the weight of the driver. A control system that has been designed will not be able to balance the system if the driver using the vehicle exceeds or less than the predetermined weight value. The main objective of the study is to develop a semi-adaptive control system by implementing an Artificial Neural Network (ANN) algorithm that can estimate the driver's weight and use this information to reset the gain used in the control system. The experimental results show that the Artificial Neural Network can be used to estimate the weight of the driver's body by using 50-ms-duration of tilt sensor data to categorize into three defined classes that have been set. The ANN algorithm provides a high accuracy given by the results of the confusion matrix and the precision calculations, which show 99%.A self-balancing type of robot works on the principle of maintaining the balance of the load's position to remains in the center. As a consequence of this principle, the driver can go forward reverse the vehicle by leaning in a particular direction. One of the factors affecting the control model is the weight of the driver. A control system that has been designed will not be able to balance the system if the driver using the vehicle exceeds or less than the predetermined weight value. The main objective of the study is to develop a semi-adaptive control system by implementing an Artificial Neural Network (ANN) algorithm that can estimate the driver's weight and use this information to reset the gain used in the control system. The experimental results show that the Artificial Neural Network can be used to estimate the weight of the driver's body by using 50-ms-duration of tilt sensor data to categorize into three defined classes that have been set. The ANN algorithm provides a high accuracy given by the results of the confusion matrix and the precision calculations, which show 99%

    Intra-pixel gain variations and high-precision photometry with the Infrared Array Camera (IRAC)

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    The Infrared Array Camera (IRAC) on the Spitzer Space Telescope has been used to measure < 10^(-4) temporal variations in point sources (such as transiting extrasolar planets) at 3.6 and 4.5 μm. Due to the under-sampled nature of the PSF, the warm IRAC arrays show variations of as much as 8% in sensitivity as the center of the PSF moves across a pixel due to normal spacecraft pointing wobble and drift. These intra-pixel gain variations are the largest source of correlated noise in IRAC photometry. Usually this effect is removed by fitting a model to the science data themselves (self-calibration), which could result in the removal of astrophysically interesting signals. We describe a new technique for significantly reducing the gain variations and improving photometric precision in a given observation, without using the data to be corrected. This comprises: (1) an adaptive centroiding and repositioning method ("Peak-Up") that uses the Spitzer Pointing Control Reference Sensor (PCRS) to repeatedly position a target to within 0.1 IRAC pixels of an area of minimal gain variation; and (2) the high-precision, high-resolution measurement of the pixel gain structure using non-variable stars. We show that the technique currently allows the reduction of correlated noise by almost an order of magnitude over raw data, which is comparable to the improvement due to self-calibration. We discuss other possible sources of correlated noise, and proposals for reducing their impact on photometric precision

    A low power clock generator with adaptive inter-phase charge balancing for variability compensation in 40-nm CMOS

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    Power dissipation besides chip area is still one main optimization issue in high performance CMOS design. Regarding high throughput building blocks for digital signal processing architectures which are optimized down to the physical level a complementary two-phase clocking scheme (CTPC) is often advantageous concerning ATE-efficiency. The clock system dissipates a significant part of overall power up to more than 50% in some applications. <br><br> One efficient power saving strategy for CTPC signal generation is the charge balancing technique. To achieve high efficiency with this approach a careful optimization of timing relations within the control is inevitable. <br><br> However, as in modern CMOS processes device variations increase, timing relations between sensitive control signals can be affected seriously. In order to compensate for the influence of global and local variations in this work, an adaptive control system for charge balancing in a CTPC generator is presented. An adjustment for the degree of charge recycling is performed in each clock cycle. In the case of insufficient recycling the delay elements which define duration and timing position of the recycling pulse are corrected by switchable timing units. <br><br> In a benchmark with the conventional clock generation system, a power reduction gain of up to 24.7% could be achieved. This means saving in power of more than 12% for a complete number-crunching building block

    Experimental comparison of parameter estimation methods in adaptive robot control

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    In the literature on adaptive robot control a large variety of parameter estimation methods have been proposed, ranging from tracking-error-driven gradient methods to combined tracking- and prediction-error-driven least-squares type adaptation methods. This paper presents experimental data from a comparative study between these adaptation methods, performed on a two-degrees-of-freedom robot manipulator. Our results show that the prediction error concept is sensitive to unavoidable model uncertainties. We also demonstrate empirically the fast convergence properties of least-squares adaptation relative to gradient approaches. However, in view of the noise sensitivity of the least-squares method, the marginal performance benefits, and the computational burden, we (cautiously) conclude that the tracking-error driven gradient method is preferred for parameter adaptation in robotic applications

    Design and Development of an Affordable Haptic Robot with Force-Feedback and Compliant Actuation to Improve Therapy for Patients with Severe Hemiparesis

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    The study describes the design and development of a single degree-of-freedom haptic robot, Haptic Theradrive, for post-stroke arm rehabilitation for in-home and clinical use. The robot overcomes many of the weaknesses of its predecessor, the TheraDrive system, that used a Logitech steering wheel as the haptic interface for rehabilitation. Although the original TheraDrive system showed success in a pilot study, its wheel was not able to withstand the rigors of use. A new haptic robot was developed that functions as a drop-in replacement for the Logitech wheel. The new robot can apply larger forces in interacting with the patient, thereby extending the functionality of the system to accommodate low-functioning patients. A new software suite offers appreciably more options for tailored and tuned rehabilitation therapies. In addition to describing the design of the hardware and software, the paper presents the results of simulation and experimental case studies examining the system\u27s performance and usability
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