ANN-based adaptive control of robotic manipulators with friction and joint elasticity

This paper proposes a control strategy based on artificial neural networks (ANNs) for a positioning system with a flexible transmission element, taking into account Coulomb friction for both motor and load, and using a variable learning rate for adaptation to parameter changes and accelerate convergence. A control structure consists of a feedforward ANN that approximates the manipulator's inverse dynamical model, an ANN feedback control law, a reference model, and the adaptation process of the ANNs with a variable learning rate. A supervisor that adapts the neural network's learning rate and a rule-based supervisor for online adaptation of the parameters of the reference model are proposed to maintain the stability of the system for large variations of load parameters. Simulation results highlight the performance of the controller to compensate the nonlinear friction terms, particularly Coulomb friction, and flexibility, and its robustness to the load and drive motor inertia parameter changes. Internal stability, which is a potential problem in such a system, is also verified. The controller is suitable for DSP and very large scale integration implementation and can be used to improve static and dynamic performances of electromechanical systems

Artificial neural network control of a flexible-joint manipulator under unstructured dynamic uncertainties

This paper proposes a position control strategy based on Artificial Neural Networks (ANN) in the face of structured and unstructured dynamic uncertainties. The control structure consists of a feedforward multilayer perceptron (MLP) to approximate the manipulator's inverse dynamics online, a feedback radial basis function (RBF) neural network to compensate for the residual errors, and a reference model that defines the desired error dynamics. The online adaptation of the RBF neural network is is accomplished through two methods: (i) the Least Mean Squares (LMS), and (ii) the Recursive Least Squares (RLS) algorithms. A comparison study is conducted to evaluate the efficiency of both algorithms on the tracking ability of the proposed control scheme. Simulation results highlight the performance of the proposed control structures in compensating for the highly nonlinear unknown dynamics of the manipulator and its robustness in the presence of model imperfections. @2007 IEEE

Hybrid neural fuzzy sliding mode control of flexible-joint manipulators with unknown dynamics

In this paper, a hybrid neural fuzzy control scheme is proposed for the control of flexible-joint robot manipulators with unknown dynamics. The control strategy is based on a feed-forward artificial neural network to partially approximate the manipulator's inverse dynamics. A fuzzy sliding mode feedback controller is also used for the online adaptation of the neural network-based controller. Simulation results of various scenarios highlight the performance and stability of the proposed controller in compensating for the highly nonlinear unknown dynamics of the manipulator under different dynamical conditions and external disturbances

3D scar segmentation from LGE-MRI using a continuous max-flow method

Myocardial scar, a non-viable tissue which forms in the myocardium due to insufficient blood supply to the heart muscle, is one of the leading causes of life-threatening heart disorders, including arrhythmias. Accurate reconstruction of myocardial scar geometry is important for diagnosis and clinicial prognosis of the patients with ischemic cardiomyopathy. The 3D late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) is increasingly being investigated for assessing myocardial tissue viability. For applications, such as computational modeling of cardiac electrophysiology aimed at stratifying patient risk for post-infarction arrhythmias, segmentation and reconstruction of the intact geometry of scar is required. However, manual analysis and segmentation of myocardial scar from 3D LGE-MRI is a tedious task. Therefore, semi-automated and fully-automated segmentation algorithms are highly desirable in a clinical setting. In this study, we developed an approach to segment the myocardial scar from 3D LGE-MR images using a continuous max-flow (CMF) method. The data term comprised of a distribution matching term for scar and normal myocardium and a boundary smoothness term for the scar boundaries. The region-of-interest for the scar segmentation is constrained, using manually segmented myocardium. We evaluated our CMF method for accuracy by comparing it to manual scar delineations using 3D LGE-MR images of 34 patients. We compare the results of the CMF technique to ones by conventional full-width-at-half-maximum (FWHM) and signal-threshold-to-reference-mean (STRM) methods. The CMF method yielded a Dice similarity coefficient (DSC) of 72±18% and an absolute volume error (V E) of 15.42±14.1 cm3. Overall, the CMF method outperformed the state-of-the-art methods for all reported metrics in 3D scar segmentation except for the recall value which STRM 2-SD perf

Comparison of myocardial scar geometries from 2D and 3D LGE-MRI

Myocardial scar geometry may influence the sensitivity of predicting risk for ventricular tachycardia (VT) using computational models of the heart. This study aims to compare the differences in reconstructed geometry of scar generated using two-dimensional (2D) versus three-dimensional (3D) late gadolinium-enhanced magnetic resonance (LGE-MR) images. We used a retrospectively-acquired dataset of 17 patients with myocardial scar who underwent both 2D and 3D LGE-MR imaging. We segmented the scar manually in both 2D and 3D LGE-MRI using a multi-planar image processing software. We then reconstructed the 2D scar segmentation boundaries from 2D LGE-MRI to 3D surfaces using a LogOdds-based interpolation method. Finally, we assessed the 3D models of scar in both 3D and 2D-reconstructed techniques using several shape and volume metrics such as, fractal dimensions, number of connected components, mean scar volume, and normalized scar volume. The higher fractal dimension resulted for 3D may indicate that the 3D LGE-MRI produces a more complex surface geometry by better capturing the intact geometry of the scar. The 2D LGE-MRI produced a larger normalized scar volume (19.48±10 cm3) than the 3D LGE-MRI (10.92±7.12 cm3). We also provided a statistical analysis on the scar volume differences acquired from 2D and 3D LGE-MRI