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The present paper uses the learning ability of neural networks (NN) for nonlinear systems in order to design a controller for trajectory tracking problems in wheeled mobile robots that have their kinematic constraints violated. The singular perturbation approach is used to highlight the presence of uncertainties related to the violation of the kinematic constraints and propose an alternative global...
This paper presents a two-stage optimization approach to mitigate the rapid voltage fluctuations and minimize the power losses of distribution systems due to the high penetration of photovoltaic (PV) generation. The first stage is a day-ahead optimal strategy which aims to minimize the total voltage deviations and power losses within the constraints of the daily maximum allowable number of operations...
In this paper, a neural network based adaptive state feedback control scheme is proposed to solve the problem of trajectory tracking in the joint space for robotic manipulators with the presence of high uncertainty in the system parameters. First nonlinear behavior of the robot is approximately eliminated by applying a linearizing control, the closed loop dynamics is stabilized using static compensator...
The paper is devoted to consider problems of project management in conditions of internal and external uncertainty in project state assessment. It is shown, that uncertainty of project state assessments influences negatively on the project and on the organized system, in which projects realize. The mechanisms of formalized project state assessment with means of artificial neural networks are proposed...
A dynamic neural network (DNN)-based observer design is presented, which amalgamates an adaptive neural network-based technique with a finite-time sliding mode estimation method. The proposed observer design is motivated by practical quadrotor unmanned aerial vehicle tracking control applications, where direct sensor measurements of translational and rotational rates are not available for feedback...
This paper addresses neural network (NN) control of a lower limb exoskeleton for rehabilitation. Both the interaction between human and exoskeleton and external disturbances are considered. The controller is developed based on a combined scheme of repetitive learning control (RLC) and neural networks (NN), where RLC is used to learn periodic uncertainties (the interaction between human and exoskeleton)...
Land use and land cover classification of remote sensing data is useful in taking relevant decisions of an area. There are various types of automated methodologies which are used for the classification of remote sensing images. Neural networks (NNs) are such systems which are known for their ability to classify the data with highly overlapping classes. But, NNs face problems like uncertainty in the...
Within the complex driving environment, progress in autonomous vehicles is supported by advances in sensing and data fusion. Safe and robust autonomous driving can only be guaranteed provided that vehicles and infrastructure are fully aware of the driving scenario. This paper proposes a methodology for feature uncertainty prediction for sensor fusion by generating neural network surrogate models directly...
Currently, there are many approaches designed for the task of detecting communities in social networks. Among them, some methods only consider the topological graph structure, while others can take use of both the graph structure and the node attributes. In real-world networks, there are many uncertain and noisy attributes in the graph. In this paper, we will present how we can detect communities...
Early detection of small faults in closed-loop systems is a challenging issue in the fault diagnosis literature. The effect of faults in closed-loop systems will be obscured by a robust feedback control, especially when the controller is coupled with nonlinear uncertainty. In this paper, an approach for rapid detection for small faults in a class of closed-loop uncertain systems is proposed based...
In this paper, an approach for rapid fault detection for a class of nonlinear sampled-data systems is proposed. Firstly, a learning estimator is constructed to capture the unknown system dynamics effects in sampled-data systems. The key issue in the learning process is that partial neural weights will converge into their optimal values based on the deterministic learning theory. Then a knowledge bank...
This paper addresses the state constrained control problem of a class of nonlinear pure-feedback systems in the presence of unknown dynamics. Minimal learning parameter technique based neural networks are used to estimate the model uncertainties, thus the amount of online updated parameters is largely reduced. Filtered signals are introduced to avoid algebraic loop problems encountered in the implementation...
In this paper, a new RBF based sliding mode controller is proposed for the joint trajectory tracking of robotic manipulators with uncertainties and disturbances. A RBF neural network is employed to approximate the nonlinear uncertainties in the mode, adaptive laws of the parameters are established, and the approximation error is compensated by designing a sliding mode controller, in which a generalized...
A dynamic neural network (DNN)-based observer design is presented, which amalgamates an adaptive neural network-based technique with a sliding mode estimation method. The proposed observer design is motivated by practical quadrotor tracking control applications, where direct sensor measurements of translational and rotational rates are not available for feedback. While sliding mode estimation strategies...
A novel robust sliding-mode controller using neural networks (NNs) is given for trajectory tracking control of permanent magnet spherical actuator (PMSA) with external disturbance and system model errors. The controller is established including sliding-mode scheme and the neural networks. The radial basis function (RBF) neural networks are chosen to approximate the unknown model and uncertainty, as...
Automatic speech recognition (ASR) in noisy environments remains a challenging goal. Recently, the idea of estimating the uncertainty about the features obtained after speech enhancement and propagating it to dynamically adapt deep neural network (DNN) based acoustic models has raised some interest. However, the results in the literature were reported on simulated noisy datasets for a limited variety...
A controller is developed for a three degrees-of-freedom surface marine craft where both the rigid body and hydrodynamic parameters are unknown. A Lyapunov-based analysis is presented to show the closed loop system is globally exponentially stable and the uncertain parameters are identified exponentially without the requirement of persistence of excitation. Simulation results are provided to validate...
Forecasts combinations normally use point forecasts that were obtained from different models or sources ([1], [2],[3]). This paper explores the incorporation of internal structure parameters of feed-forward neural network (NN) models as anapproach to combine their forecasts via ensembles. First, the generated NN models that could be part of the ensembles are subjectto a clustering algorithm that uses...
In this paper an adaptive control law is designed for formation control of underactuated autonomous ground vehicles based on the leader-follower approach and kinematics equations. The follower vehicles have limited knowledge about the leader's states. The unknown term containing the velocity information of the leader is estimated using neural networks with online adaptive weight tuning laws. The decentralized...
The leak localisation methodologies based on data and models are affected by both uncertainties in the model and in the measurements. This uncertainty should be quantified so that its effect on the localisation methods performance can be estimated. In this paper, a model-based leak localisation methodology is applied to a real District Metered Area using synthetic data. In the generation process of...
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