Wail Gueaieb

Trends in the Control Schemes for Bilateral Teleoperation with Time Delay

Lecture Notes in Computer Science, 2011

In the last two decades, an increasing interest has been observed in the development of effective... more In the last two decades, an increasing interest has been observed in the development of effective bilateral teleoperation systems. One of the biggest challenges encountered however is the presence of time delay, which can cause instability and poor transparency to the system. Researchers have been trying to tackle this problem from different angles and various control schemes have been proposed and tested. This paper aims to do an overview of those schemes and to compare them based on their assessment in order to give new researchers a summary of the current state of art. The schemes are divided into four main categories: passivity based, prediction based, sliding-mode control schemes and others. The comparison is based on transparency level, stability requirement, delay type, and delay range.

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Neural network based speed observer for interior permanent magnet synchronous motor drives

Electrical Power & Energy Conference, IEEE, 2009

This paper introduces an artificial neural network (ANN) based observer for accurate speed contro... more This paper introduces an artificial neural network (ANN) based observer for accurate speed control of an interior permanent magnet synchronous machine (IPMSM). A multilayer perception is trained online using back-propagation learning algorithm to estimate the rotor speed. Unlike other observers, no a priori offline training or weights initialization is required. Experiments for different situations highlight the performance of the proposed

Artificial Neural Network Control of a Flexible-Joint Manipulator Under Unstructured Dynamic Uncertainties

2007 International Workshop on Robotic and Sensors Environments, 2007

This paper proposes a position control strategy based on artificial neural networks (ANN) in the ... more 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&amp;amp;amp;#39;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

ANN-based adaptive motion and posture control of an inverted pendulum with unknown dynamics

2009 3rd International Conference on Signals, Circuits and Systems (SCS), 2009

... of an Inverted Pendulum with Unknown Dynamics Hicham Chaoui, Wail Gueaieb, Mustapha CE Yagoub... more

Hybrid Neural Fuzzy Sliding Mode Control of Flexible-Joint Manipulators with Unknown Dynamics

IECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics, 2006

In this paper, a hybrid neural fuzzy control scheme is proposed for the control of flexible-joint... more 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 feedforward 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

A UAV based system for real time flash flood monitoring in desert environments using Lagrangian microsensors

2013 International Conference on Unmanned Aircraft Systems (ICUAS), 2013

ABSTRACT Floods are the most common natural disasters, causing thousands of casualties every year... more ABSTRACT Floods are the most common natural disasters, causing thousands of casualties every year in the world. In particular, flash flood events are particularly deadly because of the short timescales on which they occur. Most casualties could be avoided with advance warning, for which real time monitoring is critical. While satellite-based high resolution weather forecasts can help predict floods to a certain extent, they are not reliable enough, as flood models depend on a large number of parameters that cannot be estimated beforehand. In this article, we present a novel flood sensing architecture to monitor large scale desert hydrological basins surrounding metropolitan areas, based on unmanned air vehicles. The system relies on Lagrangian (mobile) microsensors, that are released by a swarm of UAVs. A preliminary testbed implementing this technology is briefly described, and future research directions and problems are discussed.

Soft computing techniques as applied to expert tuning of PID controllers

Proceedings of the 2000 IEEE International Symposium on Intelligent Control. Held jointly with the 8th IEEE Mediterranean Conference on Control and Automation (Cat. No.00CH37147), 2000

An adaptive fuzzy control approach for cooperative manipulators

Proceeding of the 2001 IEEE International Symposium on Intelligent Control (ISIC '01) (Cat. No.01CH37206), 2001

... Cooperative Manipulators Wail Gueaieb, Fakhri Karray, and Salah Al-Sharhan ... A decentralize... more

Tools of computational intelligence as applied to bandwidth allocation in ATM networks

ICC 2001. IEEE International Conference on Communications. Conference Record (Cat. No.01CH37240), 2001

ABSTRACT This paper presents the application of soft computing-based techniques to the bandwidth ... more ABSTRACT This paper presents the application of soft computing-based techniques to the bandwidth allocation (BA) problem in ATM networks. Efficient bandwidth allocation technique implies effective resources utilization. The fluid flow model has been known to be among the most accurate conventional methods to estimate the bandwidth of a set of connections. However, and due to the computational complexity, such methods have been proven to be inefficient in coping with varying and conflicting bandwidth requirements in ATM networks. To overcome this difficulty, many approximation-based solutions were introduced. Although such solutions are not simple, they nevertheless suffer from possible inaccuracy in estimating the required bandwidth. Soft computing-based bandwidth controllers, such as neural networks and neurofuzzy based controllers, have the capability to solve indeterminate non-linear input-output relations by learning from examples. Applying these techniques to the bandwidth allocation problem in ATM network yields a flexible control mechanism that offers a fundamental trade-off for the accuracy-simplicity dilemma

A hybrid adaptive fuzzy approach for the control of cooperative manipulators

Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292), 2002

ABSTRACT We examine in this article the complex problem of simultaneous position and internal for... more ABSTRACT We examine in this article the complex problem of simultaneous position and internal force control in multiple cooperative manipulator systems. This is done in the presence of unwanted parametric and modeling uncertainties as well as external disturbances. A decentralized adaptive hybrid intelligent control scheme is proposed here. The controller makes use of a multi-input multi-output fuzzy logic engine and a systematic online adaptation mechanism. Unlike conventional adaptive controllers, the proposed one does not require a precise model of the system&amp;#39;s dynamics. The performance of the proposed controller is compared to that of a well known conventional adaptive controller.

Mobile robot navigation using particle swarm optimization and noisy RFID communication

2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, 2008

AbstractAmong the major shortcomings of modern mobile robot navigation systems are their depende... more

A Fuzzy Logic Approach for Indoor Mobile Robot Navigation Using UKF and Customized RFID Communication

Lecture Notes in Computer Science, 2011

... Wiley, Swadlincote (2004) 2. Bekkali, A., Matsumoto, M.: RFID indoor tracking system based on... more

Optimal time-varying P-controller for a class of uncertain nonlinear systems

International Journal of Control, Automation and Systems, 2014

In this manuscript, an optimal time-varying P-controller is presented for a class of continuous-t... more In this manuscript, an optimal time-varying P-controller is presented for a class of continuous-time underactuated nonlinear systems in the presence of process noise associated with systems' inputs. This is a state feedback control strategy where the optimization is performed on a time-varying feedback operator (herein called the feedback control gain). The main goal of the current manuscript is to provide a framework for multi-input multi-output nonlinear systems which yields a satisfactory tracking performance based on the optimal time-varying feedback control gain. Unlike other feedback control techniques that perform dynamic linearization of system models, the proposed time-varying P-controller provides the full-state feedback control to the original nonlinear system model. Hence, this P-controller guarantees global asymptotic statetracking. Furthermore, the bounded system's process noise is taken into consideration to measure the controller's robustness. The proposed P-controller is tested for its nonlinear trajectory tracking and fixed-point stabilization capabilities with two nonholonomic systems in the presence of actuators' noise.

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Experiments on a novel modular cost-effective RFID-based mobile robot navigation system

2007 IEEE International Conference on Systems, Man and Cybernetics, 2007

AbstractMobile robot navigation using an analog signal strength of a Radio Frequency IDentificat... more

Indoor robot navigation through intelligent processing of RFID signal measurements

2010 International Conference on Autonomous and Intelligent Systems, AIS 2010, 2010

ABSTRACT

A novel computational intelligence approach for multiple-point routing in communication networks

IEEE International Conference on Systems, Man and Cybernetics, 2002

ABSTRACT A new evolutionary algorithm for the constrained multiple destination routing problem is... more ABSTRACT A new evolutionary algorithm for the constrained multiple destination routing problem is presented. The constrained multicast problem is characterized by a minimum cost multicast tree and a bounded end-to-end delay. It has been proven that this problem is NP-complete. The proposed algorithm is based on a soft computing technique that integrates in an efficient manner the merits of genetic algorithms and concepts of the competitive learning in the artificial neural networks literature. A population based learning algorithm is utilized, among other techniques, to construct a delay bounded multicast tree. A salient feature of the algorithm is the adaptive learning concept that achieves an efficient trade-off between the exploration and exploitation of the search space.

Maritime air defence firing tactics

the 2014 Seventh IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA), 2014

Dynamics and Control of a Planar Multibody Mobile Robot for Confined Environment Inspection

Journal of Computational and Nonlinear Dynamics, 2014

In this paper, we study the dynamics of an articulated planar mobile robot for confined environme... more In this paper, we study the dynamics of an articulated planar mobile robot for confined environment exploration. The mobile vehicle is composed of n identical modules hitched together with passive revolute joints. Each module has the structure of a four-bar parallel mechanism on a mobile platform. The dynamic model is derived using Lagrange formulation. Computer simulations illustrate the model by addressing a path following problem inside a pipe. The dynamic model presented in this paper is the basis for the design of motion control algorithms that encode energy optimization and sensor performance maximization.

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