| All publications sorted by year |
| 2008 |
| 2007 |
| This article presents a decentralized control scheme for the complex problem of simultaneous position and internal force control in cooperative multiple manipulator systems. The proposed controller is composed of a sliding mode control term and a force robustifying term to simultaneously control the payload's position/orientation as well as the internal forces induced in the system. This is accomplished independently of the manipulators dynamics. Unlike most controllers that do not require prior knowledge of the manipulators dynamics, the suggested controller does not use fuzzy logic inferencing and is computationally inexpensive. Using a Lyapunov stability approach, the controller is proven to be robust in the face of varying system's dynamics. The payload's position/orientation and the internal force errors are also shown to asymptotically converge to zero under such conditions. |
| 2006 |
| 2005 |
| 2004 |
| This paper tackles the issue of designing fixed-topologies in wireless networks using recently developed tools of computational intelligence. The design the fixed-topology in a wireless network aims at finding the topology configuration that satisfies the traffic requirements and performance and reliability constraints with minimal cost. The new multimedia applications and the dynamic and rapidly changed environment of the wireless networks make the fixed-topology design a new challenge. The population-based incremental learning algorithm combines in an efficient way the features of genetic algorithms and competitive learning. In this work, a modified version of the algorithm is proposed to handle the topology design problem in computer networks. |
| 2003 |
| 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 fuzzy controller 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 algorithm does not require a precise mathematical model of the system's dynamics nor does it require a linear parameterization of the system's uncertain physical parameters. Using a Lyapunov stability approach, the controller is proven to be robust in the face of varying intensity levels of the aforementioned uncertainties. The position and the internal forces are also shown to asymptotically converge to zero under such conditions. The performance of the controller proposed is then compared with that of a well known conventional adaptive controller. |
| 2002 |
| We discuss here the implementation aspects of recently developed tools of computational intelligence as applied to joint trajectory generation of a class of multi-joint cooperative robotic systems. This is an issue closely related to the inverse kinematics problem which usually represents a heavy computational burden on the processing power of any complex robotic structure. High nonlinearities, heavy coupling between the degrees of freedom, and time variant configuration of the robot structure heavily contribute to these difficulties. Soft computing techniques have surged in recent years as effective computational tools for emulating the human capabilities when dealing with complex systems. Some of them are used here to synthesize approaches capable of substantially improving the solving of the inverse kinematics problem for a class of robotic systems and help in generating the joint trajectories in a faster way. Comparative results are provided in terms of accuracy and CPU time required for the execution of different trajectories. |
| We present in this study soft computing based results pertaining to the hierarchical tuning process of PID controllers located within the control loop of a class of nonlinear systems. The results are compared with PID controllers implemented either in a stand alone scheme or as a part of conventional gain scheduling structure. This work is motivated by the increasing need in the industry to design highly reliable and efficient controllers for dealing with regulation and tracking capabilities of complex processes characterized by nonlinearities and possibly time varying parameters. The soft computing based controllers proposed are hybrid in nature in that, they integrate within a well defined hierarchical structure the benefits of hard algorithmic controllers with those having supervisory capabilities. The controllers proposed have also the distinct features of learning and auto-tuning without the need for tedious and computationally extensive online systems identification schemes. |
| 2001 |
| This paper tackles the issue of topology design problem in broadband integrated-services digital networks using recently developed soft computing techniques. The design of a high-speed backbone network aims at finding the topology configuration that satisfies the traffic requirements and performance and reliability constraints with minimal cost. The new multimedia application and the dynamic and rapidly changed environment of the backbone networks make the topology design a new challenge. The population-based incremental learning (PBIL), combines in an efficient way the features of genetic algorithms (GA) and competitive learning. In this work, the PBIL algorithm is proposed to handle the topology design problem in computer networks. |
| 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. |
| This paper presents the application of fuzzy logic-based techniques to ATM networks resource allocation, e.g., bandwidth allocation (BA) problem. Efficient bandwidth allocation technique implies effective resources utilization. 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 complicated, they nevertheless suffer from possible inaccuracy in estimating the required bandwidth. Soft computing-based bandwidth controllers, such fuzzy logic controllers, have the capability to approximate the non-linear input-output relations by means of linguistic variables and inference rules. 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. |
| 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 fuzzy controller 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 algorithm does not require a precise mathematical model of the system's dynamics nor does it require a linear parameterization of the system's uncertain physical parameters. The performance of the controller proposed is then compared to that of a well known conventional adaptive controller. |
| 2000 |
| We discuss here the implementation aspects of recently developed tools of computational intelligence for tackling the issue of joint trajectory generation of a class of multi-joint cooperative robotic systems. This is closely related to the inverse kinematics problem which usually represents a heavy computational burden on the processing power of any complex robotic structure. High nonlinearities, heavy coupling between the degrees of freedom, and time variant configuration of the robot structure heavily contribute to these difficulties. Soft computing techniques have surged in recent years as effective computational tools for emulating the human capabilities when dealing with complex systems. Some of them are used here to synthesize approaches capable of substantially improving solving the inverse kinematics problem for a class of robotic systems and help generating the joint trajectories in a faster way. |
| 1999 |
| 1998 |
| 1997 |
| 1996 |
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