Salah Al-Sharhan

A Robust Hybrid Intelligent Position/Force Control Scheme for Cooperative Manipulators

We examine in this paper 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 controller does not require a precise dynamical model of the systems dynamics. As a matter of fact, the controller can achieve its control objectives starting from partial or no a priori knowledge of the systems dynamics. The ability to incorporate the already acquired knowledge about the systems dynamics is among what distinguishes the proposed controller from its predecessor adaptive fuzzy controllers. 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 force errors are also shown to asymptotically converge to zero under such conditions

Fuzzy entropy: a brief survey

This paper presents a survey about different types of fuzzy information measures. A number of schemes have been proposed to combine the fuzzy set theory and its application to the entropy concept as a fuzzy information measurements. The entropy concept, as a relative degree of randomness, has been utilized to measure the fuzziness in a fuzzy set or system. However, a major difference exists between the classical Shannon entropy and the fuzzy entropy. In fact while the later deals with vagueness and ambiguous uncertainties, the former tackles probabilistic uncertainties (randomness).

The hierarchical expert tuning of PID controllers using tools of soft computing

We present 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 also have the distinct features of learning and auto-tuning without the need for tedious and computationally extensive online systems identification schemes.

Brief paper: Robust computationally efficient control of cooperative closed-chain manipulators with uncertain dynamics

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 payloads 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 systems dynamics. The payloads position/orientation and the internal force errors are also shown to asymptotically converge to zero under such conditions.

Using opposition-based learning to improve the performance of particle swarm optimization

Particle swarm optimization (PSO) is a stochastic, population-based optimization method, which has been applied successfully to a wide range of problems. However, PSO is computationally expensive and suffers from premature convergence. In this paper, opposition-based learning is used to improve the performance of PSO. The performance of the proposed approaches is investigated and compared with PSO when applied to eight benchmark functions. The experiments conducted show that opposition-based learning improves the performance of PSO.

Barebones particle swarm methods for unsupervised image classification

A clustering method that is based on barebones particle swarm (BB) is developed in this paper. BB is a variant of particle swarm optimization (PSO) where parameter tuning is not required. The proposed algorithm finds the centroids of a user specified number of clusters, where each cluster groups together similar patterns. The application of the proposed clustering algorithm to the problem of unsupervised classification and segmentation of images is investigated. To illustrate its wide applicability, the proposed algorithms are then applied to synthetic, MRI and satellite images. Experimental results show that the BB-based clustering algorithm performs very well compared to other state-of-the-art clustering algorithms in all measured criteria.

Blended E-Learning Design: Discussion of Cultural Issues

Blended e-learning is becoming an educational issue especially with the new development of e-learning technology and globalization. Educators as the question: can we design these systems to accommodate different cultural groups and various learning strategies. This paper addresses some design issues when selecting a blended e-learning approach; it discusses some cultural elements that affect the design of blended e-learning. The paper also explores issues related to learning design, then emphasizes on the importance of cultural learning objects (CLO) and its role in the design of multimediabased e-learning systems.

Hybrid soft computing techniques for heterogeneous data classification

In this paper, a neuro-fuzzy based classification technique is adopted to efficiently deal with the classification problem of the heterogeneous medical data sets. The Proposed classification technique is based on the neuro-fuzzy classification (NE-FCLASS) system. However, several improvements are introduced to the origin NEFCLASS. The motivation of this work is triggered by the fact that most conventional classification techniques are capable of handling the numeric data sets but not the heterogenous ones. This can be seen in the classification of the medical data sets. This paper tackles the data classification problem of two medical diseases. The first data set, which is a numeric data set, is related to the Wisconsin breast cancer diagnosis. The second is a heterogeneous data set and is the Wisconsin heart disease diagnosis. Experimental results demonstrate that the proposed technique can effectively improve the classification performance of heterogenous data sets.

Towards an effective integrated e-learning system: Implementation, quality assurance and competency models

Engineering e-learning systems is becoming of a vital importance in order to have an effective implementation of these systems in the different learning environments. The vast and rapid development in the computer, communication and Internet technologies has significantly affected contemporary educational systems. The wide utilization of technology, the abundance of information and knowledge, and the use of multimedia applications, create real challenges to present an efficient and attractive e-learning model that encompasses all these elements. In addition, the dimension of quality assurance in the emerged e-learning environment becomes a real challenge. In such a complex environment, the teacher/instructor competency level and readiness require a new dynamic frame work to ensure the quality of education. This paper presents a new blended e-learning model and quality assurance framework for an efficient implementation in higher education with concentration on online engineering. In addition, a new integrated competency level is presented to ensure the teacher/instructor readiness for the new e-learning environment. The proposed model and framework successfully incorporates all the above elements.

Advancement in Artificial Immune Systems: A perspective of models, algorithms and applications

Artificial Immune Systems (AIS) are a branch of computational intelligence field inspired by the biological immune system. It has gained a lot of interest by researchers during the past decade aiming to develop immune-based models and techniques to solve complex computational or engineering problems. This work presents some applications of AIS techniques to health problems, i.e., cancer prediction, in addition to a thorough survey of existing AIS models and algorithms with a focus on the last five years.