This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

Adaptive Control

The function of the engineering profession is to manipulate materials, energy, and information, thereby creating benefit for humankind. To do this successfully, engineers must have a knowledge of nature that goes beyond mere theory—knowledge that is traditionally gained in educational laboratories. Over the years, however, the nature of these laboratories has changed. This paper describes the history of some of these changes and explores in some depth a few of the major factors influencing laboratories today. In particular, the paper considers the lack of coherent learning objectives for laboratories and how this lack has limited the effectiveness of laboratories and hampered meaningful research in the area. A list of fundamental objectives is presented along with suggestions for possible future research.

http://web.stanford.edu/group/asee/wikiupload/6/69/Feisel_LabsInEngEd.pdf

The Role of the Laboratory in Undergraduate Engineering Education

Technology is a never ending process. To be able to design a product using the current technology that will be beneficial to the lives of others is a huge contribution to the community. This paper presents the design and implementation of a low cost but yet flexible and secure cell phone based home automation system. The design is based on a stand alone Arduino BT board and the home appliances are connected to the input/ output ports of this board via relays. The communication between the cell phone and the Arduino BT board is wireless. This system is designed to be low cost and scalable allowing variety of devices to be controlled with minimum changes to its core. Password protection is being used to only allow authorised users from accessing the appliances at home.

Bluetooth based home automation system using cell phone

An adaptive backstepping neural-network control approach is extended to a class of large-scale nonlinear output-feedback systems with completely unknown and mismatched interconnections. The novel contribution is to remove the common assumptions on interconnections such as matching condition, bounded by upper bounding functions. Differentiation of the interconnected signals in backstepping design is avoided by replacing the interconnected signals in neural inputs with the reference signals. Furthermore, two kinds of unknown modeling errors are handled by the adaptive technique. All the closed-loop signals are guaranteed to be semiglobally uniformly ultimately bounded, and the tracking errors are proved to converge to a small residual set around the origin. The simulation results illustrate the effectiveness of the control approach proposed in this correspondence.

Decentralized Output-Feedback Neural Control for Systems With Unknown Interconnections

This paper presents the design and implementation of a Java-based automation system that can monitor and control home appliances via the World Wide Web. The design is based on a stand alone embedded system board integrated into a PC-based server at home. The home appliances are connected to the input/output ports of the embedded system board and their status are passed to the server. The monitoring and control software engine is based on the combination of JavaServer pages, JavaBeans, and interactive C. The home appliances can be monitored and controlled locally via the embedded system board, or remotely through a Web browser from anywhere in the world provided that an Internet access is available. The system is scalable and allows multi-vendor appliances to be added with no major changes to its core. Password protection is used to block unauthorized users from accessing the appliances at home. If the Internet connection is down or the server is not up, the embedded system board still can control and operate the appliances locally.

Java-based home automation system

The purpose of the paper is to illustrate the remote control of hardware in the laboratory for educational purposes, using commercial off-the-shelf equipment and available freeware. The goal is to eliminate extensive programming using high-level languages, such as Java programming to achieve remote connectivity and focus on the primary task of implementing control algorithms. The use of MS NetMeeting for Internet-based control is described. Control experiments were easily carried out by a remote user, using Matlab-based real-time tools and MS NetMeeting.

Internet-based educational control systems lab using NetMeeting

In this paper, a novel neural network (NN) backstepping controller is modified for application to an industrial motor drive system. A control system structure and NN tuning algorithms are presented that are shown to guarantee stability and performance of the closed-loop system. The NN backstepping controller is implemented on an actual motor drive system using a two-PC control system developed at The University of Texas at Arlington. The implementation results show that the NN backstepping controller is highly effective in controlling the industrial motor drive system. It is also shown that the NN controller gives better results on actual systems than a standard backstepping controller developed assuming full knowledge of the dynamics. Moreover, the NN controller does not require the linear-in-the-parameters assumption or the computation of regression matrices required by standard backstepping.

Design and implementation of industrial neural network controller using backstepping

In the paper, the procedure is given for designing speed and active power controller of hydro turbine units. The procedure is based on mathematical models of the controlled system. The controller parameters are obtained from closed-loop poles and hydro turbine parameters by derived analytical formulas over a wide range of the hydro turbine operating points. The described procedure allows the fast and direct determination of the controller parameters. There is no need for heuristic controller parameters tuning. Results from two hydro turbine units with the controllers designed using the described procedure are given as an illustration.

Speed and active power control of hydro turbine unit

The objective of this paper is to explore the implementation of a Bayesian Belief Network for an automated breast cancer detection support tool. It is intuitive that Bayesian networks are employed as one viable option for computer-aided detection by representing the relationships between diagnoses, physical findings, laboratory test results and imaging study findings. This work brings important entities such as Radiologists, Image Processing Scientists, Data Base Specialists and Applied Mathematicians on a common platform. A brief background concerning causal networks, probability theory and Bayesian networks is given; available computational tools and platforms are described. It is explained that, by exploiting conditional independencies entailed by influence chains, it is possible to represent a large instance in a Bayesian network using little space, and it is often possible to perform probabilistic inference among the features in an acceptable amount of time. The next steps towards realizing a Bayesian Belief Network Implementation are described. Bayesian networks have an unparallel advantage of being able to exploit the explicit structure of the domain model to derive a graphical representation for learning. The encoding of independencies in the network topology admits the design of efficient procedures for performing computations over the network. For the application of computer-aided detection in mammography, the researchers intend to design an interface between the project's Bayesian network learning algorithm and the radiologists, so that the radiologists can have interaction with the system by labeling only a small number of informative images presented by the active learning algorithm.

	 

	Key words: Bayesian networks, breast cancer, computer aided decision support.

Exploring Bayesian networks for medical decision support in breast cancer detection

A neural net (NN)/fuzzy logic (FL) adaptive critic controller is described. This structure takes advantage of the decision-making properties of a FL system to critique and tune a NN action-generating network. The stability of the proposed structure is proven. NN and fuzzy weight tuning algorithms are given that do not require complicated initialization procedures or any off-line learning phase. Tracking and bounded NN weights and control signals are guaranteed. The adaptive fuzzy critic controller given here is a model-free controller' in the sense that it works for any system in a prescribed class without the need for extensive modeling and preliminary analysis to find a regression matrix'. There is no linearity in the parameter (LIP) requirement. Copyright © 2003 John Wiley & Sons, Ltd.

Ognjen Kuljaca

Papers

Internet-based educational control systems lab using NetMeeting

Design and implementation of industrial neural network controller using backstepping

Speed and active power control of hydro turbine unit

Exploring Bayesian networks for medical decision support in breast cancer detection

Adaptive critic design using non‐linear network structures