by M. Coderch ... [et al.]. / Bibliography: p. 47-48. / DOE b grant ET-76-C-01-2295

Violet B. Haas. / Bibliography: leaf 19. / "February, 1984" / "R-11-8310350"

Violet B. Haas. / Bibliography: p. 20. / "April 1984" / "R-11-8310350"

Milton B. Adams, Bernard C. Levy, Alan S. Willsky. / Bibliography: leaf 6. / "September, 1984." / "... supported by the National Science Foundation under Grant ECS-83-12921."

Joseph G. Karam, Alexander H. Levis. / Bibliography: leaf 9. / "September 1984." / "...support by the Naval Electronics Systems command under Contract Number N00039-83-C-0466."

M. Coderch, A.S. Willsky, S.S. Sastry, D.A. Castanon. / Bibliography: leaves 4-5. / "Grant AFOSR 82-0253"

David A. Castanon, Marcel Coderch, Alan S. Willsky. / Caption title. "Presented at the 21st IEEE Conference on Decision and Control, Dec. 1982." / Bibliography: leaf [1] / Air Force Office of Scientific Research Grant AFOSR-82-0258

Shankar Sastry. / Caption title. / Bibliography: leaf [6] / Air Force Office of Scientific Research Grant AFOSR 82-0258

Systems of large sparse linear equations frequently arise in engineering and science. Therefore, there is a great need for methods that can solve these systems. In this thesis we will present three of the earliest and simplest iterative methods and also look at two more sophisticated methods. We will study their rate of convergence and illustrate them with examples.

Modern neural network (NN) based control schemes have surmounted many of the limitations found in the traditional control approaches. Nevertheless, these modern control techniques have only recently been introduced for use on high-specification Programmable Logic Controllers (PLCs) and usually at a very high cost in terms of the required software and hardware. This ‗intelligent‘ control in the sector of industrial automation, specifically on standard PLCs thus remains an area of study that is open to further research and development. The research documented in this thesis examined the effectiveness of linear traditional control schemes such as Proportional Integral Derivative (PID), Lead and Lead-Lag control, in comparison to non-linear NN based control schemes when applied on a strongly non-linear platform. To this end, a mechatronic-type balancing system, namely, the Ball-on-Wheel (BOW) system was designed, constructed and modelled. Thereafter various traditional and intelligent controllers were implemented in order to control the system. The BOW platform may be taken to represent any single-input, single-output (SISO) non-linear system in use in the real world. The system makes use of current industrial technology including a standard PLC as the digital computational platform, a servo drive and wireless access for remote control. The results gathered from the research revealed that NN based control schemes (i.e. Pure NN and NN-PID), although comparatively slower in response, have greater advantages over traditional controllers in that they are able to adapt to external system changes as well as system non-linearity through a process of learning. These controllers also reduce the guess work that is usually involved with the traditional control approaches where cumbersome modelling, linearization or manual tuning is required. Furthermore, the research showed that online-learning adaptive traditional controllers such as the NN-PID controller which maintains the best of both the intelligent and traditional controllers may be implemented easily and with minimum expense on standard PLCs.