|Title:||Concurrent neural networks for nonlinear self-tuning adaptive control|
Neural networks (Computer science)
Adaptive control systems
Hong Kong Polytechnic -- Dissertations
|Department:||Department of Electronic Engineering|
|Pages:||93 leaves : ill. ; 30 cm|
|Abstract:||The ever-increasing technological demands of our modern society require innovative approaches to highly demanding control problems. Artificial neural networks with their massive parallelism and learning capabilities offer the promise of better solutions, at least to some problems. By now, the control community has heard of neural networks and wonders if these networks can be used to provide better control solutions to old problems or perhaps solutions to control problems that have withstood our best efforts. In a neural network, weights are adjusted, depending on the task at hand, to improve performance. They can be assigned new values in two ways : either determined via some prescribed off-line algorithm - remaining fixed during operation - or adjusted via a learning process. Learning is accomplished by, first adjusting these weights step by step, typically to minimize some objective function, and then, storing these best values as the actual strengths of the interconnections. Neural networks can also provide, in principle, significant fault tolerance, since damage to a few links need not significantly impair the overall performance. The use of neural networks in control systems can be seen as a natural step in the evolution of control methodology to meet new challenges. Looking back, the evolution in the control area has been fueled by three major needs: the need to deal with increasing complex systems, the need to accomplish increasing demanding design requirements, and the need to attain these requirements with less precise advanced knowledge of the plant and its environment - that is, the need to control under increased uncertainty. Today, the need to control, in a better way, increasingly complex dynamical systems under significant uncertainty has led to a reevaluation of the conventional control methods, and it has made the need for new methods quite apparent. It has also led to a more general concept of control, one that includes higher-level decision making, planning, and learning, which are capabilities necessary when higher degrees of system autonomy are desirable. It is not surprising that the control community is seriously and actively searching for ideas to deal effectively with the increasingly challenging control problems of our modern society. Need is the mother of invention. So the use of the neural networks in control is rather a natural step in its evolution. Neural networks appear to offer new promising directions toward better understanding and perhaps even solving some of our most difficult control problems. History, of course, has made clear that neural networks will be accepted and used if they solve problems that have been previously impossible or very difficult to solve. They will be rejected and will be just a fast-fading novelty if they do not prove useful. The challenge of this project is to find the best way to fully utilize this powerful new tool in control; the jury is still out, as their best uses have not been decided yet. In this project, a back-propagation neural network is applied to a nonlinear self-tuning tracking problem. Traditional self-tuning adaptive control techniques can only deal with linear systems or some special nonlinear systems. The emerging back-propagation neural networks have the capability to learn arbitrary nonlinearity and show great potential for adaptive control applications. A scheme for combining back-propagation neural networks with self-tuning adaptive control techniques is worked out, and the control mechanism is analyzed. Simulation results show that the new self-tuning scheme can deal with a large unknown nonlinearity.|
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