Artificial intelligence in control of real dynamic systems.

PhDA real dynamic plant is used to compare, test and assess the present theoretical techniques of adaptive, learning or intelligent control under practical criteria. Work of this nature has yet to be carried out if "intelligent control" is to have a place in everyday practice. The project follows a natural pattern of development, the construction of computer programmes being an important part of it. First, a. real plant - a model steam engine - and its electronic interface with a general purpose digital computer are designed and built as part of the project. A rough mathematical model of the plant is then obtained through identification tests. Second, conventional control of the plant is effected using digital techniques and the above mentioned mathematical model, and the results are saved to compare with and evaluate the results of "intelligent control". Third, a few well-known adaptive or learning control algorithms are investigated and implemented. These tests bring out certain practical problems not encountered or not given due consideration in theoretical or simulation studies. Alternatively, these problems materialise because assumptions made on paper are not readily available in practice. The most important of these problematic. assumptions are those relating to computational time and storage, convergence of the adaptive or learning algorithm and the training of the controller. The human operator as a distinct candidate for the trainer is also considered and the problems therein are discussed. Finally, the notion of fuzzy sets and logic is viewed from the control point and a controller using this approach is developed and implemented. The operational advantages and the results obtained, albeit preliminary, demonstrate the potential power of this notion and provide the grounds for further work in this area

Learning a robot controller using an adaptive hierarchical fuzzy rule-based system

© 2015, Springer-Verlag Berlin Heidelberg. The majority of machine learning techniques applied to learning a robot controller generalise over either a uniform or pre-defined representation that is selected by a human designer. The approach taken in this paper is to reduce the reliance on the human designer by adapting the representation to improve the generalisation during the learning process. An extension of a Hierarchical Fuzzy Rule-Based System (HFRBS) is proposed that identifies and refines inaccurate regions of a fuzzy controller, while interacting with the environment, for both supervised and reinforcement learning problems. The paper shows that a controller using an adaptive HFRBS can learn a suitable control policy using a fewer number of fuzzy rules for both a supervised and reinforcement learning problem and is not sensitive to the layout as with a uniform representation. In supervised learning problems, a small number of extra trials are required to find an effective representation but for reinforcement learning problems, the process of adapting the representation is shown to significantly reduce the time taken to learn a suitable control policy and hence open the door to high-dimensional problems