This article describes a neural network controller for guidance of a robot arm, used to model some aspects of autonomous vehicle technology. The controller uses video images with adaptive view‐angles for the sensory input, and the system was configured to simulate an autonomous vehicle guidance system on a flat terrain using a high‐contrast guiding path. To demonstrate the feasibility of using neural networks in this type of application, an Intelledex 405 robot fitted with a video camera and associated vision system was used. Phase I of the project consisted of a single‐speed implementation and limited network training. Phase II featured a multi‐speed implementation using adaptively varied view‐angles based on robot arm velocity. It was shown that the neural network controller was able to control the robot arm along a path composed of path segments unlike those with which it was trained. In addition it was shown that a multi‐speed implementation with adaptive view angles improved system performance. © 1994 John Wiley & Sons, Inc.

No abstract

In this paper we present motivation for pursuing different classes of adaptive artif7ial neural network controller designs. A literature review of the current state of the art in artif-xial neural network controllers, and a framework for conducting research in the field is included. In Addition, hybrid neural nehvorklfuzzy controllers are described. The importance of benchmarking comparable adaptive eontrollers is discussed, and appropriate benchmarks are proposed. regulators (STR), adaptive inverse controllers (AIC) and &el referme adaptive controllers ( M U C ) , among others 121. h o t h e r way to classify these controllers, is by their use in a direct Of an indirect controller stnrcture as described byAlso there are applications of A"C,s which are not purely ANN but which include the use of linguistic or heuristic features, as used in Fuzzy Logic Controllers.

No abstract

No abstract