Premium
A self‐learning rule‐based controller employing approximate reasoning and neural net concepts
Author(s) -
Lee ChuenChien
Publication year - 1991
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.4550060105
Subject(s) - controller (irrigation) , computer science , control theory (sociology) , fuzzy logic , task (project management) , set (abstract data type) , artificial neural network , artificial intelligence , fuzzy control system , component (thermodynamics) , relation (database) , control system , rule of inference , control (management) , control engineering , engineering , data mining , physics , electrical engineering , systems engineering , agronomy , biology , programming language , thermodynamics
Abstract A self‐learning controller is proposed as an intelligent controller for dynamic processes, employing a control policy which evolves and improves automatically. A key component of the controller is a rule‐based system which provides a linguistic description of control strategy. This strategy has the form of a collection of fuzzy conditional statements which are implemented and manipulated using fuzzy set theory. the inference engine of the controller is based on the principles of approximate reasoning, while its learning capability is provided by neuron‐like elements, which are derived from animal conditioning theory. It is shown that the system can solve a fairly difficult control learning problem. More concretely, the task is 1‐D pole balancing, in which a pole is hinged to a movable cart to which a continously variable control force is applied. Simulation results demonstrate that improved learning performance can be achieved in relation to previously described systems employing bang‐bang control. Furthermore, the proposed controller is relatively insensitive to variations in the parameters of the system, for example, changes in the length and mass of the pole, initial angle, failure criteria, and slanting the base of the car‐pole system.