Multi-granular Control of Double Inverted Pendulum Based on Universal Logics Fuzzy Neural Networks

Abstract

The control of double-inverted pendulum is one of the most difficult control problems, especially for the control of parallel-type one, because of the high complexity of control systems. To attain the prescribed accuracy in reducing control complexity, a multi-granular controller for stabilizing a double inverted pendulum system is presented based on universal logics fuzzy neural networks. It is a universal multi-granular fuzzy controller which represents the process of reaching goal at different spaces of the information granularity. When the prescribed accuracy is low, a coarse fuzzy controller can be used. As the process moves from high level to low level, the prescribed accuracy becomes higher and the information granularity to fuzzy controller becomes finer. In this controller, a rough plan is generated to reach the final goal firstly. Then, the plan is decomposed to many sub-goals which are submitted to the next lower level of hierarchy. And the more refined plans to reach these sub-goals are determined. If needed, this process of successive refinement continues until the final prescribed accuracy is obtained. In the assistance of universal logics fuzzy neural networks, more flexible structures suitable for any controlled objects can be easy obtained, which improve the performance of controllers greatly. Finally, simulation results indicate the effectiveness of the proposed controller.

The research work is supported by the Ph. D Science Foundation (20041211) and the Postdoctoral Science Foundation (20041101) of North China Electric Power University.