Adaptive Critic Motion Control Design with Sequential Learning RBF Neural Network
Date Issued
2007
Date
2007
Author(s)
Yang, Tzu-Wei
DOI
en-US
Abstract
The goal of this research is to develop a methodology for the design of sequential learning dual heuristic programming (DHP) control systems. A direction-dependent radial basis function network (DDRBFN) with the sequential learning algorithm of generalized growing-pruning (GGP) mechanism is developed to implement the DHP control system. DDRBFN outperforms traditional RBFN in approximating asymmetrical functions. Ripple phenomenon in the DDRBFN approximation becomes insignificant so that the partial derivative quantities can be calculated correctly in polarities. Based on GGP-DDRBFN, the DHP motion control system and the associated updating rules of the critic and the actor are developed. By implementing the DHP control system to conduct the motion of an autonomous wheeled mobile robot, the performance of the proposed design is verified. Simulation results show that, in the sequential learning, the GGP-DDRBFN-based DHP design converges significantly faster than the multi-layered perceptron network based design. In addition, under the circumstances of system model with unmodeled dynamics and unknown disturbance, GGP-DDRBFN-based DHP design obtains better tracking property.
Subjects
順序學習
RBF類神經網路
自評
雙啟發規劃法
自主輪型機器人
sequential learning
RBFN
adaptive critic
dual heuristic programming
autonomous wheeled mobile robot
Type
thesis