A Two-Layer Robot Controller Design Using Evolutionary Algorithms
Journal of Intelligent and Robotic Systems
Online adaptive fuzzy neural identification and control of nonlinear dynamic systems
Autonomous robotic systems
Immune model-based fault diagnosis
Mathematics and Computers in Simulation
Information Sciences: an International Journal
Discrete-time decentralized neural block controller for a five DOF robot manipulator
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Robust Fuzzy Control of Electrical Manipulators
Journal of Intelligent and Robotic Systems
Some key issues in the design of self-organizing fuzzy control systems
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
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This paper develops a decentralized adaptive fuzzy control scheme for robot manipulators via a combination of genetic algorithm and gradient method. The controller for each link consists of a feedforward fuzzy torque-computing system and a feedback fuzzy PD system. The feedforward fuzzy system is trained and optimized off-line by an improved genetic algorithm, that is to say, not only the parameters but also the structure of the fuzzy system are self-organized. Because genetic algorithm can operate successfully without the system model, no exact inverse dynamics of the robot system are required. The feedback fuzzy PD system, on the other hand, is tuned on-line using gradient method. In this way, the proportional and derivative gains are adjusted properly to keep the closed-loop system stable. The proposed controller has the following merits: (1) it needs no exact dynamics of the robot systems and the computation is time-saving because of the simple structure of the fuzzy systems; and (2) the controller is insensitive to various dynamics and payload uncertainties in robot systems. These are demonstrated by analyses of the computational complexity and various computer simulations