Adaptive critic designs: a case study for neurocontrol
Neural Networks
A course in fuzzy systems and control
A course in fuzzy systems and control
Theory of Robot Control
Handbook of Learning and Approximate Dynamic Programming (IEEE Press Series on Computational Intelligence)
Approximate Dynamic Programming: Solving the Curses of Dimensionality (Wiley Series in Probability and Statistics)
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In this paper we propose a discrete tracking control system for 3 degrees of freedom (DOF) robotic manipulator control. The control system is composed of Adaptive Critic Design (ACD), a PD controller and a supervisory term derived from the Lyapunov stability theory. ACD in Dual-Heuristic Programming (DHP) configuration consists of two structures realized in a form of neural networks (NN): actor - generates a control signal and critic approximates a derivative of the cost function with respect to the state. The control system works on-line, does not require a preliminary learning and uses the 3DOF manipulator dynamicsmodel for a state prediction in ACD structure. Verification of the proposed control algorithm was realized on a SCORBOT 4PC manipulator.