Direct neuro-adaptive control of robot manipulators

The authors present a method for the adaptive control of a robot arm based on a feedforward neural network. The method is based on the backpropagation algorithm. Backpropagation is used within a learning by reinforcement framework instead of learning by teaching. A neural network is used to estimate the adaptive control law based only on an error signal resulting from the deviation between the desired position, velocity, and acceleration inputs to the robot inverse model and those generated by the robot system. The proposed method does not require any explicit parameter estimation of robot parameters. A cylindrical three-degree-of-freedom robot arm was simulated to demonstrate the control algorithm