Trajectory control of robotic manipulators using chaotic neural networks

Abstract

This paper investigates the direct adaptive control of nonlinear systems using chaotic neural networks. Since the structure of a chaotic neural network contains self and internal feedback loops in each layer, chaotic neural networks can show robust characteristics for controlling highly nonlinear dynamics such as in robotic manipulators. This paper presents modified chaotic neural networks with the backpropagation learning algorithm. To evaluate the performance of the proposed neural networks, we simulate the trajectory control of the three-axis PUMA robot with direct adaptive control strategies. The structure of the robot controller consists of the PD controller and chaotic neural networks controller in parallel. Simulation results showed the superior performance on convergence and final error compared with recurrent neural networks. Chaotic neural networks also reduce the number of nodes and computation time.