Tracking control for nonholonomic mobile robots: Integrating the analog neural network into the backstepping technique
This paper proposes an improved learning algorithm of analog compound orthogonal networks and a novel tracking control approach for nonholonomic mobile robots by integrating the neural network into the backstepping technique. The adaptive control is derived from continuously tuning parameters using the analog neural network in the backstepping control law. The proposed control approach for the mobile robot has the properties to quickly drive the position error to zero and to indicate better smooth movement in the tracking control process. These features are due to continuous online learning and adaptive capability of analog neural networks. Simulation results demonstrate that the proposed control approach is effective and has better control performance.
tracking control nonholonomic mobile robot analog neural network backstepping technique chebyshev orthogonal polynomials sigmoid function continuously learning algorithm