A Neural Network Controller Design for the Mecanum Wheel Mobile Robot

optimization, controller and observer design of a reconfigurable mobile robot using genetic algorithm and neural network

in this research optimal reconfiguration strategy of the improved srr reconfigurable mobile robot based on force-angle stability measure has been designed using genetic algorithm. path tracking nonlinear controller which keeps robot’s maximum stability has been designed and simulated in matlab. motion equations of the robot have been derived in parametric form by means of newton- euler, lagrang...

Design of a Cognitive Neural Predictive Controller for Mobile Robot

Mechatronics Design of a Mecanum Wheeled Mobile Robot

The Mecanum wheel was designed in Sweden in 1975. Using four of these wheels provides omni-directional movement for a vehicle without needing a conventional steering system (Muir & Neumann, 1990; Dickerson & Lapin, 1991; Braunl, 1999; Navy, USA, 2002 and Lunze & Schmid, 2002). The wheel itself consists of a hub carrying a number of free moving rollers angled at 45° about the hub's circumference...

Neural Network Controller for Mobile Robot Motion Control

In this paper the neural network-based controller is designed for motion control of a mobile robot. This paper treats the problems of trajectory following and posture stabilization of the mobile robot with nonholonomic constraints. For this purpose the recurrent neural network with one hidden layer is used. It learns relationship between linear velocities and error positions of the mobile robot...

Optimal design of CMAC neural-network controller for robot manipulators

This paper is concerned with the application of quadratic optimization for motion control to feedback control of robotic systems using cerebellar model arithmetic computer (CMAC) neural networks. Explicit solutions to the Hamilton–Jacobi–Bellman (H–J–B) equation for optimal control of robotic systems are found by solving an algebraic Riccati equation. It is shown how the CMAC’s can cope with no...