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, lagrange and kane dynamic methods using matlab symbolic toolbox. the robot has been simulated using adams software for optimization evaluation. path and velocity of the vehicle and end-effector and the terrain function below vehicle’s wheels have been already defined. reconfigurable mobile robots increase their stability in rough terrain by static or dynamic reconfiguration or by means of mass-center quasi-static transportation or dynamical reconfiguration during motion. extendable newton-euler, lagrange and kane dynamic methods algorithms to other kinds of robots have been designed for dynamic modeling of improved srr reconfigurable mobile. spatial complex dynamic equations of improved srr have been derived in parametric form by means of those dynamic methods using matlab symbolic toolbox. furthermore, the robot has been simulated using adams software for evaluation of dynamic model.