Intelligent Adaptive Motion Control for Ground Wheeled Vehicles

In this paper a new intelligent adaptive control is applied to solve a problem of motion control of ground vehicles with two independent wheels actuated by a differential drive. The major objective of this work is to obtain a motion control system by using a new fuzzy inference mechanism where the Lyapunov’s stability can be assured. In particular the parameters of the kinematical control law are obtained using an intelligent Fuzzy mechanism, where the properties of the Fuzzy maps have been established to have the stability above. Due to the nonlinear map of the intelligent fuzzy inference mechanism (i.e. fuzzy rules and value of the rule), the parameters above are not constant, but, time after time, based on empirical fuzzy rules, they are updated in function of the values of the tracking errors. Since the fuzzy maps are adjusted based on the control performances, the parameters updating assures a robustness and fast convergence of the tracking errors. Also, since the vehicle dynamics and kinematics can be completely unknown, a dynamical and kinematical adaptive control is added. The proposed fuzzy controller has been implemented for a real nonholonomic electrical vehicle. Therefore system robustness and stability performance are verified through simulations and experimental studies

DEXTEROUS TRAJECTORY TRACKING CONTROL OF A MOBILE ROBOT

ABSTRACT A departure from the traditional trajectory tracking control technique of a mobile robot is presented here in order to accommodate sudden changes in the reference trajectory. It is expected that in a dynamic, uncertain environment the robot may need to make sudden changes in its navigation strategy that may necessitate such an approach. In this work, a hybrid control framework is developed that first determines a suitable control strategy for a particular subtask and then implements it by means of choosing the specific controller. A supervisor is used to determine the suitable control strategy. The switching stability among a set of trajectory tracking controllers is analyzed. Extensive simulation results demonstrate the efficacy of the proposed control technique