Dynamic Modelling and Adaptive Traction Control for Mobile Robots

Mobile robots have received a great deal of research in recent years. A significant amount of research has been published in many aspects related to mobile robots. Most of the research is devoted to design and develop some control techniques for robot motion and path planning. A large number of researchers have used kinematic models to develop motion control strategy for mobile robots. Their argument and assumption that these models are valid if the robot has low speed, low acceleration and light load. However, dynamic modelling of mobile robots is very important as they are designed to travel at higher speed and perform heavy duty work. This paper presents and discusses a new approach to develop a dynamic model and control strategy for wheeled mobile robot which I modelled as a rigid body that roles on two wheels and a castor. The motion control strategy consists of two levels. The first level is dealing with the dynamic of the system and denoted as Low level controller. The second level is developed to take care of path planning and trajectory generation

Design of an adaptive neural predictive nonlinear controller for nonholonomic mobile robot system based on posture identifier in the presence of disturbance

This paper proposes an adaptive neural predictive nonlinear controller to guide a nonholonomic wheeled mobile robot during continuous and non-continuous gradients trajectory tracking. The structure of the controller consists of two models that describe the kinematics and dynamics of the mobile robot system and a feedforward neural controller. The models are modified Elman neural network and feedforward multi-layer perceptron respectively. The modified Elman neural network model is trained off-line and on-line stages to guarantee the outputs of the model accurately represent the actual outputs of the mobile robot system. The trained neural model acts as the position and orientation identifier. The feedforward neural controller is trained off-line and adaptive weights are adapted on-line to find the reference torques, which controls the steady-state outputs of the mobile robot system. The feedback neural controller is based on the posture neural identifier and quadratic performance index optimization algorithm to find the optimal torque action in the transient state for N-step-ahead prediction. General back propagation algorithm is used to learn the feedforward neural controller and the posture neural identifier. Simulation results show the effectiveness of the proposed adaptive neural predictive control algorithm; this is demonstrated by the minimised tracking error and the smoothness of the torque control signal obtained with bounded external disturbances

Slide-Down Prevention for Wheeled Mobile Robots on Slopes

Wheeled mobile robots on inclined terrain can slide down due to loss of traction and gravity. This type of instability, which is different from tip-over, can provoke uncontrolled motion or get the vehicle stuck. This paper proposes slide-down prevention by real-time computation of a straightforward stability margin for a given ground-wheel friction coefficient. This margin is applied to the case study of Lazaro, a hybrid skid-steer mobile robot with caster-leg mechanism that allows tests with four or five wheel contact points. Experimental results for both ADAMS simulations and the actual vehicle demonstrate the effectiveness of the proposed approach.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Experimental comparison of control strategies for trajectory tracking for mobile robots

The purpose of this paper is to implement, test and compare the performance of different control strategies for tracking trajectory for mobile robots. The control strategies used are based on linear algebra, PID controller and on a sliding mode controller. Each control scheme is developed taking into consideration the model of the robot. The linear algebra approaches take into account the complete kinematic model of the robot; and the PID and the sliding mode controller use a reduced order model, which is obtained considering the mobile robot platform as a black-box. All the controllers are tested and compared, firstly by simulations and then, by using a Pioneer 3DX robot in field experiments.Fil: Capito, Linda. Escuela Politécnica Nacional; EcuadorFil: Proaño, Pablo. Escuela Politécnica Nacional; EcuadorFil: Camacho, Oscar. Escuela Politécnica Nacional; EcuadorFil: Rosales, Andrés. Escuela Politécnica Nacional; EcuadorFil: Scaglia, Gustavo Juan Eduardo. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentin

Integral sliding mode control of an extended Heisenberg system

International audienceThis paper deals with the practical robust stabilization and tracking problems of the perturbed multidimensional Heisenberg system with some additional integrators in the control input path. This objective is achieved by the use of variable structure control laws with an integral augmented sliding variable. This note shows how to select the integral sliding surface in such a way that the practical stabilization of the extended Heisenberg system is achieved in spite of the uncertainties and without loss of controllability. Experimental results on a wheeled mobile robot show the performance of the proposed controller for the practical stabilization and tracking problems