Sliding Mode Control for Trajectory Tracking of a Non-holonomic Mobile Robot using Adaptive Neural Networks

In this work a sliding mode control method for a non-holonomic mobile robot using an adaptive neural network is proposed. Due to this property and restricted mobility, the trajectory tracking of this system has been one of the research topics for the last ten years. The proposed control structure combines a feedback linearization model, based on a nominal kinematic model, and a practical design that combines an indirect neural adaptation technique with sliding mode control to compensate for the dynamics of the robot. A neural sliding mode controller is used to approximate the equivalent control in the neighbourhood of the sliding manifold, using an online adaptation scheme. A sliding control is appended to ensure that the neural sliding mode control can achieve a stable closed-loop system for the trajectory-tracking control of a mobile robot with unknown non-linear dynamics. Also, the proposed control technique can reduce the steady-state error using the online adaptive neural network with sliding mode control; the design is based on Lyapunov’s theory. Experimental results show that the proposed method is effective in controlling mobile robots with large dynamic uncertaintiesFil: Rossomando, Francisco Guido. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Soria, Carlos Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Carelli Albarracin, Ricardo Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentin

Stabilizing control of two-wheeled wheelchair with movable payload using optimized interval type-2 fuzzy logic

The control schemes of a wheelchair having two wheels with movable payload utilizing the concept of a double-link inverted pendulum have been investigated in this article. The proposed wheelchair has been simulated using SimWise 4D software considering the most efficient parameters. These parameters are extracted using the spiral dynamic algorithm while being controlled with interval type-2 fuzzy logic controller (IT2FLC). The robustness and stability of the implemented controller are assessed under different situations including standing upright, forward motion and application of varying directions and magnitudes of outer disturbances to movable (up and down) system payload. It is shown that the two-wheeled wheelchair adopted by the newly introduced controller has achieved a 94% drop in torque for both Link1 and Link2 and more than 98% fall in distance travelled in comparison with fuzzy logic control type-1 (FLCT1) controller employed in an earlier design. The present study has further considered the increased nonlinearity and complexity of the additional moving payload. From the outcome of this study, it is obvious that the proposed IT2FLC-spiral dynamic algorithm demonstrates better performance than FLCT1 to manage the uncertainties and nonlinearities in case of a movable payload two-wheel wheelchair system

A Dynamic Parameter Identification Method for Migrating Control Strategies Between Heterogeneous Wheeled Mobile Robots

Recent works on the control of wheeled mobile robots have shifted from the use of the kinematic model to the use of the dynamic model. Since theoretical results typically treat the inputs to the dynamic model as torques, few experimental results have been provided, as torque is typically not the input to most commercially available robots. Few papers have implemented controllers based on the dynamic model, and those that have did not address the issue of identifying the parameters of the dynamic model. This work focuses on a method for identifying the parameters of the dynamic model of a wheeled mobile robot. The method is shown to be both effective and easy to implement, and requires no prior knowledge of what the parameters may be. Experimental results on two mobile robots of different scale demonstrate its effectiveness. The estimates of the parameters created by the proposed method are then used in an adaptive controller to verify their accuracy. For future work, this method should be completed autonomously in a two-part manner, onboard the mobile robot. First, the robot should perform the method proposed here to generate an initial parameter estimate, and then use adaptive control to update the estimates

Multilayer perceptron functional adaptive control for trajectory tracking of wheeled mobile robots

Sigmoidal multilayer perceptron neural networks are proposed to effect functional adaptive control for handling the trajectory tracking problem in a nonholonomic wheeled mobile robot. The scheme is developed in discrete time and the multilayer perceptron neural networks are used for the estimation of the robot’s nonlinear kinematic functions, which are assumed to be unknown. On-line weight tuning is achieved by employing the extended Kalman filter algorithm based on a specifically formulated multiple-input, multiple-output, stochastic model for the trajectory error dynamics of the mobile base. The estimated functions are then used on a certainty equivalence basis in the control law proposed in (Corradini et al., 2003) for trajectory tracking. The performance of the system is analyzed and compared by simulation.peer-reviewe

Contemporary Robotics

This book book is a collection of 18 chapters written by internationally recognized experts and well-known professionals of the field. Chapters contribute to diverse facets of contemporary robotics and autonomous systems. The volume is organized in four thematic parts according to the main subjects, regarding the recent advances in the contemporary robotics. The first thematic topics of the book are devoted to the theoretical issues. This includes development of algorithms for automatic trajectory generation using redudancy resolution scheme, intelligent algorithms for robotic grasping, modelling approach for reactive mode handling of flexible manufacturing and design of an advanced controller for robot manipulators. The second part of the book deals with different aspects of robot calibration and sensing. This includes a geometric and treshold calibration of a multiple robotic line-vision system, robot-based inline 2D/3D quality monitoring using picture-giving and laser triangulation, and a study on prospective polymer composite materials for flexible tactile sensors. The third part addresses issues of mobile robots and multi-agent systems, including SLAM of mobile robots based on fusion of odometry and visual data, configuration of a localization system by a team of mobile robots, development of generic real-time motion controller for differential mobile robots, control of fuel cells of mobile robots, modelling of omni-directional wheeled-based robots, building of hunter- hybrid tracking environment, as well as design of a cooperative control in distributed population-based multi-agent approach. The fourth part presents recent approaches and results in humanoid and bioinspirative robotics. It deals with design of adaptive control of anthropomorphic biped gait, building of dynamic-based simulation for humanoid robot walking, building controller for perceptual motor control dynamics of humans and biomimetic approach to control mechatronic structure using smart materials

Velocity control of a unicycle type of mobile robot using optimal pid controller

A unicycle model of control a mobile robot is a simplified modeling approach modified from the differential drive mobile robots. Instead of controlling the right speed, VR and the left speed, VL of the drive systems, the unicycle model is using u and ω as the controller parameters. Tracking is much easier in this model. In this paper, the dynamic of the robot parameter is controlled using two blocks of Proportional-Integral-Derivative(PID) controllers. The gains of the PID are firstly determined using particle swarm optimization(PSO) in offline mode. After the optimal gain is determined, the tracking of the robot’s trajectory is performed online with optimal PID controller. The achieved results of the proposed scheme are compared with those of dynamic model optimized with genetic algorithm(GA) and manually tuned PID controller gains. In the algorithm, the control parameters are computed by minimizing the fitness function defined by using the integral absolute error(IAE) performance index. The simulation results obtained reveal advantages of the proposed PSO-PID dynamic controller for trajectory tracking of a unicycle type of mobile robot. A MATLAB-Simulink program is used to simulate the designed system and the results are graphically plotted. In addition, numerical simulations using 8-shape as a reference trajectory with several numbers of iterations are reported to show the validity of the proposed scheme