Hybrid stabilizing control on a real mobile robot

To establish empirical verification of a stabilizing controller for nonholonomic systems, the authors implement a hybrid control concept on a 2-DOF mobile robot. Practical issues of velocity control are also addressed through a velocity controller which transforms the mobile robot to a new system with linear and angular velocity inputs. Experiments in the physical meaning of different controller components provide insights which result in significant improvements in controller performanc

Robust tracking control of two-degrees-of-freedom mobile robots

A robust tracking controller for a mobile robot with two degrees of freedom has been developed. It is implemented and tested on a real mobile robot. Where other controllers show decreasing performance for low reference velocities, the performance of this controller depends only on the geometry of the reference trajectory. This allows accurate positioning at low speeds, close to obstacles. The dynamics of the velocity-controlled mobile robot are considered as perturbed unity transfer from input velocity to actual velocity. It is shown that the tracking controller is robust with respect to these perturbations

Interval Prediction for Continuous-Time Systems with Parametric Uncertainties

The problem of behaviour prediction for linear parameter-varying systems is considered in the interval framework. It is assumed that the system is subject to uncertain inputs and the vector of scheduling parameters is unmeasurable, but all uncertainties take values in a given admissible set. Then an interval predictor is designed and its stability is guaranteed applying Lyapunov function with a novel structure. The conditions of stability are formulated in the form of linear matrix inequalities. Efficiency of the theoretical results is demonstrated in the application to safe motion planning for autonomous vehicles.Comment: 6 pages, CDC 2019. Website: https://eleurent.github.io/interval-prediction

A Learning-Based Framework for Two-Dimensional Vehicle Maneuver Prediction over V2V Networks

Situational awareness in vehicular networks could be substantially improved utilizing reliable trajectory prediction methods. More precise situational awareness, in turn, results in notably better performance of critical safety applications, such as Forward Collision Warning (FCW), as well as comfort applications like Cooperative Adaptive Cruise Control (CACC). Therefore, vehicle trajectory prediction problem needs to be deeply investigated in order to come up with an end to end framework with enough precision required by the safety applications' controllers. This problem has been tackled in the literature using different methods. However, machine learning, which is a promising and emerging field with remarkable potential for time series prediction, has not been explored enough for this purpose. In this paper, a two-layer neural network-based system is developed which predicts the future values of vehicle parameters, such as velocity, acceleration, and yaw rate, in the first layer and then predicts the two-dimensional, i.e. longitudinal and lateral, trajectory points based on the first layer's outputs. The performance of the proposed framework has been evaluated in realistic cut-in scenarios from Safety Pilot Model Deployment (SPMD) dataset and the results show a noticeable improvement in the prediction accuracy in comparison with the kinematics model which is the dominant employed model by the automotive industry. Both ideal and nonideal communication circumstances have been investigated for our system evaluation. For non-ideal case, an estimation step is included in the framework before the parameter prediction block to handle the drawbacks of packet drops or sensor failures and reconstruct the time series of vehicle parameters at a desirable frequency

Passenger car handling model validation using LMS-DADS

This paper presents the modeling and validation of a multibody passenger car model to evaluate the handling performance. LMSDADS generated the multibody passenger car model with PROTON WAJA as the benchmark. Several transient handling performance test had been performed, included double-lane change test, J-turn test and Slalom test at constant speed. An openloop study is performed to evaluate the handling performance which the model responded to the steering input given. Comparisons of the experiment result and model simulation with steering wheel imposed motion are made. Some of the quantities illustrated include steering wheel input, lateral acceleration and yaw rate. Predictions of the model’s responses agree with the actual measured vehicle’s responses within some specified level of accuracy using qualitative validation

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