Nonlinear control of wheeled mobile robots

The purpose of this project is to implement an autonomous navigation system using nonlinear control techniques to control a wheeled mobile robot (WMR) to follow a preplanned trajectory and track a path. Two other aspects of navigation are studied: path planning and obstacle avoidance. Those three aspects are integrated into a navigation strategy that manages navigation and prevents deadlocks. Two nonlinear control techniques for path tracking and trajectory following have been developed and implemented. In the first approach, a fuzzy logic controller is used to drive the robot through a set of waypoints leading to the destination. In another approach, a controller derived from a Lyapunov function is used to track a reference trajectory that is time dependent. For path planning, a novel optimization technique based on dynamic programming has been developed. The curvature velocity method has been used for obstacle avoidance. The testing was conducted on a P3-AT all-terrain mobile robot equipped with encoders, a gyroscope, and sonar sensors for localization and environment perception. The test results validate the effectiveness of the different approaches that have been developed

LPV-MPC control of autonomous vehicles

In this work, a novel approach is presented to solve the trajectory tracking problem for autonomous vehicles. This method is based on the use of a cascade control where the external loop solves the position control using a novel Linear Parameter Varying - Model Predictive Control (LPV-MPC) approach and the internal loop is in charge of the dynamic control of the vehicle using a LPV - Linear Quadratic Regulator technique designed via Linear Matrix Inequalities (LPV-LMI-LQR). Both techniques use an LPV representation of the kinematic and dynamic models of the vehicle. The main contribution of the LPV-MPC technique is its ability to calculate solutions very close to those obtained by the non-linear version but reducing significantly the computational cost and allowing the real-time operation. To demonstrate the potential of the LPV-MPC, we propose a comparison between the non-linear MPC formulation (NL-MPC) and the LPV-MPC approach.This work has been partially funded by the Spanish Governmentand FEDER through the projects CICYT DEOCS and SCAV (refs.MINECO DPI2016-76493, DPI2017-88403-R). This work has alsobeen partially funded by AGAUR of Generalitat de Catalunyathrough the Advanced Control Systems (SAC) group grant (2017SGR 482), and by AGAUR and the Spanish Research Agencythrough the Maria de Maetzu Seal of Excellence to IRI (MDM-2016-0656).Peer ReviewedPostprint (author's final draft

Path-tracking of a tractor-trailer vehicle along rectilinear and circular paths: A Lyapunov-based approach

Published versio

Co-leaders and a flexible virtual structure based formation motion control

International audienceThe motion control of multi-robots in formation using a Flexible Virtual Structure Approach (FVSA) is proposed. The dynamic model of n agents in formation is developed and sufficient conditions to the desired shape's stability over time are given. Inspired by a shepherd who supervises his troop by controlling the elements on the border, thus, he is able to control all the remainder of the troop. To control the formation shape, one defines control laws for co-leaders, selected from the border, which permits to control motions of the remaining formation agents. The strategy depend strongly on two objectives, on one hand performing an obstacles free motion and on the other avoiding collision among the agents. The Lyapunov technique is used to construct the control law ensuring obstacles avoidance for the agents on the border

TS-MPC for autonomous vehicles Including a TS-MHE-UIO estimator

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this paper, a novel approach is presented to solve the trajectory tracking problem for autonomous vehicles. This approach is based on the use of a cascade control where the external loop solves the position control using a novel Takagi Sugeno-Model Predictive Control (TS-MPC) approach and the internal loop is in charge of the dynamic control of the vehicle using a Takagi Sugeno-Linear Quadratic Regulator technique designed via Linear Matrix Inequalities (TS-LMI-LQR). Both techniques use a TS representation of the kinematic and dynamic models of the vehicle. In addition, a novel Takagi-Sugeno estimator-Moving Horizon Estimator-Unknown Input Observer (TS-MHE-UIO) is presented. This method estimates the dynamic states of the vehicle optimally as well as the force of friction acting on the vehicle that is used to reduce the control efforts. The innovative contribution of the TS-MPC and TS-MHE-UIO techniques is that using the TS model formulation of the vehicle allows us to solve the nonlinear problem as if it were linear, reducing computation times by 10-20 times. To demonstrate the potential of the TS-MPC, we propose a comparison between three methods of solving the kinematic control problem: Using the nonlinear MPC formulation (NL-MPC) with compensated friction force, the TS-MPC approach with compensated friction force, and TS-MPC without compensated friction force.This work was supported by the Spanish Min-istry of Economy and Competitiveness (MINECO) and FEDER through theProjects SCAV (ref. DPI2017-88403-R) and HARCRICS (ref. DPI2014-58104-R). The corresponding author, Eugenio Alcalá, is supported under FI AGAURGrant (ref 2017 FI B00433).Peer ReviewedPostprint (author's final draft

Co-leaders and a flexible virtual structure based formation motion control

International audienceThe motion control of multi-robots in formation using a Flexible Virtual Structure Approach (FVSA) is proposed. The dynamic model of n agents in formation is developed and sufficient conditions to the desired shape's stability over time are given. Inspired by a shepherd who supervises his troop by controlling the elements on the border, thus, he is able to control all the remainder of the troop. To control the formation shape, one defines control laws for co-leaders, selected from the border, which permits to control motions of the remaining formation agents. The strategy depend strongly on two objectives, on one hand performing an obstacles free motion and on the other avoiding collision among the agents. The Lyapunov technique is used to construct the control law ensuring obstacles avoidance for the agents on the border