Michel Lopez-Franco

Discrete super twisting control algorithm for the nonholonomic mobile robots tracking problem

The tracking control of nonholonomic mobile robots has been an important class of control problems. This paper deals with the design and real-time implementation of a discrete-time super twisting control algorithm for nonholonomic wheeled mobile robots, without the previous knowledged of the plant model or its parameters. In order to show the effectiveness of the proposed controller experimental results are included for a nonholonomic mobile robot QBot®3.

Real-time neural identification and inverse optimal control for a tracked robot

This work presents the implementation in real-time of a neural identifier based on a recurrent high-order neural network which is trained with an extended Kalman filter–based training algorithm and an inverse optimal control applied to a tracked robot. The recurrent high-order neural network identifier is developed without the knowledge of the plant model or its parameters; on the other hand, the inverse optimal control is designed for tracking velocity references. This article includes simulation and real-time results, both using MATLAB®, and also the experimental tests use a modified HD2® Treaded ATR Tank Robot Platform with wireless communication.

Neural Control for Driving a Mobile Robot Integrating Stereo Vision Feedback

This paper proposes a neural control integrating stereo vision feedback for driving a mobile robot. The proposed approach consists in synthesizing a suitable inverse optimal control to avoid solving the Hamilton Jacobi Bellman equation associated to nonlinear system optimal control. The mobile robot dynamics is approximated by an identifier using a discrete-time recurrent high order neural network, trained with an extended Kalman filter algorithm. The desired trajectory of the robot is computed during navigation using a stereo camera sensor. Simulation and experimental result are presented to illustrate the effectiveness of the proposed control scheme.

Real-Time Inverse Optimal Neural Control for Image Based Visual Servoing with Nonholonomic Mobile Robots

We present an inverse optimal neural controller for a nonholonomic mobile robot with parameter uncertainties and unknown external disturbances. The neural controller is based on a discrete-time recurrent high order neural network (RHONN) trained with an extended Kalman filter. The reference velocities for the neural controller are obtained with a visual sensor. The effectiveness of the proposed approach is tested by simulations and real-time experiments.

Inverse kinematics of mobile manipulators based on differential evolution

The solution of the inverse kinematics of mobile manipulators is a fundamental capability to solve problems such as path planning, visual-guided motion, object grasping, and so on. In this article, we present a metaheuristic approach to solve the inverse kinematic problem of mobile manipulators. In this approach, we represent the robot kinematics using the Denavit–Hartenberg model. The algorithm is able to solve the inverse kinematic problem taking into account the mobile platform. The proposed approach is able to avoid singularities configurations, since it does not require the inversion of a Jacobian matrix. Those are two of the main drawbacks to solve inverse kinematics through traditional approaches. Applicability of the proposed approach is illustrated using simulation results as well as experimental ones using an omnidirectional mobile manipulator.

Real-Time Neural Control of Mobile Robots

This chapter presents two application in real-time using neural controls for mobile robots. First, a decentralized inverse optimal neural control is developed for a Shrimp robot, which is a kind of mobile robot with has terrain adaptability. Additionally, a neural control is designed for driving a nonholonomic mobile robot integrating stereo vision feedback. The desired trajectory of the robot is computed during the navigation process using the stereo camera sensor. The proposed neural control approaches are based on discrete-time High Order Neural Networks (RHONN’s) trained with an extended Kalman filter (EKF).

Discrete time neural control of a nonholonomic mobile robot integrating stereo vision feedback

In this paper, we present a discrete time neural controller for driving a nonholonomic mobile robot integrating stereo camera sensos. The proposed approach is based on a discrete-time high order neural network (RHONN) trained with an extended Kalman filter (EKF). The desired trjectory of the robot is computed during the navigation process using the stereo camera sensor. Simulation result is presented to show the effectiveness of the proposed control scheme.

Discrete-time neural identifier for electrically driven nonholonomic mobile robots

A nonlinear discrete-time neural identifier for discrete-time unknown nonlinear systems, in presence of external and internal uncertainties are presented. This identifier is based on a discrete-time recurrent high order neural network (RHONN) trained with an extended Kalman filter (EKF)-based algorithm. Applicability of the scheme is illustrated via simulation for an electrically driven nonholonomic mobile robot.

Decentralized Inverse Optimal Control for Stabilization: A CLF Approach

This chapter proposes a decentralized control scheme for stabilization of a nonlinear system using a neural inverse optimal control approach, developing a suitable controller for each subsystem. Accordingly, each subsystem is approximated by an identifier using a discrete-time recurrent high order neural network (RHONN), trained with an extended Kalman filter (EKF) algorithm. The neural identifier scheme is used to model the uncertain nonlinear subsystem, and based on this neural model and the knowledge of a control Lyapunov function, then an inverse optimal controller is synthesized in order to achieve stability.

Decentralized Inverse Optimal Control for Trajectory Tracking

This chapter proposes a decentralized control for trajectory tracking of a nonlinear system using a neural inverse optimal control approach in order to design a suitable controller for each subsystem. Accordingly, each subsystem is approximated by an identifier using a discrete-time recurrent high order neural network (RHONN), trained with an extended Kalman filter (EKF) algorithm. The neural identifier scheme is used to model the uncertain nonlinear system, and based on this neural model and the knowledge of a control Lyapunov function, then an inverse optimal controller is synthesized in order to achieve trajectory tracking. Applicability of the proposed approach is illustrated via real-time control of a Shrimp robot.