Adaptive Tracking in Mobile Robots With Input-Output Linearization

2014 ◽  
Vol 136 (5) ◽  
Author(s):  
Cesáreo Raimúndez ◽  
Alejandro F. Villaverde ◽  
Antonio Barreiro
Keyword(s):  
Neural Network ◽  
Mobile Robots ◽  
Trajectory Tracking ◽  
Tracking Error ◽  
Adaptive Controller ◽  
Lyapunov Theory ◽  
Input Output ◽  
External Perturbations ◽  
Neural Network Controller ◽  
Input Output Linearization

This paper presents a neural network adaptive controller for trajectory tracking of nonholonomic mobile robots. By defining a point to follow (look-ahead control), the path-following problem is solved with input-output linearization. A computed torque plus derivative (PD) controller and a dynamic inversion neural network controller are responsible for reducing tracking error and adapting to unmodeled external perturbations. The adaptive controller is implemented through a hidden layer feed-forward neural network, with weights updated in real time. The stability of the whole system is analyzed using Lyapunov theory, and control errors are proven to be bounded. Simulation results demonstrate the good performance of the proposed controller for trajectory tracking under external perturbations.

Direct Adaptive Control of Underwater Vehicles using Neural Networks

2003 ◽  
Vol 9 (5) ◽  
pp. 605-619 ◽  
Author(s):  
Myung-Hyun Kim ◽  
Daniel J. Inman
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Network Architecture ◽  
Explicit Knowledge ◽  
Tracking Error ◽  
Network Control ◽  
Lyapunov Theory ◽  
Direct Adaptive Control ◽  
Neural Network Controller ◽  
Comparison Of The Results

A direct adaptive neural network controller is developed for a model of an underwater vehicle. A radial basis neural network and a multilayer neural network are used in the closed-loop to approximate the nonlinear vehicle dynamics. No prior off-line training phase and no explicit knowledge of the structure of the plant are required, and this scheme exploits the advantages of both neural network control and adaptive control. A control law and a stable on-line adaptive law are derived using the Lyapunov theory, and the convergence of the tracking error to zero and the boundedness of signals are guaranteed. A comparison of the results with different neural network architecture is made, and the performance of the controller is demonstrated by computer simulations.

scholarly journals Complex Dynamical Network Control for Trajectory Tracking Using Delayed Recurrent Neural Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Jose P. Perez ◽  
Joel Perez Padron ◽  
Angel Flores Hemandez ◽  
Santiago Arroyo
Keyword(s):  
Neural Network ◽  
Dynamical System ◽  
Trajectory Tracking ◽  
Recurrent Neural Networks ◽  
Tracking Error ◽  
Network Control ◽  
Lyapunov Theory ◽  
Control Law ◽  
Complex Dynamical Network ◽  
Dynamical Network

In this paper, the problem of trajectory tracking is studied. Based on the V-stability and Lyapunov theory, a control law that achieves the global asymptotic stability of the tracking error between a delayed recurrent neural network and a complex dynamical network is obtained. To illustrate the analytic results, we present a tracking simulation of a dynamical network with each node being just one Lorenz’s dynamical system and three identical Chen’s dynamical systems.

Trajectory Tracking of a Spherical Robot Based on an RBF Neural Network

2011 ◽  
Vol 383-390 ◽  
pp. 631-637 ◽  
Author(s):  
Ming Hui Zheng ◽  
Qiang Zhan ◽  
Jin Kun Liu ◽  
Yao Cai
Keyword(s):  
Neural Network ◽  
Trajectory Tracking ◽  
Rbf Neural Network ◽  
Tracking Error ◽  
Lyapunov Theory ◽  
Pd Controller ◽  
Spherical Robot ◽  
Rule Based ◽  
Rbf Network ◽  
Spherical Mobile Robot

This paper deals with trajectory tracking problem of a spherical mobile robot, BHQ-1. First, a desired velocity is obtained by proposing a PD controller based on the kinematics. Then a PD controller with an RBF (Radial Basis Function) neural network is proposed based on the desired velocity and the inexact dynamics. The weights of the RBF network are designed with an adaptive rule based on the tracking error, and hence the network can compensate the uncertainties of the dynamics more effectively. Stability is presented via Lyapunov Theory and simulation results are provided to illustrate the tracking performance.

Reducing Trajectory Tracking Error of Flexible Mobile Robots Using Command Shaping With Error-Limiting Constraints

2017 ◽  
Author(s):  
Gerald Eaglin ◽  
Joshua Vaughan
Keyword(s):  
Mobile Robots ◽  
Trajectory Tracking ◽  
Tracking Error ◽  
Input Shaping ◽  
Flexible Systems ◽  
Command Shaping ◽  
Modeling Errors ◽  
Temporal Tracking ◽  
Point Motion ◽  
Point To Point

The ability to track a trajectory without significant error is a vital requirement for mobile robots. Numerous methods have been proposed to mitigate tracking error. While these trajectory-tracking methods are efficient for rigid systems, many excite unwanted vibration when applied to flexible systems, leading to tracking error. This paper analyzes a modification of input shaping, which has been primarily used to limit residual vibration for point-to-point motion of flexible systems. Standard input shaping is modified using error-limiting constraints to reduce transient tracking error for the duration of the system’s motion. This method is simulated with trajectory inputs constructed using line segments and Catmull-Rom splines. Error-limiting commands are shown to improve both spatial and temporal tracking performance and can be made robust to modeling errors in natural frequency.

scholarly journals Singularity-Free Neural Control for the Exponential Trajectory Tracking in Multiple-Input Uncertain Systems with Unknown Deadzone Nonlinearities

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
J. Humberto Pérez-Cruz ◽  
José de Jesús Rubio ◽  
Rodrigo Encinas ◽  
Ricardo Balcazar
Keyword(s):  
Neural Network ◽  
Trajectory Tracking ◽  
Feedback Linearization ◽  
Neural Control ◽  
Tracking Error ◽  
Uncertain System ◽  
Reference Trajectory ◽  
System A ◽  
Multiple Input ◽  
Feedback Linearization Control

The trajectory tracking for a class of uncertain nonlinear systems in which the number of possible states is equal to the number of inputs and each input is preceded by an unknown symmetric deadzone is considered. The unknown dynamics is identified by means of a continuous time recurrent neural network in which the control singularity is conveniently avoided by guaranteeing the invertibility of the coupling matrix. Given this neural network-based mathematical model of the uncertain system, a singularity-free feedback linearization control law is developed in order to compel the system state to follow a reference trajectory. By means of Lyapunov-like analysis, the exponential convergence of the tracking error to a bounded zone can be proven. Likewise, the boundedness of all closed-loop signals can be guaranteed.

scholarly journals Adaptive Neural Network Control with Backstepping for Surface Ships with Input Dead-Zone

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Guoqing Xia ◽  
Xingchao Shao ◽  
Ang Zhao ◽  
Huiyong Wu
Keyword(s):  
Neural Network ◽  
Explicit Knowledge ◽  
Dead Zone ◽  
Nonlinear Function ◽  
Lyapunov Theory ◽  
Adaptive Neural Network ◽  
Neural Network Controller ◽  
Network Controller ◽  
Adaptive Neural Network Control ◽  
Fuzzy Logic Technique

This paper addresses the problem of adaptive neural network controller with backstepping technique for fully actuated surface vessels with input dead-zone. The combination of approximation-based adaptive technique and neural network system is used for approximating the nonlinear function of the ship plant. Through backstepping and Lyapunov theory synthesis, an indirect adaptive network controller is derived for dynamic positioning ships without dead-zone property. In order to improve the control effect, a dead-zone compensator is derived using fuzzy logic technique to handle the dead-zone nonlinearity. The main advantage of the proposed controller is that it can be designed without explicit knowledge about the ship motion model, and dead-zone nonlinearity is well compensated. A set of simulations is carried out to verify the performance of the proposed controller.

Adaptive fault tolerant control for trajectory tracking of a quadrotor helicopter

2017 ◽  
Vol 40 (12) ◽  
pp. 3560-3569 ◽  
Author(s):  
Min Li ◽  
Zongyu Zuo ◽  
Hao Liu ◽  
Cunjia Liu ◽  
Bing Zhu
Keyword(s):  
Trajectory Tracking ◽  
Fault Tolerant ◽  
Tracking Error ◽  
Adaptive Controller ◽  
Reference Trajectory ◽  
Quadrotor Helicopter ◽  
Attitude Error ◽  
Inertial Parameters ◽  
Error Dynamics ◽  
Fast Adaptation

In this paper, an adaptive fault tolerant controller based on [Formula: see text] control is developed and applied to the trajectory tracking for a quadrotor helicopter. Both multiplicative and additive actuator faults are considered. The proposed design is based on nonlinear feed-forward compensations and a typical nonlinear quadrotor model with uncertain inertial parameters and external disturbances. The [Formula: see text] adaptive control design is slightly modified to adapt with the position and the attitude error dynamics. The proposed adaptive controller yields uniformly verifiable bounds on the transient and the steady-state tracking error for any designated bounded reference trajectory. In the presence of fast adaptation, the adaptive controller compensates for actuator fault and disturbances in a particular frequency range. Finally, simulation results are included to validate the effectiveness of the proposed design.

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