scholarly journals Trajectory Tracking Error Using PID Control Law for Two-Link Robot Manipulator via Adaptive Neural Networks

2012 ◽  
Vol 3 ◽  
pp. 139-146 ◽  
Author(s):  
Joel Perez P. ◽  
Jose P. Perez ◽  
Rogelio Soto ◽  
Angel Flores ◽  
Francisco Rodriguez ◽  
...  
Keyword(s):  
Neural Networks ◽  
Trajectory Tracking ◽  
Pid Control ◽  
Robot Manipulator ◽  
Tracking Error ◽  
Control Law ◽  
Adaptive Neural Networks

scholarly journals Synthetic Jet Actuator-Based Aircraft Tracking Using a Continuous Robust Nonlinear Control Strategy

2017 ◽  
Vol 2017 ◽  
pp. 1-13
Author(s):  
N. Ramos-Pedroza ◽  
W. MacKunis ◽  
M. Reyhanoglu
Keyword(s):  
Nonlinear Control ◽  
Trajectory Tracking ◽  
Parameter Uncertainty ◽  
Tracking Error ◽  
Synthetic Jet ◽  
Parameter Estimates ◽  
Control Law ◽  
Robust Nonlinear Control ◽  
Trajectory Tracking Control ◽  
Nonlinear Control Law

A robust nonlinear control law that achieves trajectory tracking control for unmanned aerial vehicles (UAVs) equipped with synthetic jet actuators (SJAs) is presented in this paper. A key challenge in the control design is that the dynamic characteristics of SJAs are nonlinear and contain parametric uncertainty. The challenge resulting from the uncertain SJA actuator parameters is mitigated via innovative algebraic manipulation in the tracking error system derivation along with a robust nonlinear control law employing constant SJA parameter estimates. A key contribution of the paper is a rigorous analysis of the range of SJA actuator parameter uncertainty within which asymptotic UAV trajectory tracking can be achieved. A rigorous stability analysis is carried out to prove semiglobal asymptotic trajectory tracking. Detailed simulation results are included to illustrate the effectiveness of the proposed control law in the presence of wind gusts and varying levels of SJA actuator parameter uncertainty.

A Note on a Reduced-Order Observer Based Controller for a Class of Lipschitz Nonlinear Systems

2012 ◽  
Vol 135 (1) ◽  
Author(s):  
Mohamadreza Homayounzade ◽  
Mehdi Keshmiri
Keyword(s):  
Nonlinear Systems ◽  
Global Exponential Stability ◽  
Degrees Of Freedom ◽  
Large Range ◽  
Robot Manipulator ◽  
Tracking Error ◽  
Control Law ◽  
Reduced Order ◽  
Lipschitz Nonlinear Systems ◽  
Reduced Order Observer

This paper presents a novel reduced-order observer based controller for a class of Lipschitz nonlinear systems, described by a set of second order ordinary differential equations. The control law is designed based on the measured output and estimated states. The main features are: (1) The computation cost is reduced noticeably, since the observer is a reduced-order one; (2) The controller guarantees semi-global exponential stability for both estimation and tracking error; and (3) The proposed method can be used in a large range of applications, especially in mechanical systems. The effectiveness of the proposed method is investigated through the numerical simulation for a two-degrees-of-freedom robot manipulator acting on a horizontal worktable.

Experimental Verification of Adaptive Dominant Type Hybrid Adaptive and Learning Controller for Trajectory Tracking of Robot Manipulators

2013 ◽  
Vol 25 (4) ◽  
pp. 737-747 ◽  
Author(s):  
Munadi ◽  
◽  
Tomohide Naniwa ◽  
Keyword(s):  
Adaptive Control ◽  
Motion Control ◽  
Trajectory Tracking ◽  
Robot Manipulator ◽  
Robot Manipulators ◽  
Tracking Error ◽  
Control Input ◽  
Dominant Type ◽  
Dynamical Parameters ◽  
Small Robot

This paper presents an experimental study to verify an adaptive dominant type hybrid adaptive and learning controller for acquiring an accurate trajectory tracking of periodic desired trajectory of robot manipulators. The proposed controller is developed based on combining the model-based adaptive control (MBAC), repetitive learning control (RLC) and proportionalderivative (PD) control in which the MBAC input becomes dominant than other inputs. Dominance of adaptive control input gives the advantage that the proposed controller could adjust the feed-forward motion control input immediately after changing the desired motion or load of the manipulator. In motion control law, the proposed controller uses only one vector to estimate the unknown dynamical parameters. It makes the proposed controller as a simpler hybrid adaptive and learning controller which does not need much computational power and also is easily be implemented for real applications of robot manipulators. The proposed controller is verified through experiments on a four-link small robot manipulator as representation of a scale robot manipulator to ensure this controller can be applied in the real applications of robot manipulators. The experimental results show the effectiveness of the proposed controller by indicating the position tracking error approaches to zero.

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.

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