scholarly journals LAMDA Controller Applied to the Trajectory Tracking of an Aerial Manipulator

2021 ◽  
Vol 11 (13) ◽  
pp. 5885
Author(s):  
Gabriela M. Andaluz ◽  
Luis Morales ◽  
Paulo Leica ◽  
Víctor H. Andaluz ◽  
Guillermo Palacios-Navarro
Keyword(s):  
Trajectory Tracking ◽  
Inverse Dynamics ◽  
Sliding Mode ◽  
Learning Algorithm ◽  
Control Strategies ◽  
Experimental Tests ◽  
Aerial Manipulator ◽  
Control Objective ◽  
First Time ◽  
Manipulator Robot

In this work, a novel LAMDA (Learning Algorithm for Multivariable Data Analysis) control strategy for trajectory tracking for an aerial manipulator is presented. Four control strategies are developed: Kinematic, Inverse Dynamics, Sliding Mode (SMC), and LAMDA. These are compared with each other in order to verify their performance to fulfill the control objective. Experimental tests were also carried out to validate the developed controllers. In addition, a study of stability has been also performed for all the controllers. The results obtained by the LAMDA controller demonstrated the good performance of the controller in the aerial manipulator robot. To the best of our knowledge, this is the first time a LAMDA controller has been applied to an aerial robotic manipulator.

scholarly journals A comparative study and experimental validation on single phase series active power filter control strategies using pi, flc and sliding mode controllers

2019 ◽  
Vol 10 (2) ◽  
pp. 731
Author(s):  
Abdallah Ben Abdelkader ◽  
Othmane Abdelkhalek ◽  
Ismail Khalil Bousserhane ◽  
Mohamed Amine Hartani ◽  
Aymen Omari
Keyword(s):  
Comparative Study ◽  
Power Quality ◽  
Experimental Validation ◽  
Single Phase ◽  
Sliding Mode ◽  
Control Strategies ◽  
Active Power Filter ◽  
Experimental Tests ◽  
Active Power ◽  
Power Filter

<p><span>Sensitive loads are widely used in industrial, which is the main cause of sag-swell and harmonics voltages problems that can affect the power quality. Among the devices that solve such power quality perturbations, the series active power Filter APFS is considered in this paper. Thus, a single phase APFS is developed through an analytic analysis, supported by an experimental validation, where we applied classical proportional integrator PI, fuzzy logic FLC and <a name="_Hlk525422768"></a>sliding mode SM controllers to improve the dynamic response of the APFS. In addition, a comparative study between these control strategies has made in order to mitigate voltage sag-swell and especially harmonics, where the SMC has showed more effective and robust results compared to PI and FLC and proved by the Total harmonic distortion THD ratio. Results of the proposed controllers are simulated in MATLAB simulink® and validated through experimental tests applied on our system prototype.</span></p>

scholarly journals Cascade Sliding Control for Trajectory Tracking of a Nonholonomic Mobile Robot with Adaptive Neural Compensator

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Flavio Capraro ◽  
Francisco Guido Rossomando ◽  
Carlos Soria ◽  
Gustavo Scaglia
Keyword(s):  
Mobile Robot ◽  
Trajectory Tracking ◽  
Sliding Mode ◽  
Approximation Error ◽  
The Other ◽  
Control Technique ◽  
Trajectory Tracking Control ◽  
Inverse Dynamic ◽  
Control Objective ◽  
Lyapunov’S Theorem

A design of sliding mode controllers (SMC) with adaptive capacity is presented. This control technique is formed by two cascaded SMC controllers, one of them having an adaptive neural compensator (ANC); both are put on a WMR (wheeled mobile robot). The mobile robot is divided into a kinematics and a dynamics structure; the first SMC controller acts only on the kinematic structure and the SMC with neural adaptive compensator on the other one. The dynamic SMC was designed applying an inverse dynamic controller and using the model dynamics of the WMR. The adaptive neural compensation (ANC) was used in order to reduce the control error caused by the dynamics variations but it conveys a residual approximation error, so a sliding part was designed to cancel such error. This technique allows achieving the control objective despite parameter variations and external disturbances that take place in the dynamics; on the other hand, the ANC can adjust its neural parameters to reduce the dynamics variations of the WMR and thus improve the trajectory tracking control. Problems of convergence and stability are treated and design rules based on Lyapunov’s theorem are given.

scholarly journals Disturbance Rejection Trajectory Tracking Control for an Unmanned Quadrotor Based on Hybrid Controllers

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xiaoming Ji ◽  
Zihui Cai
Keyword(s):  
Trajectory Tracking ◽  
Disturbance Rejection ◽  
Tracking Control ◽  
Sliding Mode ◽  
Experimental Tests ◽  
Euler Method ◽  
Trajectory Tracking Control ◽  
Hybrid Controller ◽  
Active Disturbance Rejection ◽  
Aerial Vehicle

The purpose of this article is to explore a dual-loop problem regarding the trajectory tracking control of a quadrotor unmanned aerial vehicle, applying a linear active disturbance rejection and conditional integrator sliding mode controller, namely, LARC-CISMC. The quadrotor system model is derived through Newton–Euler method and consists of two subsystems. The hybrid controller for position and attitude loops is constructed. An evaluation of the proposed controller is presented in comparison with the linear active disturbance rejection controller. Simulation comparisons and experimental tests illustrate that the proposed controller has a satisfied robustness and accuracy under lumped disturbances.

scholarly journals A Robust Hybrid Control for Autonomous Flying Robots in an Uncertain and Disturbed Environment

2021 ◽  
Vol 13 (2) ◽  
pp. 187-204
Author(s):  
Yunes Sh. ALQUDSI ◽  
Ayman H. KASSEM ◽  
Gamal M. El-BAYOUMI
Keyword(s):  
Comparative Analysis ◽  
Trajectory Tracking ◽  
Hybrid Control ◽  
Sliding Mode ◽  
Control Strategies ◽  
Qualitative Comparative Analysis ◽  
Nonlinear Feedback ◽  
Control Laws ◽  
Exogenous Disturbances ◽  
Flying Robots

With the aim of efficiently achieving complex trajectory tracking missions in the presence of model uncertainties and exogenous disturbances, this paper proposes a robust hybrid control for the orientation and position of flying robots by adopting insights from sliding mode, geometric tracking, and nonlinear feedback control strategies. Various retrofits are implemented to the composite control scheme in order to tackle the system uncertainties, eliminate the chattering effects, and enhance the trajectory tracking performance. The convergence and stability analysis demonstrated the asymptotic stability of the proposed control algorithm. To reveal the promising performance of the developed control schemes, a qualitative comparative analysis of different proposed control approaches is performed. The comparative analysis examines highly maneuverable trajectories for various tracking scenarios in the presence of uncertain disturbances. The simulation results demonstrated the versatility, robustness, and convergence of the developed control laws that allow autonomous flying robots to effectively perform agile maneuvers.

scholarly journals Model-based dynamic feedback control of a planar soft robot: trajectory tracking and interaction with the environment

2020 ◽  
Vol 39 (4) ◽  
pp. 490-513 ◽  
Author(s):  
Cosimo Della Santina ◽  
Robert K Katzschmann ◽  
Antonio Bicchi ◽  
Daniela Rus
Keyword(s):  
Trajectory Tracking ◽  
Control Strategies ◽  
Soft Robot ◽  
Dynamic Feedback ◽  
Robot Trajectory ◽  
Elastic Bodies ◽  
Unstructured Environment ◽  
Dynamic Feedback Control ◽  
First Time ◽  
Planar Motions

Leveraging the elastic bodies of soft robots promises to enable the execution of dynamic motions as well as compliant and safe interaction with an unstructured environment. However, the exploitation of these abilities is constrained by the lack of appropriate control strategies. This work tackles for the first time the development of closed-loop dynamic controllers for a continuous soft robot. We present two architectures designed for dynamic trajectory tracking and surface following, respectively. Both controllers are designed to preserve the natural softness of the robot and adapt to interactions with an unstructured environment. The validity of the controllers is proven analytically within the hypotheses of the model. The controllers are evaluated through an extensive series of simulations, and through experiments on a physical soft robot capable of planar motions.

Robust optimal nonlinear control strategies for an aerial manipulator

2020 ◽  
Author(s):  
Júnio E. de Morais ◽  
Daniel Neri ◽  
Guilherme V. Raffo
Keyword(s):  
Operating System ◽  
Nonlinear Control ◽  
Trajectory Tracking ◽  
Control Strategies ◽  
Hardware In The Loop ◽  
H Infinity ◽  
Robot Operating System ◽  
Advantages And Disadvantages ◽  
Aerial Manipulator

This work presents two control strategies based on a classic nonlinear H infinity controller and on a novel nonlinear W infinity controller for robust trajectory tracking of an unmanned aerial manipulator (UAM). The controllers are implemented in a hardware-in-the-loop (HIL) framework using the ProVANT simulator, which was developed on the Gazebo and Robot Operating System (ROS) platforms. In addition, the performance of these controllers is compared in order to highlight their advantages and disadvantages.

scholarly journals Trajectory Tracking with Adaptive Robust Control for Quadrotor

2021 ◽  
Vol 11 (18) ◽  
pp. 8571
Author(s):  
Tadeo Espinoza-Fraire ◽  
Armando Saenz ◽  
Francisco Salas ◽  
Raymundo Juarez ◽  
Wojciech Giernacki
Keyword(s):  
Adaptive Control ◽  
Robust Control ◽  
Trajectory Tracking ◽  
Sliding Mode ◽  
Model Reference Adaptive Control ◽  
Adaptive Robust Control ◽  
Adaptive Structure ◽  
Control Objective ◽  
Proportional Derivative Controller ◽  
Model Reference Adaptive

This work proposes three robust mechanisms based on the MIT rule and the sliding-mode techniques. These robust mechanisms have to tune the gains of an adaptive Proportional-Derivative controller to steer a quadrotor in a predefined trajectory. The adaptive structure is a model reference adaptive control (MRAC). The robust mechanisms proposed to achieve the control objective (trajectory tracking) are MIT rule, MIT rule with sliding mode (MIT-SM), MIT rule with twisting (MIT-Twisting), and MIT rule with high order sliding mode (MIT-HOSM).

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