scholarly journals Adaptive Augmented Torque Control of a Quadcopter with an Aerial Manipulator

2021 ◽  
Author(s):  
Vidya Sumathy ◽  
Debasish Ghose
Keyword(s):  
Feedback Linearization ◽  
Estimation Error ◽  
Tracking Error ◽  
Lyapunov Theory ◽  
Torque Control ◽  
Control Law ◽  
Control Input ◽  
Positive Real ◽  
Aerial Robot ◽  
Non Linear

A quadcopter manipulator system is an aerial robot consisting of a quadcopter with a robotic arm attached to it. The system has coupled non-linear dynamics with uncertain time-varying parameters. The work in this paper focuses on designing an adaptive non-linear controller to facilitate the uncertain system’s trajectory tracking and stability. The novelty of the proposed work is the design and implementation of an adaptive feedback linearization controller, called adaptive augmented torque (AAT) control, for the aerial robot. The control law is based on a feedback linearization controller with model reference adaptive controller and a tracking error-based augmented term. Using the input-to-state (ISS) stability concept, a bound on the parameter estimation error is also developed. In the presented methodology, the controller uses estimated values of system parameters obtained from the adaptive mechanism and the tracking error to compute the control input using the AAT control law. An adaptive law for estimating unknown parameters is obtained using the strictly positive real-Lyapunov method. The asymptotic stability of the closed-loop system is analyzed via the Lyapunov theory. Simulations implemented in MATLAB and ROS/Gazebo and preliminary hardware experiments are presented to validate the theoretical results and to corroborate the performance of the AAT control law.

scholarly journals A Novel Data-Driven Terminal Iterative Learning Control with Iteration Prediction Algorithm for a Class of Discrete-Time Nonlinear Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Shangtai Jin ◽  
Zhongsheng Hou ◽  
Ronghu Chi
Keyword(s):  
Nonlinear Systems ◽  
Discrete Time ◽  
Iterative Learning Control ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Data Driven ◽  
Prediction Algorithm ◽  
Control Law ◽  
Control Input

A data-driven predictive terminal iterative learning control (DDPTILC) approach is proposed for discrete-time nonlinear systems with terminal tracking tasks, where only the terminal output tracking error instead of entire output trajectory tracking error is available. The proposed DDPTILC scheme consists of an iterative learning control law, an iterative parameter estimation law, and an iterative parameter prediction law. If the partial derivative of the controlled system with respect to control input is bounded, then the proposed control approach guarantees the terminal tracking error convergence. Furthermore, the control performance is improved by using more information of predictive terminal outputs, which are predicted along the iteration axis and used to update the control law and estimation law. Rigorous analysis shows the monotonic convergence and bounded input and bounded output (BIBO) stability of the DDPTILC. In addition, extensive simulations are provided to show the applicability and effectiveness of the proposed approach.

Modelling of a logarithmic parameter adaptation law for adaptive control of mechanical manipulators

Robotica ◽  
2006 ◽  
Vol 24 (4) ◽  
pp. 523-525 ◽  
Author(s):  
Recep Burkan
Keyword(s):  
Parameter Estimation ◽  
Adaptive Control ◽  
Tracking Error ◽  
Lyapunov Theory ◽  
Control Law ◽  
Trigonometric Functions ◽  
Parameter Adaptation ◽  
Mechanical Manipulators ◽  
Adaptation Law ◽  
Adaptive Control Law

In the paper, a new adaptive control law for controlling robot manipulators is derived based on the Lyapunov theory; trigonometric functions are used for the derivation of the parameter estimation law. In this note, we have derived a logarithmic parameter estimation law based on a previous paper, and the boundedness of tracking error has been shown.

Robust Framework for the Computed Torque Control of Nondeterministic Multibody Dynamic Systems

2016 ◽  
Author(s):  
Sahand Sabet ◽  
Mohammad Poursina
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Feedback Linearization ◽  
Torque Control ◽  
Control Law ◽  
Mathematical Framework ◽  
Computed Torque Control ◽  
Highly Nonlinear ◽  
Multibody Problems ◽  
Computed Torque

Considering uncertainty is inarguably a crucial aspect of dynamic analysis, design, and control of a mechanical system. When it comes to multibody problems, the effect of uncertainty in the system’s parameters and excitations becomes even more significant due to the accumulation of inaccuracies. For this reason, this paper presents a detailed research on the use of the Polynomial Chaos Expansion (PCE) method for the control of nondeterministic multibody systems. PCE is essentially a way to compactly represent random variables. In this scheme, each stochastic response output and random input is projected onto the space of appropriate independent orthogonal polynomial basis functions. In the field of robotics, a required task is to force robotic arms to follow designated paths. Controlling such systems usually leads to difficulties since the dynamic equations of multibody problems are highly nonlinear. Computed Torque Control Law (CTCL) is able to overcome these difficulties by using feedback linearization to evaluate the required torque/force at any time to make the system follow a trajectory. In this paper, a mathematical framework is introduced to apply the Computed Torque Control Law to a multibody system with uncertainty. Surprisingly, it is shown that using this control scheme, uncertainty in geometry does not affect the closed-loop equations of controlled systems. Both the intrusive PCE method and the Monte Carlo approach are used to control a fully actuated two-link planar elbow arm where each link is required to follow a specified path. Lastly, a comparison of the time efficiency and accuracy between the traditionally used Monte Carlo method and the intrusive PCE is presented. The results indicate that the intrusive PCE approach can provide better accuracy with much less computation time than the Monte Carlo method.

Lyapunov-Based Formation Control of Underwater Robots

Robotica ◽  
2019 ◽  
Vol 38 (6) ◽  
pp. 1105-1122 ◽  
Author(s):  
Ali Keymasi Khalaji ◽  
Rasoul Zahedifar
Keyword(s):  
Feedback Linearization ◽  
Formation Control ◽  
Control Algorithm ◽  
Kinematic Model ◽  
Lyapunov Theory ◽  
Control Law ◽  
Underwater Robots ◽  
Oceanographic Research ◽  
Inspection And Maintenance ◽  
Reliability And Robustness

SUMMARYToday, automatic diving robots are used for research, inspection, and maintenance, extensively. Control of autonomous underwater robots (AUVs) is challenging due to their nonlinear dynamics, uncertain models, and the system underactuation. Data collection using underwater robots is increasing within the oceanographic research community. Also, the ability to navigate and cooperate in a group of robots has many advantages compared with individual navigations. Among them, the effectiveness of using resources, the possibility of robots’ collaboration, increasing reliability, and robustness to defects can be pointed out. In this paper, the formation control of underwater robots has been studied. First, the kinematic model of the AUV is presented. Next, a novel Lyapunov-based tracking control algorithm is investigated for the leader robot. Subsequently, a control law is designed using Lyapunov theory and feedback linearization techniques to navigate a group of follower robots in a desired formation associated with the leader and follow it simultaneously. In the obtained results for different reference paths and various formations, the effectiveness of the proposed algorithm is represented.

A Nonlinear Automatic Landing Control System for a UAV

2011 ◽  
Author(s):  
Ghassan M. Atmeh ◽  
Wahba I. Al-Taq ◽  
Zeaid Hasan
Keyword(s):  
Feedback Linearization ◽  
Linearization Method ◽  
State Feedback Control ◽  
Control Law ◽  
Control Input ◽  
Nonlinear Controller ◽  
Nonlinear Simulation ◽  
Automatic Landing ◽  
Ground Effects ◽  
Linear State

An automatic landing system for an unmanned aerial vehicle (UAV) is presented in the following paper. The nonlinear aircraft model with thrust, elevator, rudder and aileron deflections as control inputs is established using the appropriate aerodynamic data. The flight trajectory the airplane is expected to travel during landing is then defined. A nonlinear control law, using feedback linearization method, is designed to develop the automatic landing controller for the UAV aircraft. A linear state-feedback control law is also designed for means of comparison with the nonlinear controller. The elevator is employed for longitudinal control whereas the rudder and aileron aid in lateral control. Thrust is the control input for velocity control, which is held constant during landing. A nonlinear simulation, incorporating wind shear and ground effects, is run using MATLAB/Simulink to assess the controllers’ integrity. The auto-landing system designed in this paper is meant to increase the autonomy of the UAV to eventually reach a fully autonomous system. Simulation results show the importance of designing the controller considering such effects. Landing trajectory tracking performance by the nonlinear controller is of great tone.

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.

Parameter estimation and upper bounding adaptation in adaptive-robust control approaches for trajectory control of robots

Robotica ◽  
2002 ◽  
Vol 20 (6) ◽  
pp. 653-660 ◽  
Author(s):  
Ibrahim Uzmay ◽  
Recep Burkan
Keyword(s):  
Parameter Estimation ◽  
Adaptive Control ◽  
Robust Control ◽  
Uncertain System ◽  
Adaptive Controller ◽  
Robust Adaptive Control ◽  
Adaptive Robust Control ◽  
Lyapunov Theory ◽  
Control Law ◽  
Control Input

In this paper a new robust adaptive control law for n-link robot manipulators with parametic uncertainties is derived using the Lyapunov theory thus guaranteed the stability of an uncertain system. The novelty of the adaptive robust control algorithm is that manipulator parameters and adaptive upper bounding functions are estimated to control the system properly, and the adaptive robust control law is also updated as an exponential function of manipulator kinematics, inertia parameters and tracking errors. The proposed adaptive control input includes a parameter estimation law as an adaptive controller and an additional control input vector as a robust controller. The developed approach has the advantages of both adaptive and robust control laws, without their discolour tags.

Disturbance observer-based optimal flight control of near space vehicle with external disturbance

2019 ◽  
Vol 42 (2) ◽  
pp. 272-284 ◽  
Author(s):  
Rongsheng Xia ◽  
Qingxian Wu ◽  
Shuyi Shao
Keyword(s):  
Flight Control ◽  
Disturbance Observer ◽  
Estimation Error ◽  
External Disturbance ◽  
Tracking Error ◽  
Space Vehicle ◽  
Optimal Feedback Control ◽  
Control Input ◽  
Disturbance Estimation ◽  
Near Space

This paper presents a disturbance observer-based robust optimal flight control strategy for near space vehicle (NSV) attitude system with external time-varying disturbance generated by an exogenous system. For the purpose of eliminating the effect of the disturbance, nonlinear disturbance observer (NDO) technique is used and the disturbance estimation error is guaranteed to be globally exponential convergence. Then, based on the disturbance estimation result and desired trajectory signal, a steady state control input is presented and the optimal tracking problem of original system with external disturbance can be converted into the optimal regulation problem of a nominal error system. Furthermore, a single network-based adaptive dynamic programming (ADP) method is applied to obtain the corresponding optimal feedback control law. Finally, all the signals in closed-loop system are proved to be uniformly ultimately bounded (UUB) and the tracking error can converge to a sufficiently small bound. Simulation tests about NSV attitude system are given to verify the effectiveness of proposed robust optimal flight control scheme.

scholarly journals Nonlinear Control System Design for Active Lubrication of Hydrostatic Thrust Bearing

Coatings ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 426
Author(s):  
Yuanpeng Sha ◽  
Changhou Lu ◽  
Wei Pan ◽  
Shujiang Chen ◽  
Peiqi Ge
Keyword(s):  
Nonlinear Control ◽  
Feedback Linearization ◽  
Thrust Bearing ◽  
Control Method ◽  
Sliding Mode ◽  
Linearization Method ◽  
Control Law ◽  
Machining Accuracy ◽  
Control Input ◽  
Vibration Absorption

The active controlled hydrostatic bearing is becoming more and more popular because of its accuracy, safety, as well as low vibration and noise. In this paper, we present a design approach for a hydrostatic thrust bearing system, where the analytical nonlinear state space equation of the system is established first, and then three kinds of control inputs are investigated and compared to each other. It is found that, by selecting the supply pressure as the control input, we could obtain an affine nonlinear system, which could be linearized by the feedback linearization method, and its robustness could be enhanced by the sliding mode control method. The tracking control law could be easily obtained with the linearized system. The simulation verifies the effectiveness of the nonlinear control law. The proposed nonlinear control model might have a positive effect on the improvement of the machining accuracy, safety, and vibration absorption.

Robust estimation and control of robotic manipulators

Robotica ◽  
1995 ◽  
Vol 13 (3) ◽  
pp. 223-231 ◽  
Author(s):  
Zhihua Qu ◽  
Darren M. Dawson ◽  
John F. Dorsey ◽  
John D. Duffie
Keyword(s):  
Robust Control ◽  
Robust Estimation ◽  
Robot Manipulator ◽  
Estimation Error ◽  
Torque Control ◽  
Robust Estimator ◽  
Control Law ◽  
Robot Dynamics ◽  
Estimation And Control ◽  
And Control

SummaryFor the trajectory following problem of a robot manipulator, a robust estimation and control scheme which requires only position measurements is proposed to guarantee uniform ultimate bounded stability under significant uncertainties and disturbances in the robot dynamics. The scheme combines a class of robust control laws with a robust estimator where the robust control law can be chosen to be either a modification of the standard computed torque control law or simply a linear and decentralized “PD” control law. The proposed robust estimator is also linear and decentralized for easy implementation. Constructive choices of the gains in the control law and estimator are proposed which depend only on the coefficients of a polynomial bounding function of the unknown dynamics. The asymptotic stability of the tracking errors and the estimation error is also investigated. Experimentation results verify the theoretical analysis.

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