Discover @attaoveisi Open Source projects

Atta Oveisi (@attaoveisi)

attaoveisi

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182
Global Rank 132,573 (Top 5 %)
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Registered almost 9 years ago
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C++
52.0 %

MATLAB
20.0 %

Python
8.0 %

Scilab 8.0 %

Jupyter Notebook
8.0 %

C
4.0 %
Location 🇩🇪 Germany
Country Total Rank 8,319
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Scilab 1

MATLAB
24

C++
2,090

C
2,098

Jupyter Notebook
3,695

Python
4,486

AFISMC

a new observer-based adaptive fuzzy integral sliding mode controller (AFISMC) is proposed based on the Lyapunov stability theorem. The plant under study is subjected to a square-integrable disturbance and is assumed to have mismatch uncertainties both in state- and input-matrices. In addition, a norm-bounded time varying term is introduced to address the possible existence of un-modelled/nonlinear dynamics. Based on the classical sliding mode controller (SMC), the equivalent control effort is obtained to satisfy the sufficient requirement of SMC and then the control law is modified to guarantee the reachability of the system trajectory to the sliding manifold. The sliding surface is compensated based on the observed states in the form of linear matrix inequality (LMI). In order to relax the norm-bounded constrains on the control law and solve the chattering problem of SMC, a fuzzy logic (FL) inference mechanism is combined with the controller. An adaptive law is then introduced to tune the parameters of the fuzzy system on-line. Finally, by aiming at evaluating the validity of the controller and the robust performance of the closed-loop system, the proposed regulator is implemented on a real-time mechanical vibrating system.

Extended_State_Observer

a disturbance rejection-based solution to the problem of robust output regulation of linear systems. The difference between the underlying plant and its nominal mathematical model is represented by two classes of disturbances. The first class is generated by an autonomous linear system while the other class has no specific dynamical structure. Robustness against the first disturbance class is achieved by the internal model principle. Next, in the framework of disturbance rejection control, an extended state observer (ESO) is designed to estimate and compensate for the second class of disturbances. As a result, the proposed output regulation method can deal with a vast range of uncertainties. The stability of the closed loop system is investigated and results on practical regulation are drawn. Keywords—Output regulation, extended state observer, disturbance rejection, linear system, robustness

Disturbance_observer

In this note, disturbance rejection control (DRC) based on unknown input observation (UIO), and disturbance-observer based control (DOBC) methods are revisited for a class of MIMO systems with mismatch disturbance conditions. In both of these methods, the estimated disturbance is considered to be in the feedback channel. The disturbance term could represent either unknown mismatched signals penetrating the states, or unknown dynamics not captured in the modeling process, or physical parameter variations not accounted for in the mathematical model of the plant. Unlike the high-gain approaches and variable structure methods, a systematic synthesis of the state/disturbance observer-based controller is carried out. For this purpose, first, using a series of singular value decompositions, the linearized plant is transformed into disturbance-free and disturbance-dependent subsystems. Then, functional state reconstruction based on generalized detectability concept is proposed for the disturbance-free part. Then, a DRC based on quadratic stability theorem is employed to guarantee the performance of the closed-loop system. An important contribution offered in this article is the independence of the estimated disturbance from the control input which seem to be missing in the literature for disturbance decoupling problems. In the second method, DOBC is reconsidered with the aim of achieving a high level of robustness against modeling uncertainties and matched/mismatched disturbances, while at the same time retaining performance. Accordingly, unlike the first method, DRC, full information state observation is developed independent of the disturbance estimation. An advantage of such a combination is that disturbance estimation does not involve output derivatives. Finally, the case of systems with matched disturbances is presented as a corollary of the main results.

Coupling_ABAQUS_MATLAB

we introduce a new framework for running the finite element (FE) packages inside an online Loop together with MATLAB. Contrary to the Hardware-in-the-Loop techniques (HiL), in the proposed Software-in-the-Loop framework (SiL), the FE package represents a simulation platform replicating the real system which can be out of access due to several strategic reasons, e.g., costs and accessibility. Practically, SiL for sophisticated structural design and multi-physical simulations provides a platform for preliminary tests before prototyping and mass production. This feature may reduce the new product’s costs significantly and may add several flexibilities in implementing different instruments with the goal of shortlisting the most cost-effective ones before moving to real-time experiments for the civil and mechanical systems. The proposed SiL interconnection is not limited to ABAQUS as long as the host FE package is capable of executing user-defined commands in FORTRAN language. The focal point of this research is on using the compiled FORTRAN subroutine as a messenger between ABAQUS/CAE kernel and MATLAB Engine. In order to show the generality of the proposed scheme, the limitations of the available SiL schemes in the literature are addressed in this paper. Additionally, all technical details for establishing the connection between FEM and MATLAB are provided for the interested reader. Finally, two numerical sub-problems are defined for offline and online post-processing, i.e., offline optimization and closed-loop system performance analysis in control theory. Keywords: software-in-the-loop; finite element; optimal placement; structural optimization; vibration control.

MPC

Observer-Based Repetitive Model Predictive Control in Active Vibration Suppression

Hybrid_Fuzzy_Kalman_Filter

Mixed Kalman-Fuzzy Sliding Mode State Observer in Disturbance Rejection Control of a Vibrating Smart Structure Atta Oveisi*1, Tamara Nestorović1 1Ruhr-Universität Bochum, Mechanik adaptiver Systeme, Institut Computational Engineering, D-44801, Bochum, Germany. E-Mail: [email protected] ABSTRACT In the controllers that are synthesized on a nominal model of the nonlinear plant, the parametric matched uncertainties and nonlinear/unmodeled dynamics of high order nature can significantly affect the performance of the closed-loop system. In this note, owing to the robust character of the sliding mode observer against modeling perturbations, measurement noise, and unknown disturbances and due to the non-fragile behavior of the Kalman filter against process noise, a mixed Kalman sliding mode state-observer is proposed and later enhanced by the addition of an intelligent fuzzy agent. In light of the proposed technique, the chattering phenomena and the conservative boundary neighboring layer of the high gain sliding mode observer are addressed. Then, a robust active disturbance rejection controller is developed by using static feedback of the estimated states using direct Lyapunov quadratic stability Theorem. The reduced order plant for control design purposes is subjected to some simulated square-integrable disturbances and is assumed to have mismatch uncertainties in system matrices. Finally, the robust performance of the closed-loop scheme with respect to the mentioned perturbation signals and modeling imperfections is tested by implementing the control system on a mechanical vibrating smart cantilever beam. Keywords: Fuzzy system; Nonlinear control; Active disturbance rejection; Kalman Filter; Vibration suppression.

Anti-windup-Compensator

List of additional files available for the interested reader of "Vibration Control Subjected to Windup Problem: An Applied View on Analysis and Synthesis with Convex Formulation"

turtlebot2_Localization

This is the localization of the Turtlebo2 by fusing imu, odometry, and GPS data

L2C_AttBot2

Low level controller for AttBot2

PathPlanning_Classics

The path planning and graph search algorithms

EKF

implementation of extended kalman filter for sensor fusion

turtlebot2_SLAM

This is an example of how to to integrate a fast SLAM algorithm with Turtlebot2

occupancy_grid_mapping

This is a pseudo C++ package for doing mapping based on the occupancy grid

Non-fragile_observer_based_Controller

Robust Non-Fragile Observer-based Controller

pseudo_particle_filter

An implementation of MCL

Subspace_System_Identification

Frequency Domain Subspace Identification of Dynamical Systems

AttBot_Rasberry

AttBot2_SLAM

This is a package for SLAM based on hector_slam package together with AttBot 2.0

L2C_GPS_Logger

GAN

generative adversarial network

Jupyter Notebook

AttBot2_Localization

The repository is a benchmark for localizing AttBot2.0 using robot_localization package

CNN

Convolution neural network

L2C_Act_AttBot2

Controller for AttBot2

Particle_explosion

A graphic interface for particle explosion example based on SDL

ANN

Artificial neural network

Jupyter Notebook

Robot_arm_mover_Gazebo

ORPM