A robust feedback linearization controller is presented for attitude control of an unmanned aerial vehicle (UAV). The objective of this controller is to make the roll angle, pitch angle, and yaw angle track the given trajectories(commands) respectively. This design is developed using dynamic inversion and extended state observer (ESO). Firstly, dynamic inversion is used to linearize and decouple UAV attitude system into three single-input-single-output (SISO) systems, then three proportional-derivative (PD) controllers are designed for these linearized systems. Extended state observers are used to estimate and compensate unmodeled dynamics and extent disturbances. Simulation results show that the proposed controller is effective and robust.

ADRC uses an Extended state observer to linearize the Quadrotor's Nonlinear dynamics (similar to Feedback linearization). This makes it capable of eliminating disturbances (robustness).

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.

Control barrier function as safety filter with disturbance 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

code for paper: [1] M. Hu, X. Wang, Y. Bian, D. Cao, and H. Wang, “Disturbance Observer-Based Cooperative Control of Vehicle Platoons Subject to Mismatched Disturbance,” IEEE Transactions on Intelligent Vehicles, vol. 8, no. 4, pp. 2748–2758, Apr. 2023.

This paper presents a data-driven control design framework to achieve robust tracking control without exploiting mathematical model of nonlinear underactuated mechanical systems (UMS). The method leverages the differential flatness property of linearized systems and online estimation and compensation of disturbances by active disturbance rejection control (ADRC). The differentially flat output is derived directly from measured data with unknown dynamics and parameters of UMS by the flat output identification (FOID) algorithm. A reduced nominal model of UMS is proposed to simplify the process of finding flat output and trajectory planning. Technique of sparse regression is applied to identify the relationships between flat output and system states, which reduces the order of the well-known extended state observer (ESO) and thereby make the ESO more effective for both trajectory planning and tracking in terms …

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.

A whole-body controller with disturbance rejection through a momentum-based observer for quadruped robots

APDOB - Adaptive Periodic-Disturbance Observer

DisturbanceObserver NonlinearDisturbanceObserver

No description available

Supplementary material to a paper to the IEEE IROS 2016 conference (impedance control and disturbance observer for hydraulic robot arm)

This project aims to enhance quadrotor drone control by implementing a disturbance observer-based controller. It detects and addresses uncertainties during flight, improving performance in turbulent conditions and motion control accuracy.

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: atta.oveisi@rub.de 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.

Disturbance Observer-based Robust Integral Control Barrier Functions for Nonlinear Systems with High Relative Degree [ACC '24]

PX4 and SITL/jMAVSim simulation for the ICUAS 2020 conference paper submission 'Disturbance Observer-Based Integral Backstepping Controller for Multirotor UAVs'

Disturbance Observer based trajectory tracking for MAVs

A non linear disturbance observer and PID control law for the quadrotor dynamic system.

This repository includes some simulation examples for the prescribed-time extended state observer (PESO) and the prescribed-time disturbance rejection control (PDRC) applied to several mechanical and aerospace systems

The set of codes provided here includes five different methods for controlling the torque output of the system; namely Proportional Integral Derivative Control (PID), cascaded PID control, Cascaded PID control with a disturbance observer (DoB), differential flatness (DF) with a DoB, feedforward (FF) PID control with a DoB and Sliding Mode Control (SMC).

QDOB - Quasiperiodic Disturbance Observer

This is a demo code for paper "Bias-Variance Trade-off in Kalman Filter-Based Disturbance Observers".

MATLAB Implementation of Adaptive H-Infinity MPC-Fuzzy Control with Sliding-Mode Disturbance Observer for 5-DOF Robotic Manipulator

Disturbance Observer Based Control for Quasi Continuum Manipulators

Safety-Critical Control With Control Barrier Function Based on Disturbance Observer

This Project is based on Arduino Due platform

ROS2 Gazebo simulation for 6-DOF manipulator control using Nonlinear MPC and Disturbance Observer

Position control of a pendulum using sliding mode controller with finite time disturbance observer

BLDC_sim is a Python simulator for BLDC motors featuring FOC and Lyapunov-based PID control. Visualize motor dynamics, phase voltages, and speed control with an interactive GUI. Ideal for learning, testing control strategies, and experimenting with disturbance observers.