scholarly journals High-Order Differential Feedback Control for Quadrotor UAV: Theory and Experimentation

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2001
Author(s):  
Guoyuan Qi ◽  
Shengli Ma ◽  
Xitong Guo ◽  
Xia Li ◽  
Jianchuan Guo
Keyword(s):  
Feedback Control ◽  
Trajectory Tracking ◽  
Absolute Error ◽  
High Order ◽  
Superior Performance ◽  
Aerial Vehicle ◽  
The Stability ◽  
Optical Positioning ◽  
Fifth Order ◽  
Reference Trajectories

A model-independent control strategy called high-order differential feedback control (HODFC) is applied to a quadrotor unmanned aerial vehicle (QUAV) based on a semi-autopilot indoor optical positioning system. The affine system form of the quadrotor model is provided to facilitate the design of the HODFC. A fifth-order high order differentiator (HOD) is introduced to estimate with high precision the derivatives of the reference input and the QUAV system’s states. A filtering signal of the control output is incorporated in the control law to overcome the system model’s unknown part in the HODFC scheme. The stability of both the HODFC and the HOD are proved. The physical and straightforward parameters are provided to make the HODFC scheme for the QUAV easy to operate. The real-time trajectory tracking experiments with varied reference trajectories and disturbances are carried out to illustrate the superior performance of the HODFC versus the proportional-integral-derivative (PID) method, in terms of the mean of absolute error, the integral of absolute error and the integral of the time-weighted absolute error. The results also demonstrate that the HODFC has superiority in static and dynamic trajectory tracking, especially when the system is disturbed.

High order differential feedback controller design and implementation for a Stewart platform

2019 ◽  
Vol 26 (11-12) ◽  
pp. 976-988 ◽  
Author(s):  
Mustafa S Ayas ◽  
Erdinc Sahin ◽  
Ismail H Altas
Keyword(s):  
Trajectory Tracking ◽  
Absolute Error ◽  
Parallel Manipulators ◽  
Stewart Platform ◽  
High Order ◽  
Feedback Controller ◽  
Proportional Integral Derivative ◽  
Proportional Integral Derivative Controller ◽  
Proportional Integral ◽  
Reference Trajectories

Stewart platform or other parallel manipulators with a Stewart structure are commonly used in flight simulators, surgical operations, medical rehabilitation processes, machine tools, industrial applications, etc. Therefore, researchers have paid attention to position control of these manipulators in addition to their design and development process. In this study, a developed Stewart platform and its inverse kinematic analysis are presented first. Then, a model-free control scheme called a high order differential feedback controller scheme is designed for the Stewart platform in order to improve its trajectory tracking performance and robustness against to different reference trajectories. Real-time trajectory tracking experiments with varied reference trajectories are carried out to show the robustness and effectiveness of the high order differential feedback controller scheme compared to the traditional proportional–integral–derivative controller of which the parameters are optimally tuned. The obtained visual trajectory tracking results and numerical performance results based on error-based performance measurement metrics such as integral of absolute error, integral of squared error, and integral of time-weighted absolute error are provided for both the proposed high order differential feedback controller scheme and the optimal tuned proportional–integral–derivative controller. Experimental results show that the proposed high order differential feedback controller scheme is more robust than the proportional–integral–derivative controller. Furthermore, the high order differential feedback controller scheme has superiority in both transient and steady-state responses and even the parameters of the proportional–integral–derivative controller are optimally tuned.

DirCol5i: Trajectory Optimization for Problems With High-Order Derivatives

2018 ◽  
Vol 141 (3) ◽  
Author(s):  
Matthew P. Kelly
Keyword(s):  
Nonlinear System ◽  
Trajectory Optimization ◽  
Optimization Problems ◽  
High Order ◽  
Robot Arm ◽  
Minimum Jerk ◽  
Higher Derivatives ◽  
A Chain ◽  
Fifth Order ◽  
Reference Trajectories

In this technical brief, we focus on solving trajectory optimization problems that have nonlinear system dynamics and that include high-order derivatives in the objective function. This type of problem comes up in robotics—for example, when computing minimum-snap reference trajectories for a quadrotor or computing minimum-jerk trajectories for a robot arm. DirCol5i is a transcription method that is specialized for solving this type of problem. It uses the fifth-order splines and analytic differentiation to compute higher-derivatives, rather than using a chain-integrator as would be required by traditional methods. We compare DirCol5i to traditional transcription methods. Although it is slower for some simple optimization problems, when solving problems with high-order derivatives DirCol5i is faster, more numerically robust, and does not require setting up a chain integrator.

Scalable Approach to High Coverages on Oxides via Iterative Training of a Machine-Learning Algorithm

2019 ◽  
Author(s):  
Andrew Medford ◽  
Shengchun Yang ◽  
Fuzhu Liu
Keyword(s):  
Machine Learning ◽  
Chemical Potential ◽  
Learning Algorithm ◽  
Absolute Error ◽  
Low Energy ◽  
Training Data ◽  
High Coverage ◽  
Metal Compounds ◽  
Adsorption Energies ◽  
The Stability

Understanding the interaction of multiple types of adsorbate molecules on solid surfaces is crucial to establishing the stability of catalysts under various chemical environments. Computational studies on the high coverage and mixed coverages of reaction intermediates are still challenging, especially for transition-metal compounds. In this work, we present a framework to predict differential adsorption energies and identify low-energy structures under high- and mixed-adsorbate coverages on oxide materials. The approach uses Gaussian process machine-learning models with quantified uncertainty in conjunction with an iterative training algorithm to actively identify the training set. The framework is demonstrated for the mixed adsorption of CH<sub>x</sub>, NH<sub>x</sub> and OH<sub>x</sub> species on the oxygen vacancy and pristine rutile TiO<sub>2</sub>(110) surface sites. The results indicate that the proposed algorithm is highly efficient at identifying the most valuable training data, and is able to predict differential adsorption energies with a mean absolute error of ~0.3 eV based on <25% of the total DFT data. The algorithm is also used to identify 76% of the low-energy structures based on <30% of the total DFT data, enabling construction of surface phase diagrams that account for high and mixed coverage as a function of the chemical potential of C, H, O, and N. Furthermore, the computational scaling indicates the algorithm scales nearly linearly (N<sup>1.12</sup>) as the number of adsorbates increases. This framework can be directly extended to metals, metal oxides, and other materials, providing a practical route toward the investigation of the behavior of catalysts under high-coverage conditions.

scholarly journals Evaluation of Process Control Schemes for Sour Water Strippers in Petroleum Refining

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 363
Author(s):  
Chii-Dong Ho ◽  
Yih-Hang Chen ◽  
Chao-Min Chang ◽  
Hsuan Chang
Keyword(s):  
Feedback Control ◽  
Prediction Models ◽  
Feedforward Control ◽  
Absolute Error ◽  
Dynamic Responses ◽  
Control Schemes ◽  
Online Measurements ◽  
Measurement Delay ◽  
Water Feed ◽  
Sour Water

For the sour water strippers in petroleum refinery plants, three prediction models were developed first, including the estimators of sour water feed concentrations using convenient online measurements, the minimum reboiler duty and the corresponding internal temperature at a specific location (Tstage,29). Feedforward control schemes were developed based on these prediction models. Four categories of control schemes, including feedforward, feedback, feedback with external reset, and feedforward-feedback, were proposed and evaluated by the rigorous dynamic simulation model of the sour water stripper for their dynamic responses to the sour water feed stream disturbances. The comparison of control performance, in terms of the settling time, integrated absolute error (IAE) of the NH3 concentration of the stripped sour water and IAE of the specific reboiler duty, reveals that FFT (feedforward control of Tstage,29) and FBA-DT3 (feedback control with 3 min concentration measurement delay) are the best control schemes. The second-best control scheme is FBAT (cascade feedback control of concentration with temperature).

Optimization of the Navy’s three-dimensional mine impact burial prediction simulation model, Impact35, using high-order numerical methods

2021 ◽  
pp. 154851292110286
Author(s):  
Athanasios Donas ◽  
Ioannis Famelis ◽  
Peter C Chu ◽  
George Galanis
Keyword(s):  
Numerical Analysis ◽  
Numerical Methods ◽  
Prediction Model ◽  
Simulation Model ◽  
Three Dimensional ◽  
Simulation System ◽  
High Order ◽  
Alternative Methodology ◽  
Runge Kutta ◽  
Fifth Order

The aim of this paper is to present an application of high-order numerical analysis methods to a simulation system that models the movement of a cylindrical-shaped object (mine, projectile, etc.) in a marine environment and in general in fluids with important applications in Naval operations. More specifically, an alternative methodology is proposed for the dynamics of the Navy’s three-dimensional mine impact burial prediction model, Impact35/vortex, based on the Dormand–Prince Runge–Kutta fifth-order and the singly diagonally implicit Runge–Kutta fifth-order methods. The main aim is to improve the time efficiency of the system, while keeping the deviation levels of the final results, derived from the standard and the proposed methodology, low.

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