A linear input/output data-based predictive control with integral action property

2001 ◽  
Vol 34 (25) ◽  
pp. 631-636
Author(s):  
In-Hyoup Song ◽  
Kee-Yoon Yoo ◽  
Hyun-Ku Rhee
Keyword(s):  
Predictive Control ◽  
Input Output ◽  
Output Data ◽  
Integral Action

Analysis and Design of a Linear Input/Output Data-Based Predictive Control

2001 ◽  
Vol 40 (20) ◽  
pp. 4292-4301 ◽  
Author(s):  
In-Hyoup Song ◽  
Kee-Youn Yoo ◽  
Hyun-Ku Rhee
Keyword(s):  
Predictive Control ◽  
Input Output ◽  
Output Data ◽  
Analysis And Design

scholarly journals Identification and Predictive Control by p-norm Minimization

10.14311/802 ◽  
2006 ◽  
Vol 46 (1) ◽  
Author(s):  
J. Pekař ◽  
J. Štecha
Keyword(s):  
Least Squares ◽  
Predictive Control ◽  
System Parameter ◽  
Total Least Squares ◽  
Input Output ◽  
Time System ◽  
Output Data ◽  
Real Time System ◽  
Norm Minimization ◽  
System Equations

Real time system parameter estimation from the set of input-output data is usually solved by minimization of quadratic norm errors of system equations – known in the literature as least squares (LS) or its modification as total least squares (TLS) or mixed LS and TLS. It is known that the utilization of the p-norm (1?p

scholarly journals A New Model-Free Predictive Control Method Using Input and Output Data

2014 ◽  
Vol 1042 ◽  
pp. 182-187 ◽  
Author(s):  
Shigeru Yamamoto
Keyword(s):  
Mathematical Model ◽  
Predictive Control ◽  
Control Method ◽  
Just In Time ◽  
Control Input ◽  
Input Output ◽  
Output Data ◽  
Online Data ◽  
Model Free ◽  
The Mathematical Model

The purpose of this paper is to present a new predictive control utilizing online data and stored data of input/output of the controlled system. The conventional predictive control methods utilize the mathematical model of the control system to predict an optimal future input to control the system. The model is usually obtained by a standard system identification method from the measured input/output data. The proposed method does not require the mathematical model to predict the optimal future control input to achieve the desired output. This control strategy, called just-in-time, was originally proposed by Inoue and Yamamoto in 2004. In this paper, we proposed a simplified version of the original just-in-time predictive control method.

scholarly journals Input-output Data-driven Modeling and MIMO Predictive Control of an RCCI Engine Combustion

2021 ◽  
Vol 54 (20) ◽  
pp. 406-411
Author(s):  
Behrouz Khoshbakht Irdmousa ◽  
Jeffrey Donald Naber ◽  
Javad Mohammadpour Velni ◽  
Hoseinali Borhan ◽  
Mahdi Shahbakhti
Keyword(s):  
Predictive Control ◽  
Data Driven ◽  
Input Output ◽  
Output Data ◽  
Engine Combustion ◽  
Data Driven Modeling

scholarly journals A modular framework to generate robust biped locomotion: from planning to control

2021 ◽  
Vol 3 (9) ◽  
Author(s):  
Mohammadreza Kasaei ◽  
Ali Ahmadi ◽  
Nuno Lau ◽  
Artur Pereira
Keyword(s):  
Numerical Simulations ◽  
Predictive Control ◽  
Biped Locomotion ◽  
Input Output ◽  
Dynamics Model ◽  
Biped Robots ◽  
The Core ◽  
Simulation Results ◽  
Reference Trajectories ◽  
Modular Framework

AbstractBiped robots are inherently unstable because of their complex kinematics as well as dynamics. Despite many research efforts in developing biped locomotion, the performance of biped locomotion is still far from the expectations. This paper proposes a model-based framework to generate stable biped locomotion. The core of this framework is an abstract dynamics model which is composed of three masses to consider the dynamics of stance leg, torso, and swing leg for minimizing the tracking problems. According to this dynamics model, we propose a modular walking reference trajectories planner which takes into account obstacles to plan all the references. Moreover, this dynamics model is used to formulate the controller as a Model Predictive Control (MPC) scheme which can consider some constraints in the states of the system, inputs, outputs, and also mixed input-output. The performance and the robustness of the proposed framework are validated by performing several numerical simulations using MATLAB. Moreover, the framework is deployed on a simulated torque-controlled humanoid to verify its performance and robustness. The simulation results show that the proposed framework is capable of generating biped locomotion robustly.

scholarly journals Method of value chain mapping with international input–output data: application to the agricultural value chain in three Greater Mekong Subregion countries

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Ikuo Kuroiwa
Keyword(s):  
Value Chain ◽  
Value Added ◽  
Input Output ◽  
Output Data ◽  
Primary Input ◽  
Goods And Services ◽  
Unit Structure ◽  
Data Application ◽  
Final Output ◽  
Greater Mekong Subregion

AbstractExtending the technique of unit structure analysis, which was originally developed by Ozaki (J Econ 73(5):720–748, 1980), this study introduces a method of value chain mapping that uses international input–output data and reveals both the upstream and downstream transactions of goods and services, as well as primary input (value added) and final output (final demand) transactions, which emerge along the entire value chain. This method is then applied to the agricultural value chain of three Greater Mekong Subregion countries: Thailand, Vietnam, and Cambodia. The results show that the agricultural value chain has been increasingly internationalized, although there is still room to benefit from participating in global value chains, especially in a country such as Cambodia. Although there are some constraints regarding the methodology and data, the method proves useful in tracing the entire value chain.

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