Personal robot assisting transportation to support active human life — Reference generation based on model predictive control for robust quick turning

2014 ◽  
Author(s):  
Noriaki Hirose ◽  
Ryosuke Tajima ◽  
Kazutoshi Sukigara ◽  
Minoru Tanaka
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Human Life ◽  
Personal Robot

Posture Stabilization Control of Personal Robot Based on Model Predictive Control

2017 ◽  
Vol 100 (7) ◽  
pp. 32-44
Author(s):  
NORIAKI HIROSE ◽  
RYOSUKE TAJIMA ◽  
KAZUTOSHI SUKIGARA ◽  
NAGISA KOYAMA ◽  
MINORU TANAKA ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Stabilization Control ◽  
Personal Robot ◽  
Posture Stabilization

Posture Stabilization Control of Personal Robot based on Model Predictive Control

2015 ◽  
Vol 135 (3) ◽  
pp. 172-181 ◽  
Author(s):  
Noriaki Hirose ◽  
Ryosuke Tajima ◽  
Kazutoshi Sukigara ◽  
Nagisa Koyama ◽  
Minoru Tanaka ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Stabilization Control ◽  
Personal Robot ◽  
Posture Stabilization

scholarly journals Robust model predictive control for a small reverse osmosis desalination unit subject to uncertainty and actuator fault

2020 ◽  
Vol 20 (4) ◽  
pp. 1229-1240
Author(s):  
Seyed Mohamad Kargar ◽  
Reza Mehrad
Keyword(s):  
Model Predictive Control ◽  
Reverse Osmosis ◽  
Predictive Control ◽  
Mimo Systems ◽  
Linear Time ◽  
Human Life ◽  
Noise Model ◽  
Actuator Fault ◽  
Suggested Approach ◽  
Reverse Osmosis Desalination

Abstract Actuator faults are inevitable in small reverse osmosis desalination plants. They may cause energy losses and reduce the quality of the freshwater, which may endanger human life. Model predictive control (MPC) is a model-based approach widely used to control process systems such as reverse osmosis, while considering a set of constraints. In this paper, three methods of predictive model controllers are considered for the control of a multi-input multi-output (MIMO) reverse osmosis desalination system in the presence of noise, model mismatch, and actuator fault. Formulation of enhanced constrained receding horizon predictive control via bounded data uncertainties (CRHPC-BDU) are extended for linear time-invariant MIMO systems. Permeate flow rate and conductivity of the water produced are controlled by a retentate valve and a bypass valve, respectively. The simulation results show the robustness of the suggested approach in the presence of both noise and uncertainties. CRHPC-BDU has a better performance subject to systems with model uncertainty and actuator fault up to a reasonable limit. By increasing the actuator fault up to 34%, the robustness of CRHPC-BDU is further highlighted in permeate conductivity, where the fluctuations of permeate conductivity dampen sooner than in the other two controllers.

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