A model-free control structure for the on-line tuning of the semi-active suspension of a passenger car

2007 ◽  
Vol 21 (3) ◽  
pp. 1422-1436 ◽  
Author(s):  
J. Swevers ◽  
C. Lauwerys ◽  
B. Vandersmissen ◽  
M. Maes ◽  
K. Reybrouck ◽  
...  
Keyword(s):  
Control Structure ◽  
Active Suspension ◽  
Passenger Car ◽  
Model Free ◽  
On Line ◽  
Model Free Control

Intelligent PD controller design for active suspension system based on robust model-free control strategy

2019 ◽  
Vol 233 (14) ◽  
pp. 4863-4880 ◽  
Author(s):  
Maroua Haddar ◽  
Riadh Chaari ◽  
S Caglar Baslamisli ◽  
Fakher Chaari ◽  
Mohamed Haddar
Keyword(s):  
Controller Design ◽  
Design Method ◽  
Active Suspension ◽  
Vehicle Speed ◽  
Pd Controller ◽  
Suspension Systems ◽  
Model Free ◽  
Robust Model ◽  
Road Disturbance ◽  
Model Free Control

A novel active suspension control design method is proposed for attenuating vibrations caused by road disturbance inputs in vehicle suspension systems. For the control algorithm, we propose an intelligent PD controller structure that effectively rejects online estimated disturbances. The main theoretical techniques used in this paper consist of an ultra-local model which replaces the mathematical model of quarter car system and a new algebraic estimator of unknown information. The measurement of only input and output variables of the plant is required for achieving the reference tracking task and the cancellation of unmodeled exogenous and endogenous perturbations such as roughness road variation, unpredictable variation of vehicle speed and load variation. The performance and robustness of the proposed active suspension algorithm are compared with ADRC control and LQR control. Numerical results are provided for showing the improvement of passenger comfort criteria with model-free control.

Cost-Benefit Arbitration Between Multiple Reinforcement-Learning Systems

2017 ◽  
Vol 28 (9) ◽  
pp. 1321-1333 ◽  
Author(s):  
Wouter Kool ◽  
Samuel J. Gershman ◽  
Fiery A. Cushman
Keyword(s):  
Reinforcement Learning ◽  
Cost Benefit Analysis ◽  
Cost Benefit ◽  
Learning Theories ◽  
Free System ◽  
Model Based ◽  
Model Free ◽  
Accurate Performance ◽  
On Line ◽  
Model Free Control

Human behavior is sometimes determined by habit and other times by goal-directed planning. Modern reinforcement-learning theories formalize this distinction as a competition between a computationally cheap but inaccurate model-free system that gives rise to habits and a computationally expensive but accurate model-based system that implements planning. It is unclear, however, how people choose to allocate control between these systems. Here, we propose that arbitration occurs by comparing each system’s task-specific costs and benefits. To investigate this proposal, we conducted two experiments showing that people increase model-based control when it achieves greater accuracy than model-free control, and especially when the rewards of accurate performance are amplified. In contrast, they are insensitive to reward amplification when model-based and model-free control yield equivalent accuracy. This suggests that humans adaptively balance habitual and planned action through on-line cost-benefit analysis.

Multi-Motor Synchronization of Multi-Wire Saw Based on Model-Free Adaptive Control

2009 ◽  
Vol 69-70 ◽  
pp. 660-664 ◽  
Author(s):  
Jin Jiang ◽  
Y.X. Dai ◽  
Y.B. Zhang ◽  
R. Tang ◽  
Wan Li Xiong
Keyword(s):  
Adaptive Control ◽  
Control Structure ◽  
Estimation Algorithm ◽  
System Model ◽  
Overall Design ◽  
Model Free ◽  
Wire Saw ◽  
Parameter Estimation Algorithm ◽  
On Line ◽  
Similar Dynamic

Aimed at the problem of multi-motor synchronization in multi-wire saw, Strategy based on model-free adaptive control (MFAC), which has some characteristics such as without accurate system model, without system identification and without complicated manual tuning, is produced. MFAC is based directly on pseudo-partial-derivatives (PPD) derived on-line from the input and output data of the system using a novel parameter estimation algorithm. It is suitable for multi-wire saw in such a complex system which is nonlinearity, strong interference and strong coupling. Overall design of multi-wire saw is analyzed. Control structure of multi-motor is given. Motion model of multi-motor synchronization is established. To speed of main motor for reference, supply spool motor and collect spool motor, which have similar dynamic characteristics with main motor, are adjusted adaptively to follow operation of main motor, and synchronization motion is ensured. The prototype experiments show that the method used is right and feasible.

scholarly journals Author Correction: Reliance on model-based and model-free control in obesity

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lieneke K. Janssen ◽  
Florian P. Mahner ◽  
Florian Schlagenhauf ◽  
Lorenz Deserno ◽  
Annette Horstmann
Keyword(s):  
Model Based ◽  
Model Free ◽  
Model Free Control

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Model-Based and Model-Free Control of DC-DC Converters with High-Order Dynamics and Limited Measurements

2020 ◽  
pp. 1-1
Author(s):  
Javier Loranca ◽  
Jonathan Carlos Mayo Maldonado ◽  
Gerardo Escobar ◽  
Carlos Villarreal-Hernandez ◽  
Thabiso Maupong ◽  
...  
Keyword(s):  
High Order ◽  
Model Based ◽  
Model Free ◽  
Model Free Control
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