An adaptive fuzzy model based process state identification for prediction and control

2005 ◽  
Author(s):  
Meng Tang ◽  
W.H. Koch
Keyword(s):  
Fuzzy Model ◽  
Adaptive Fuzzy ◽  
Model Based ◽  
State Identification ◽  
Prediction And Control ◽  
And Control

scholarly journals A model based study on the dynamics of COVID-19: Prediction and control

2020 ◽  
Vol 136 ◽  
pp. 109889 ◽  
Author(s):  
Manotosh Mandal ◽  
Soovoojeet Jana ◽  
Swapan Kumar Nandi ◽  
Anupam Khatua ◽  
Sayani Adak ◽  
...  
Keyword(s):  
Model Based ◽  
Prediction And Control ◽  
And Control

Recursive Characteristics of a Running-in Attractor in a Ring-on-Disk Tribosystem

2018 ◽  
Vol 141 (1) ◽  
Author(s):  
Cong Ding ◽  
Hua Zhu ◽  
Yu Jiang ◽  
Guodong Sun ◽  
Chunling Wei
Keyword(s):  
Dimensional Space ◽  
Recurrence Plot ◽  
Dynamic Features ◽  
Wear Process ◽  
Friction Noise ◽  
State Identification ◽  
Prediction And Control ◽  
Homogeneous Pattern ◽  
Process Prediction ◽  
And Control

To explore the recursive characteristics of a running-in attractor, recurrence plot (RP) and recursive parameters are used to investigate the dynamic features of the structure. The running-in attractor is constructed based on friction noise signals generated from the ring-on-disk wear experiments. The RPs of the running-in attractor are then reproduced in a two-dimensional space. Recursive parameters, recurrence rate (RR), entropy (ENTR), and trend of recurrence (RT) are calculated. Results show that the RP evolves from a disrupted pattern to a homogeneous pattern and then returns to a disrupted pattern in the entire wear process, corresponding to the “formation–stabilization–disappearance” stage of the running-in attractor. The RR and ENTR of the running-in attractor sharply increase at first, remain steady, and then sharply decrease. Moreover, the inclination of RT in the normal wear process is smaller than those in the other two processes. This observation reveals that the running-in attractor exhibits high stability and complexity. This finding may contribute to the running-in state identification, process prediction, and control.

Design of an adaptive fuzzy model based controller for chaotic dynamics in Lorenz systems with uncertainty

2002 ◽  
Vol 147 (1-4) ◽  
pp. 245-266 ◽  
Author(s):  
Chang-Woo Park ◽  
Chang-Hoon Lee ◽  
Mignon Park
Keyword(s):  
Chaotic Dynamics ◽  
Fuzzy Model ◽  
Adaptive Fuzzy ◽  
Model Based

scholarly journals The algorithmic anatomy of model-based evaluation

2014 ◽  
Vol 369 (1655) ◽  
pp. 20130478 ◽  
Author(s):  
Nathaniel D. Daw ◽  
Peter Dayan
Keyword(s):  
Twentieth Century ◽  
Reinforcement Learning ◽  
Evaluation Methods ◽  
Neural Data ◽  
Model Based ◽  
Model Free ◽  
Prediction And Control ◽  
And Control

Despite many debates in the first half of the twentieth century, it is now largely a truism that humans and other animals build models of their environments and use them for prediction and control. However, model-based (MB) reasoning presents severe computational challenges. Alternative, computationally simpler, model-free (MF) schemes have been suggested in the reinforcement learning literature, and have afforded influential accounts of behavioural and neural data. Here, we study the realization of MB calculations, and the ways that this might be woven together with MF values and evaluation methods. There are as yet mostly only hints in the literature as to the resulting tapestry, so we offer more preview than review.

scholarly journals Advanced Construction of the Dynamic Matrix in Numerically Efficient Fuzzy MPC Algorithms

Algorithms ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 25
Author(s):  
Piotr M. Marusak
Keyword(s):  
Process Model ◽  
Linear Models ◽  
Control Plant ◽  
Fuzzy Model ◽  
Nonlinear Process ◽  
Linear Constraints ◽  
Good Prediction ◽  
Dynamic Matrix ◽  
Prediction And Control ◽  
And Control

A method for the advanced construction of the dynamic matrix for Model Predictive Control (MPC) algorithms with linearization is proposed in the paper. It extends numerically efficient fuzzy algorithms utilizing skillful linearization. The algorithms combine the control performance offered by the MPC algorithms with nonlinear optimization (NMPC algorithms) with the numerical efficiency of the MPC algorithms based on linear models in which the optimization problem is a standard, easy-to-solve, quadratic programming problem with linear constraints. In the researched algorithms, the free response obtained using a nonlinear process model and the future trajectory of the control signals is used to construct an advanced dynamic matrix utilizing the easy-to-obtain fuzzy model. This leads to obtaining very good prediction and control quality very close to those offered by NMPC algorithms. The proposed approach is tested in the control system of a nonlinear chemical control plant—a CSTR reactor with the van de Vusse reaction.

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