Bayes parameter identification for optimal control of robots

2003 ◽  
Author(s):  
P. Kulczycki ◽  
A. Mazgaj
Keyword(s):  
Optimal Control ◽  
Parameter Identification

Parameter identification and optimal control of ground temperature

1995 ◽  
Vol 20 (8-9) ◽  
pp. 789-801 ◽  
Author(s):  
Mutsuto Kawahara ◽  
Ken-Ichi Sasaki ◽  
Yasuhiko Sano
Keyword(s):  
Optimal Control ◽  
Parameter Identification ◽  
Ground Temperature

scholarly journals Parameter identification for nonlocal phase field models for tumor growth via optimal control and asymptotic analysis

2021 ◽  
pp. 1-52
Author(s):  
Elisabetta Rocca ◽  
Luca Scarpa ◽  
Andrea Signori
Keyword(s):  
Optimal Control ◽  
Tumor Growth ◽  
Parameter Identification ◽  
Optimality Conditions ◽  
A Priori ◽  
Three Dimensional ◽  
Necessary Optimality Conditions ◽  
Reaction Diffusion ◽  
Original System ◽  
Identification Problem

In this paper, we introduce the problem of parameter identification for a coupled nonlocal Cahn–Hilliard-reaction-diffusion PDE system stemming from a recently introduced tumor growth model. The inverse problem of identifying relevant parameters is studied here by relying on techniques from optimal control theory of PDE systems. The parameters to be identified play the role of controls, and a suitable cost functional of tracking-type is introduced to account for the discrepancy between some a priori knowledge of the parameters and the controls themselves. The analysis is carried out for several classes of models, each one depending on a specific relaxation (of parabolic or viscous type) performed on the original system. First-order necessary optimality conditions are obtained on the fully relaxed system, in both the two- and three-dimensional cases. Then, the optimal control problem on the non-relaxed models is tackled by means of asymptotic arguments, by showing convergence of the respective adjoint systems and the minimization problems as each one of the relaxing coefficients vanishes. This allows obtaining the desired necessary optimality conditions, hence to solve the parameter identification problem, for the original PDE system in case of physically relevant double-well potentials.

scholarly journals Parameter optimal identification and dynamic behavior analysis of nonlinear model for the solution purification process of zinc hydrometallurgy

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Qianqian Wang ◽  
Minan Tang ◽  
Aimin An ◽  
Jiawei Lu ◽  
Yingying Zhao
Keyword(s):  
Optimal Control ◽  
Parameter Identification ◽  
Objective Function ◽  
Ion Concentration ◽  
Steepest Descent Method ◽  
Practical Implementation ◽  
Model Parameters ◽  
Purification Process ◽  
Lead Zinc ◽  
Zinc Hydrometallurgy

<p style='text-indent:20px;'>Impurity removal is a momentous part of zinc hydrometallurgy process, and the quality of products and the stability of the whole process are affected directly by its control effect. The application of dynamic model is of great significance to the prediction of key indexes and the optimization of process control. In this paper, considering the complex coupling relationship of stage II purification process, a hybrid modeling method of mechanism modeling and parameter identification modeling was proposed on the basis of not changing the actual production process of lead-zinc smeltery. Firstly, the overall nonlinear dynamic mechanism model was established, and then the deviation between the theoretical value and the actual detected outlet ion concentration was taken as the objective function to establish the parameter identification optimization model. Since the built model is nonlinear, it may pose implementation problems. On the premise of deriving the gradient vector and Hessian matrix of the objective function with respect to the parameter vector, an optimization algorithm based on the steepest descent method and Newton method is proposed. Finally, using the historical production data of a lead-zinc smeltery in China, the model parameters were accurately inversed. An intensive simulation validation and analysis of the dynamic characteristics about the whole model shows the accuracy and the potential of the model, also in the perspective of practical implementation, which provides the basis for the optimal control of system output and the guidance for the optimal control of zinc powder addition.</p>

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