Add to ORKGSelf-optimizing control focuses on minimizing loss for processes in the presence of disturbances by holding selected controlled variables at constant set-points. The loss can further be reduced by controlling measurement combinations to constant values. Two methods for finding appropriate measurement combinations are the Null-space and the Exact local method. Both approaches offer sets with an infinite number of solutions that give the same loss. Since self-optimizing control is mainly concerned with minimizing the steady-state loss, little attention has been put on the dynamic performance when selecting measurement combinations
Abstract The self-triggered control produces non-periodic sampling sequences that vary depending on...
Abstract Rules for control structure design for industrial processes have been extensively proposed ...
Bidirectional branch and bound for controlled variable selection Part II: exact local method for sel...
Self-optimizing control focuses on minimizing loss for processes in the presence of disturbances by ...
Self-optimizing control focuses on minimizing the steady-state loss for processes in the presence of...
In this project we have studied a newly developed way to find self-optimizing variables. The method ...
The following important question is frequenctly overlooked: Which variables should we select to cont...
Increased competition in the process industries requires optimal operation and better utilization of...
The selection of appropriate controlled variables (CVs) is important during the design of control sy...
An ADMM algorithm is proposed for selecting structurally constrained measurement combinations as con...
Optimal operation is important to improve productivity to be more competitive, and therefore, increa...
An increasingly competitive global market, together with stricter environmental and safety regulatio...
In order to operate continuous processes near the economically optimal steady-state operating point,...
After 15 year development, it is still hard to find any real application of the self-optimizing cont...
The self-triggered control includes a sampling strategy that focuses on decreasing the use of comput...
Abstract The self-triggered control produces non-periodic sampling sequences that vary depending on...
Abstract Rules for control structure design for industrial processes have been extensively proposed ...
Bidirectional branch and bound for controlled variable selection Part II: exact local method for sel...
Self-optimizing control focuses on minimizing loss for processes in the presence of disturbances by ...
Self-optimizing control focuses on minimizing the steady-state loss for processes in the presence of...
In this project we have studied a newly developed way to find self-optimizing variables. The method ...
The following important question is frequenctly overlooked: Which variables should we select to cont...
Increased competition in the process industries requires optimal operation and better utilization of...
The selection of appropriate controlled variables (CVs) is important during the design of control sy...
An ADMM algorithm is proposed for selecting structurally constrained measurement combinations as con...
Optimal operation is important to improve productivity to be more competitive, and therefore, increa...
An increasingly competitive global market, together with stricter environmental and safety regulatio...
In order to operate continuous processes near the economically optimal steady-state operating point,...
After 15 year development, it is still hard to find any real application of the self-optimizing cont...
The self-triggered control includes a sampling strategy that focuses on decreasing the use of comput...
Abstract The self-triggered control produces non-periodic sampling sequences that vary depending on...
Abstract Rules for control structure design for industrial processes have been extensively proposed ...
Bidirectional branch and bound for controlled variable selection Part II: exact local method for sel...