Optimal tuning of PID controller in time delay system: a review on various optimization techniques

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Thomas George ◽  
V. Ganesan
Keyword(s):  
Time Delay ◽  
Pid Control ◽  
Pid Controller ◽  
Optimization Techniques ◽  
Operating Conditions ◽  
Delay Systems ◽  
Pid Controllers ◽  
Time Delay Systems ◽  
Process Industries ◽  
First Order

AbstractThe processes which contain at least one pole at the origin are known as integrating systems. The process output varies continuously with time at certain speed when they are disturbed from the equilibrium operating point by any environment disturbance/change in input conditions and thus they are considered as non-self-regulating. In most occasions this phenomenon is very disadvantageous and dangerous. Therefore it is always a challenging task to efficient control such kind of processes. Depending upon the number of poles present at the origin and also on the location of other poles in transfer function different types of integrating systems exist. Stable first order plus time delay systems with an integrator (FOPTDI), unstable first order plus time delay systems with an integrator (UFOPTDI), pure integrating plus time delay (PIPTD) systems and double integrating plus time delay (DIPTD) systems are the classifications of integrating systems. By using a well-controlled positioning stage the advances in micro and nano metrology are inevitable in order satisfy the need to maintain the product quality of miniaturized components. As proportional-integral-derivative (PID) controllers are very simple to tune, easy to understand and robust in control they are widely implemented in many of the chemical process industries. In industries this PID control is the most common control algorithm used and also this has been universally accepted in industrial control. In a wide range of operating conditions the popularity of PID controllers can be attributed partly to their robust performance and partly to their functional simplicity which allows engineers to operate them in a simple, straight forward manner. One of the accepted control algorithms by the process industries is the PID control. However, in order to accomplish high precision positioning performance and to build a robust controller tuning of the key parameters in a PID controller is most inevitable. Therefore, for PID controllers many tuning methods are proposed. the main factors that lead to lifetime reduction in gain loss of PID parameters are described in This paper and also the main methods used for gain tuning based on optimization approach analysis is reviewed. The advantages and disadvantages of each one are outlined and some future directions for research are analyzed.

Designing Document SQL (DSQL)

2009 ◽  
Vol 20 (4) ◽  
pp. 26-53 ◽  
Author(s):  
Arijit Sengupta ◽  
V. Ramesh
Keyword(s):  
Ad Hoc ◽  
Query Language ◽  
Optimization Techniques ◽  
Order Logic ◽  
Conservative Extension ◽  
Tree Structures ◽  
First Order ◽  
Easy Integration ◽  
Query Semantics ◽  
Theoretical Foundations

This article presents DSQL, a conservative extension of SQL, as an ad-hoc query language for XML. The development of DSQL follows the theoretical foundations of first order logic, and uses common query semantics already accepted for SQL. DSQL represents a core subset of XQuery that lends well to optimization techniques, while at the same time allows easy integration into current databases and applications that useSQL. The intent of DSQL is not to replace XQuery, the current W3C recommended XML query language, but to serve as an ad-hoc querying frontend to XQuery. Further, the authors present proofs for important query language properties such as complexity and closure. An empirical study comparing DSQL and XQuery for the purpose of ad-hoc querying demonstrates that users perform better with DSQL for both flat and tree structures, in terms of both accuracy and efficiency.

On first-order algorithms for l1/nuclear norm minimization

Acta Numerica ◽  
2013 ◽  
Vol 22 ◽  
pp. 509-575 ◽  
Author(s):  
Yurii Nesterov ◽  
Arkadi Nemirovski
Keyword(s):  
Saddle Point ◽  
Gradient Methods ◽  
Saddle Point Problem ◽  
Optimization Techniques ◽  
Nuclear Norm ◽  
Point Problem ◽  
Nuclear Norm Minimization ◽  
Dantzig Selector ◽  
First Order ◽  
Norm Minimization

In the past decade, problems related to l1/nuclear norm minimization have attracted much attention in the signal processing, machine learning and optimization communities. In this paper, devoted to l1/nuclear norm minimization as ‘optimization beasts’, we give a detailed description of two attractive first-order optimization techniques for solving problems of this type. The first one, aimed primarily at lasso-type problems, comprises fast gradient methods applied to composite minimization formulations. The second approach, aimed at Dantzig-selector-type problems, utilizes saddle-point first-order algorithms and reformulation of the problem of interest as a generalized bilinear saddle-point problem. For both approaches, we give complete and detailed complexity analyses and discuss the application domains.

A Comparative Evaluation of Unconstrained Optimization Methods Applied to the Thermal Tomography Problem

1985 ◽  
Vol 107 (3) ◽  
pp. 228-233 ◽  
Author(s):  
S. T. Clegg ◽  
R. B. Roemer
Keyword(s):  
Unconstrained Optimization ◽  
Blood Perfusion ◽  
Optimization Methods ◽  
Sensitivity Study ◽  
Initial Guess ◽  
Optimization Techniques ◽  
Temperature Fields ◽  
Tissue Temperature ◽  
Initial Attempt ◽  
Error Space

In cancer hyperthermia treatments, it is important to be able to predict complete tissue temperature fields from sampled temperatures taken at the limited number of locations allowed by clinical constraints. An initial attempt to do this automatically using unconstrained optimization techniques to minimize the differences between experimental temperatures and temperatures predicted from treatment simulations has been previously reported [1]. This paper reports on a comparative study which applies a range of different optimization techniques (relaxation, steepest descent, conjugate gradient, Gauss, Box-Kanemasu, and Modified Box-Kanemasu) to this problem. The results show that the Gauss method converges more rapidly than the others, and that it converges to the correct solution regardless of the initial guess for the unknown blood perfusion vector. A sensitivity study of the error space is also performed, and the relationships between the error space characteristics and the comparative speeds of the optimization techniques are discussed.

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