Iterative Procedure for Tuning Decentralized PID Controllers

Euzébio, Thiago A. M.; Barros, Péricles Rezende

doi:10.1016/j.ifacol.2015.09.128

Cited by 8 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fuzzy control is a mathematical model with simple structure, strong robustness and independent of the precision of the controlled object. Its goal, like other practical theories, is to solve various practical problems (Euzebio et al, 2015). The basic idea of fuzzy control is to apply fuzzy set theory in the control method, transform the natural language of human control strategy into the control algorithm described by the algorithm language acceptable to the computer.…”

Section: Development and Application Of Fuzzy Controlmentioning

confidence: 99%

“…The basic idea of fuzzification is to define a fuzzy map (Elith et al, 2002), as a fuzzy relation from the numeric domain to the linguistic domain, to map the numerical signal to the linguistic domain. Its essence is to obtain the membership function of the fuzzy set in the numerical field that corresponds to the corresponding concept (Euzebio et al, 2015). This process specifically includes quantification and fuzzy division of the universe of discourse.…”

Section: Fuzzy Controllermentioning

confidence: 99%

See 1 more Smart Citation

Research on the mill feeding system of an elastic variable universe fuzzy control based on particle swarm optimization algorithm

Tian¹,

Huang²,

Li³

et al. 2023

Physicochem. Probl. Miner. Process.

View full text Add to dashboard Cite

The grinding process in the concentrator is a part of the largest energy consumption, but also the most likely to cause a waste of resources, so the optimization of the grinding process is a very important link. The traditional fuzzy controller relies solely on the expert knowledge summary to construct control rules, which can cause significant steady-state errors in the model. In order to solve the above problem, this paper proposes an elastic variable universe fuzzy control based on Particle Swarm Optimization (PSO) algorithm. The elastic universe fuzzy control model does not need precise fuzzy rules, but only needs to input the general trend of the rules, and the division of the universe is performed by the contraction-expansion factor. The control performance is directly related to the contraction-expansion factor, so this article also proposes using particle swarm optimization to optimize the scaling factor to achieve the optimal value. Finally, simulation models of traditional fuzzy control and elastic universe fuzzy control of feeding system of mill were built using Python to verify the control effect. Its simulation results show that the time of the reaction of the fuzzy control system in the elastic variable theory universe based on particle swarm optimization was shorter by 34.48% comparing to the traditional one. Elastic variable universe fuzzy control based on particle swarm optimization (PSO) effectively improved the control accuracy of the mill feeding system and improved the response speed of the system to a certain extent.

show abstract

Section: Development and Application Of Fuzzy Controlmentioning

confidence: 99%

Section: Fuzzy Controllermentioning

confidence: 99%

Research on the mill feeding system of an elastic variable universe fuzzy control based on particle swarm optimization algorithm

Tian¹,

Huang²,

Li³

et al. 2023

Physicochem. Probl. Miner. Process.

View full text Add to dashboard Cite

show abstract

“…This is expected because for the Wood-Berry column, the main and interactive effects are in the opposite directions; see equations ( 35) and (36) where main gain and interactive gain have opposite signs. The performance of the double-loop multi-scale control scheme, Case B (DL-MSC-B scheme) is compared against a decentralized PID control scheme, tuned using a two different optimization-based methods: iterative optimization (Euzebio & Barros, 2015), and retuning optimization (Veronesi & Visioli, 2018). These two methods are adopted because they provide the best performance of decentralized PID control for the Wood-Berry column so far.…”

Section: Illustrative Example Example 1 -2x2 Wood and Berry Columnmentioning

confidence: 99%

Double-Loop Multi-Scale Control using Routh-Hurwitz Dimensionless Parameter Tuning for MIMO Processes

Lim,

Nandong

2020

IJIE

View full text Add to dashboard Cite

This paper presents a new approach to controlling MIMO processes by using the double-loop multi-scale control scheme in the decentralized control architecture. The decentralized PID control system has been used in process industry despite its several limitations due to process interactions, time-delays and right half plane poles. To overcome the performance limitation due to process interactions, decoupling controllers are often added to the decentralized PID control system. The proposed strategy based on the double-loop multi-scale control scheme has some advantages over the existing control strategies for MIMO processes. An advantage of the proposed scheme over the decentralized PID control with decoupling system is that, the proposed strategy has a fixed number of dimensionless tuning parameters that are easy to tune. For an n×n MIMO process, the proposed scheme requires the tuning of only 3 to 6 dimensionless parameters instead of the 3n original PID parameters.

show abstract

Implementation of advanced control in the process industry without the use of MPC

Forsman

2016

IFAC-PapersOnLine

View full text Add to dashboard Cite

Iterative Procedure for Tuning Decentralized PID Controllers

Cited by 8 publications

References 10 publications

Research on the mill feeding system of an elastic variable universe fuzzy control based on particle swarm optimization algorithm

Research on the mill feeding system of an elastic variable universe fuzzy control based on particle swarm optimization algorithm

Double-Loop Multi-Scale Control using Routh-Hurwitz Dimensionless Parameter Tuning for MIMO Processes

Implementation of advanced control in the process industry without the use of MPC

Contact Info

Product

Resources

About