An auto-tuning PID control system based on genetic algorithms to provide delay guarantees in Passive Optical Networks

Jiménez, Tamara; Merayo, Noemí; Andrés, Anaïs; Durán, Ramón J.; Aguado, Juan Carlos; Miguel, Ignacio de; Fernández, Patricia; Lorenzo, Rubén M.; Abril, Evaristo J.

doi:10.1016/j.eswa.2015.07.078

Cited by 27 publications

(15 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…One of the fundamental tasks in intelligent control is the controller tuning problem (Jiménez et al, 2015;Mishra et al, 2015;Ponce et al, 2015;Sabzi et al, 2016). Such problem consists in finding suitable values for the tuneable parameters of a given control structure.…”

Section: Introductionmentioning

confidence: 97%

Evolutionary multi-objective optimisation with preferences for multivariable PI controller tuning

Reynoso-Meza

Sanchis

Blasco

et al. 2016

Expert Systems with Applications

View full text Add to dashboard Cite

Highlights• We present an evolutionary multiobjective optimisation approach for PI controller tuning.• This approach incorporates designer´s preferences into the optimisation process.• The methodology is evaluated in a multivariable process.• It is possible to improve pertinency of the approximated Pareto front. AbstractMulti-objective optimisation design procedures have shown to be a valuable tool for control engineers. They enable the designer having a close embedment of the tuning process for a wide variety of applications. In such procedures, evolutionary multi-objective optimisation has been extensively used for PI and PID controller tuning; one reason for this is due to their flexibility to include mechanisms in order to enhance convergence and diversity. Although its usability, when dealing with multi-variable processes, the resulting Pareto front approximation might not be useful, due to the number of design objectives stated. That is, a vast region of the objective space might be impractical or useless a priori, due to the strong degradation in some of the design objectives. In this paper preference handling techniques are incorporated into the optimisation process, seeking to improve the pertinency of the approximated Pareto front for multi-variable PI controller tuning. That is, the inclusion of preferences into the optimisation process, in order to seek actively for a pertinent Pareto front approximation. With such approach, it is possible to tune a multi-variable PI controller, fulfilling several design objectives, using previous knowledge from the designer on the expected trade-off performance. This is validated with a well-known benchmark example in multi-variable control. Control tests show the usefulness of the proposed approach when compared with other tuning techniques.

show abstract

Section: Introductionmentioning

confidence: 97%

Evolutionary multi-objective optimisation with preferences for multivariable PI controller tuning

Reynoso-Meza

Sanchis

Blasco

et al. 2016

Expert Systems with Applications

View full text Add to dashboard Cite

show abstract

“…Recent studies illustrate that evolutionary algorithms are considered as one of the most efficient ways to gain the optimal values of these parameters. For instance, the genetic algorithm has been utilized in order to adjust the parameters of the PID control (Jiménez et al, 2015). Furthermore, a gravitational search algorithm has been used to optimize the design of the Posicast PID control systems (De Moura Oliveira et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Optimal design of an adaptive robust proportional-integral-derivative controller based upon sliding surfaces for under-actuated dynamical systems

2021

View full text Add to dashboard Cite

This paper presents a novel adaptive robust proportional-integralderivative (PID) controller for under-actuated dynamical systems via employing the advantages of the PID control and sliding surfaces. The related control gains as adjustable parameters are computed during the control process using gradient descent techniques and the chain derivative rule. Based on the design of control rules, the genetic algorithm optimization is utilized in order to find the optimal values of the learning rates and initial conditions of the control gains. The suggested strategy is implemented successfully to stabilize the cart-pole and ball-beam systems. Lastly, the simulation results demonstrate the efficacy of the proposed controller to control such under-actuated dynamical systems as well as its superiority in comparison with other recently introduced methods.

show abstract

“…Nowadays, artificial intelligence methods have been widely used to turn or set PID parameters adaptively, which greatly enhance tuning effect and efficiency [2], mainly including Expert system-based PID (ES-PID), Fuzzy inference-based PID (FI-PID) and Artificial neural network-based PID (ANN-PID) etc. ES-PID abstracts heuristic rules from expert's knowledge about controlled object and control experience to depict the nonlinear relationship between the control variables and PID parameters, and then, the designed inference engine can infer the corresponding values of PID parameters from the rules activated by the online values of control variables [9]. However, there are some difficult issues one has to face, such as, how to distinguish good knowledge from bad knowledge because the latter will lead to useless, conflicting, even counter-intuitive rules; how to enhance the online learning and updating abilities of expert system and improve completeness and adaptability of the constructed rule base [13].…”

Section: Introductionmentioning

confidence: 99%

Self-Tuning Method of PID Parameters Based on Belief Rule Base Inference

Wen

et al. 2018

ITC

View full text Add to dashboard Cite

As a generic inference mechanism, the belief rule-based (BRB) system can effectively integrate quantitative information with qualitative knowledge to model causal relationships of complex application systems. Based on the BRB, this paper develops a novel self-tuning strategy of PID parameters such that the output of closed-loop control system generated by PID controller can accurately follow control input. Firstly, the initial belief rule base is abstracted from expert's control experiences to depict the highly nonlinear relationship between the variables of control system and each PID parameter. Secondly, the objective function is established to minimize the error between the given control input and the closed loop output, and then the online optimization method via sequential linear programming is presented to optimize the parameters of BRB system so as to adaptively adjust PID parameters by the optimized BRB system in real time. Typical control simulation experiments of DC motor are implemented to illustrate the advantages of the proposed BRB-PID over widely used neural network-based PID.

show abstract

An auto-tuning PID control system based on genetic algorithms to provide delay guarantees in Passive Optical Networks

Cited by 27 publications

References 33 publications

Evolutionary multi-objective optimisation with preferences for multivariable PI controller tuning

Evolutionary multi-objective optimisation with preferences for multivariable PI controller tuning

Optimal design of an adaptive robust proportional-integral-derivative controller based upon sliding surfaces for under-actuated dynamical systems

Self-Tuning Method of PID Parameters Based on Belief Rule Base Inference

Contact Info

Product

Resources

About