Tuning Evolvable PID Controllers through a Clonal Selection Algorithm

Amaral, Jorge; Amaral, J.F.M.; Tanscheit, Ricardo; Pacheco, Marco Aurélio Cavalcanti; Mesquita, A.

doi:10.1109/eh.2005.47

Cited by 6 publications

(5 citation statements)

References 6 publications

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“…This characteristic has many times lead to poor controlling results, due to the dynamic nature of the processes. In these systems, intelligent PID controllers turn out to be very helpful [3] by adopting intelligent techniques such as neural network [4][5][6][7], fuzzy logic [8][9][10], genetic algorithms [11][12][13] etc. Specifically, in terms of speed control of the DC motors, many researchers have been used intelligent PIDs.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Q-Learning Agent for Online Tuning of PID Controller for DC Motor Speed Control

Kofinas

Dounis

2018

Algorithms

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This paper proposes a hybrid Zeigler-Nichols (Z-N) reinforcement learning approach for online tuning of the parameters of the Proportional Integral Derivative (PID) for controlling the speed of a DC motor. The PID gains are set by the Z-N method, and are then adapted online through the fuzzy Q-Learning agent. The fuzzy Q-Learning agent is used instead of the conventional Q-Learning, in order to deal with the continuous state-action space. The fuzzy Q-Learning agent defines its state according to the value of the error. The output signal of the agent consists of three output variables, in which each one defines the percentage change of each gain. Each gain can be increased or decreased from 0% to 50% of its initial value. Through this method, the gains of the controller are adjusted online via the interaction of the environment. The knowledge of the expert is not a necessity during the setup process. The simulation results highlight the performance of the proposed control strategy. After the exploration phase, the settling time is reduced in the steady states. In the transient states, the response has less amplitude oscillations and reaches the equilibrium point faster than the conventional PID controller.

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Section: Introductionmentioning

confidence: 99%

Fuzzy Q-Learning Agent for Online Tuning of PID Controller for DC Motor Speed Control

Kofinas

Dounis

2018

Algorithms

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“…GA based design of PID controller for cascade control process is presented in Patil and Lakhekar (2017). An investigation on applicability of genetic algorithms for automatic tuning of PID controller parameters is presented in Amaral et al (2018).…”

Section: Genetic Algorithm For Pid Tuningmentioning

confidence: 99%

Metaheuristic algorithms for PID controller parameters tuning: review, approaches and open problems

Bassi

Dada

Abidemi

et al. 2022

Heliyon

164

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“…The adjusting of a PIC consists of choosing gains and thus that performance specifications are satisfied. The parameter effects on the control system as follow: decrease the rise time, increase the overshoot, decrease the error, and make a small change in the turning time, while parameter effects as follow: decrease the rise time, increase the overshoot, eliminate the error, and increase the turning time [26].…”

Section: The Picmentioning

confidence: 99%

Variable speed control for 2Ph-HSM in RGS: a comparative simulation study

Shneen¹,

Sulttan²,

Jaber³

2020

IJECE

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There are many applications of two-phase hybrid stepping motor (2Ph-HSM) system. The robotic grinding system (RGS) one of these applications. In this work, under the title variable speed control. The aim is Simulink the 2Ph-HSM in RGS with a proportional-integral controller (PIC) and optimization unit such as Genetic Algorithm (GA) which tuning PIC as (GA_PIC) to improve the RGS action by improving the parameters of PIC. Also comparing the act of PIC and GA_PIC to see which state is the best. The simulation results of this work show the GA_PIC is the best that comparative with PIC.

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Tuning Evolvable PID Controllers through a Clonal Selection Algorithm

Cited by 6 publications

References 6 publications

Fuzzy Q-Learning Agent for Online Tuning of PID Controller for DC Motor Speed Control

Fuzzy Q-Learning Agent for Online Tuning of PID Controller for DC Motor Speed Control

Metaheuristic algorithms for PID controller parameters tuning: review, approaches and open problems

Variable speed control for 2Ph-HSM in RGS: a comparative simulation study

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