Implementation on FPGA of Neuro-Genetic PID Controllers Auto-Tuning  

作　　者：Nícolas Rosa Marcio da Silva Arantes Claudio Fabiano Motta Toledo João Miguel G. Lima Nícolas Rosa;Marcio da Silva Arantes;Claudio Fabiano Motta Toledo;João Miguel G. Lima(Institute of Mathematics and Computer Science, University of São Paulo, São Carlos, Brazil;Department of Electronic Engineering and Informatics, University of Algarve, Campus de Gambelas Faro, Portugal)

机构地区：[1]Institute of Mathematics and Computer Science, University of Sã o Paulo, Sã o Carlos, Brazil [2]Department of Electronic Engineering and Informatics, University of Algarve, Campus de Gambelas Faro, Portugal

出　　处：《Intelligent Information Management》2022年第5期165-193,共29页智能信息管理（英文）

摘　　要：The present paper studies the use of genetic algorithm to optimize the tuning of the Proportional, Integral and Derivative (PID) controller. Two control criteria were considered, the integral of the time multiplied by the absolute error (ITAE), and the integral of the time multiplied by the absolute output (ITAY). The time variant plant tested is a first-order plant with time delay. We aim at a real time implementation inside a digital board, so, the previous continuous approach was discretized and tested;the corresponding control algorithm is presented in this paper. The genetic algorithms and the PID controller are executed using the soft processor NIOS II in the Field Programmable Gate Array (FPGA). The computational results show the robustness and versatility of this technology.The present paper studies the use of genetic algorithm to optimize the tuning of the Proportional, Integral and Derivative (PID) controller. Two control criteria were considered, the integral of the time multiplied by the absolute error (ITAE), and the integral of the time multiplied by the absolute output (ITAY). The time variant plant tested is a first-order plant with time delay. We aim at a real time implementation inside a digital board, so, the previous continuous approach was discretized and tested;the corresponding control algorithm is presented in this paper. The genetic algorithms and the PID controller are executed using the soft processor NIOS II in the Field Programmable Gate Array (FPGA). The computational results show the robustness and versatility of this technology.

关 键 词：Genetic Algorithms PID Controller FPGA Board Neural Networks 

分 类 号：O17[理学—数学]