Tuning PID Controller Using Multiobjective Ant Colony Optimization

Abstract

This paper treats a tuning of PID controllers method using multiobjective ant colony optimization. The design objective was to apply the ant colony algorithm in the aim of tuning the optimum solution of the PID controllers (K p , K i , and K d ) by minimizing the multiobjective function. The potential of using multiobjective ant algorithms is to identify the Pareto optimal solution. The other methods are applied to make comparisons between a classic approach based on the "Ziegler-Nichols" method and a metaheuristic approach based on the genetic algorithms. Simulation results demonstrate that the new tuning method using multiobjective ant colony optimization has a better control system performance compared with the classic approach and the genetic algorithms.

PID Tuning Using Internal Model Controller with Tustin Model Approach

“…PID kontroller sangat sering digunakan dalam proses kontrol untuk mengatur perilaku domain waktu dari berbagai jenis pekerjaan yang dinamis (Chiha et al, 2012).…”

“…Salah satu metode yang menjadi standar dalam proses penalaan adalah metode Ziegler-Nichols (Chiha et al, 2012), namun metode ini seringkali sulit untuk menemukan parameter-parameter PID yang optimal. Metode penalaan seperti ini sudah jarang digunakan dalam praktek disebabkan melelahkan dan memakan banyak waktu, khususnya untuk proses dengan time constant yang besar.…”

Multi-objective optimal tuning of two degrees of freedom PID controllers using the ENNC method

“…In this sense, the multi-objective optimization of PID controllers remains an open research topic, even though it has been studied for several decades [5]- [7] and with multiple optimization methods, including bio-inspired techniques such as neural networks, fuzzy logic and genetic algorithms to solve the problem of the optimization [8], [9].…”

Predator and Prey Modified Biogeography Based Optimization Approach (PMBBO) in Tuning a PID Controller for Nonlinear Systems

“…The PID control involves three gains to be determined Proportional, Integral and Derivative [8]. Adjusting the PID parameters is considered as an optimization problem which has been solved by evolutionary algorith ms (EA s), including genetic algorith ms [9,10], ant colony optimization [11], particle swarm optimization [12,13] and biogeography based optimization (BBO) [14].…”