An approach to optimize the proportional-integral-derivative controller system

Abstract

This paper aims at using imperialist competitive algorithm based fuzzy logic (FICA), to control an automatic voltage regulator (A VR) in order to increase the stability and obtain more controllability of the system. For the stabilization of the automatic voltage regulator a proportional-integral-derivative controller (PlD) was used. We applied the FICA, which is the combination of the imperialist competitive algorithm (ICA) and fuzzy logic to determine the optimal coefficients of the proportional integral-derivative controller (PID). The new algorithm solves the main problems of the imperialist competitive algorithm (ICA), which are entrapped in local optimum points and low-speed convergence. This way, the control of the effective parameters of the (ICA) likes as the assimilation coefficient and the cost of colonies is performed with high accuracy and speed. We will show that the results obtained with the proposed intelligent algorithm (FICA), have higher convergence rate and more accuracy in comparison with the other algorithms.

Experimental Evaluation of Optimal Tuning for PID Parameters in an AVR System

“…Those algorithms include Monarch Butterfly Optimization Algorithm (MBO) [24], Taguchi method [25], Slap Swarm Algorithm (SSA) [26], Artificial Bee Colony (ABC) [27], Bacterial Foraging Technique (BFT) [28], Memetic Algorithm (MA) [29], Firefly Optimization Technique (FOT) [30], Shuffled Frog Leaping (SFL) [31], Continuous Action Reinforcement Learning Automata (CARLA) [32], Differential Evolution (DE) and Teaching-Learning-Based Optimization (TLBO) algorithms [33,34], Pattern Search Algorithm (PSA) [35], Simulated Annealing (SA) [36], Finite Gradient [37], Global Neighborhood Algorithm (GNA) [38], Imperialist Competitive Algorithm (ICA) [39], Gravitational Search Algorithm [40], Vector-Based Swarm Optimization (VBSO) [41], Continuous Human Learning Optimizer (CHLO) [42], Artificial Electric Field (AEF) [43], Whale Optimization Algorithm (WOA) [44], Cuckoo Search (CS) [45,46], Jaya Optimization Algorithm (JOA) [45], Ant Colony Optimization (ACO) [21], Chaotic Ant Swarm (CAS) algorithm [47], Chaotic optimization algorithm [48] and Grey Wolf Optimizer (GWO) [49].…”

Model Predictive Control With Disturbance Observer for Marine Diesel Engine Speed Control

“…However, it should be emphasized that the performance of fuzzy PID control depends heavily on the fuzzy rules designed manually, and this is a tedious process [12]. PID algorithm based on swarm intelligent algorithm or neural network algorithm to adjust parameters faces the problem of slow convergence speed [13], [14], in many fields can not meet the real-time requirements, thus affecting the control performance. Especially in the field of diesel engine speed control, most of them can not be applied in engineering.…”