GA-PID controller for position control of inverted pendulum

Abstract

Stability is very necessary in control system and it becomes more difficult to achieve for a nonlinear system which inverted pendulum is an example. Most of the controllers available suffer from problems such as difficult in tuning process, sluggishness in response time, quick and global convergence etc. This paper considered Proportional-Integra-Derivative optimized with Genetic Algorithm (GA-PID) Controller on Inverted pendulum for the control of the angle position. Conventional PID controller was used to validate the proposed controller. A MATLAB script for genetic algorithm was written with the aim of obtaining optimum PID parameters that would keep the pendulum angle at equilibrium (i.e. returns the pendulum to a desire point as quick as possible) by minimizing an objective function (Integral time absolute error ITAE). On the other hand, a convention PID controller was designed using MATLAB/Simulink environment; the PID's gains were manually tuned until an optimum response is achieved. The results obtained in both schemes shows that GA-PID showed superiority in all the performance indices used in evaluating the two controller schemes and therefore can serves as a valuable controller for the system.

Genetic algorithm based optimal back stepping controller design for stabilizing inverted pendulum

“…In [1] double PID and LQR controllers are designed for inverted pendulum where author used limit cycle criteria based on friction to evaluate control strategies. In [2] an optimal PID controller is designed based on ITAE as performance criteria and genetic algorithm is used to optimize parameters of PID controller. In [3] an optimal LQR controller is designed for inverted pendulum while the weight matrices were selected using PSO optimization method.…”

A Pid Based Anfis & Fuzzy Control Of Inverted Pendulum On Inclined Plane (Ipip)

“…The control signal in PID is sum of P-term (which is proportional to the error), the I-term (which is proportional to the integral of the error) and the D-term (which is proportional to derivative of the error). The general representation of PID controller is given in equation 26 [14,31].…”

“…Past literature showed that researchers have shown keen interest in the control of IP system using PID control. Yusuf and Magaji [14] considered PID controller optimized with GA for control of inverted pendulum angle. The PID gains were tuned manually for obtaining an optimum response.…”

Particle Swarm Optimization Based LQR Control of an Inverted Pendulum

“…For this reason, many optimization methods such as genetic algorithm, the bees algorithm, PSO, etc. are used to determine optimum LQR parameters [13][14][15][16][17][18].…”

“…Fuzzy logic and artificial neural network controllers have been used to obtain more stable control in systems where disturbing input is present [9][10][11][12]. In order to achieve better results from classical PID and LQR type controllers, many studies that used some optimization methods such as bees algorithm [13,14], genetic algorithm [15][16][17] and particle swarm optimization [17,18] have been carried out so that the control parameters can be adjusted appropriately. In this study, optimization of the Linear Quadratic Regulator (LQR) parameters by using Particle Swarm Optimization (PSO) method for balance and position control of the inverse pendulum system is discussed.…”