Genetic Algorithm and Particle Swarm Optimization Based Cascade Interval Type 2 Fuzzy PD Controller for Rotary Inverted Pendulum System

Abstract

This paper presents the design of an optimized Interval Type 2 Fuzzy Proportional Derivative Controller (IT2F-PDC) in cascade form for Rotary Inverted Pendulum (RIP) system. The parameters of the IT2F-PDC are optimised by using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The goal is to balance the pendulum in upright unstable equilibrium position. The IT2F-PDC which is the extended version of conventional type 1 fuzzy logic controller, improves the control strategy by using the advantage of its footprint of uncertainty for the fuzzy membership function. The performance characteristics considered for the controller are steady state error, settling time, rise time, maximum overshoot, and control energy. Experimental and simulation results indicated that the effectiveness and robustness of the proposed GA- and PSO-based controllers on the RIP with respect to load disturbances, parameter variation, and noise effects have been improved over state-of-the-art method. However, the comparative results for simulation and experiment based on cascade IT2F-PDC indicate that GA-based IT2F-PDC has lower steady state error while PSO-based IT2F-PDC has lower overshoot, settling time, and control energy, but both have almost the same rise time. The proposed control strategy can be regarded as a promising strategy for controlling different unstable and nonlinear systems.

A survey on the application of fuzzy systems for underactuated systems

“…Nagarajan et al 72–74 applied the cascade PID method to control an omnidirectional balancing mobile robot; the control structure is shown in Figure 6(c). In addition, Hamza et al 75 designed a cascade interval type-II fuzzy PD controller for a rotary inverted pendulum system in which the PID laws are used as the input sets of the interval type-II fuzzy system.…”

Underactuated robotics: A review

“…Omni-directionally balanced mobile robots are also controlled by cascaded PID methods. [180][181][182] Hamza et al 183 presented the design of an optimized interval type-2 fuzzy PD controller in cascade form for rotary IP system. The proposed control strategy could be regarded as a promising strategy for controlling different unstable and nonlinear systems.…”

“…Fuzzy control Model-based fuzzy control [154][155][156][157][158][159][234][235][236] Model-free fuzzy control [160][161][162]164,[166][167][168][169][170][237][238][239] Hybrid fuzzy control [171][172][173][174] The integration of fuzzy control with other traditional control Fuzzy PID composite control [175][176][177][178][179][180][181][182][240][241][242] Sliding mode variable structure control 205, Backstepping control 186,[276][277][278][279] Adaptive fuzzy control 127,189,190,[280][281][282]…”

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.…”