Designing an interval type-2 fuzzy disturbance observer for a class of nonlinear systems based on modified particle swar
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Designing an interval type-2 fuzzy disturbance observer for a class of nonlinear systems based on modified particle swarm optimization Shokoufeh Naderi1 · Behrooz Rezaie1
· Mostafa Faramin1
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract This paper presents a new interval type-2 fuzzy disturbance observer design for a class of nonlinear systems using modified particle swarm optimization. The design procedure has two main parts, including the selection of the initial structure of the type-2 fuzzy disturbance observer, and the optimization of the observer parameters using a modified particle swarm optimization algorithm. The modified particle swarm optimization algorithm has a better performance in terms of the accuracy and convergence rate compared with the standard particle swarm optimization and many other evolutionary algorithms. In this algorithm, the upper and lower bounds of the search space are defined for the parameters of each particle based on their values, and weaker particles are substituted with new particles. To accentuate the outstanding performance of the modified particle swarm optimization for the considered task, its performance is compared with five famous metaheuristic optimization algorithms. In addition, utilizing interval type-2 fuzzy systems in the proposed observer provides more robustness compared with type-1 fuzzy systems. The effectiveness of the proposed fuzzy disturbance observer is shown through computer simulation and experimental results for the ball and beam system, while the system is subjected to sinusoid and square disturbances, and a comparison is drawn to indicate the superiority of the proposed fuzzy disturbance observer over the other observers. Keywords Fuzzy disturbance observer · Interval type-2 fuzzy system · Modified particle swarm optimization · Ball and beam system
1 Introduction Unknown disturbances, unmodeled dynamics, parameter uncertainties, and nonlinearities are unfavorable factors that may exist in almost all practical systems. These effects, along with the existence of some noises in sensor measurement, the fault in actuators, and other structural vibrations, can severely degrade the performance of a
Behrooz Rezaie
[email protected] Shokoufeh Naderi shuku [email protected] Mostafa Faramin [email protected] 1
Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran
control system and may even lead to instability in some cases. Therefore, disturbance attenuation in dynamical systems is one of the challenging subjects in the modern control theory and has been one of the most active research areas in the last few decades [31, 51]. In dealing with systems affected by disturbance inputs, robust methods are usually applied to reduce the effects of disturbance. To reject these influences, usually, it is required to have some information about the disturbance. Therefore, estimating the disturbance behavior and then taking control action has been suggested as an attractive id
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