Abstract
Currently, fuzzy proportional integral derivative (PID) controller schemes, which include simplified fuzzy reasoning decision methodologies and PID parameters, are broadly and efficaciously practiced in various fields from industrial applications, military service, to rescue operations, civilian information and also horticultural observation and agricultural surveillance. A fusion particle swarm optimization (PSO)–evolutionary programming (EP) algorithm, which is an improved version of the stochastic optimization strategy PSO, was presented for designing and optimizing controller gains in this study. The mathematical calculations of this study include the reproduction of EP with PSO. By minimizing the integral of the absolute error (IAE) criterion that is used for estimating the system response as a fitness function, the obtained integrated design of the fusion PSO–EP algorithm generated and updated the new elite parameters for proposed controller schemes. This progression was used for the complicated non-linear systems of the attitude-control pilot models of a tricopter unmanned aerial vehicle (UAV) to demonstrate an improvement on the performance in terms of rapid response, precision, reliability, and stability.
Highlights
Evolutionary programming (EP) is one of the four primary evolutionary algorithm paradigms and was first proposed by Lawrence J
The value of the tilt angle μ should be negative in the initial condition tocondition maintain to maintain system
Five controller designs were applied to a tricopter pilot system in this research work
Summary
Evolutionary programming (EP) is one of the four primary evolutionary algorithm paradigms and was first proposed by Lawrence J. In contrast to the genetic algorithm (GA), the EP algorithm does not include a crossover operator but only contains a mutation operator and a specific selection strategy; its parameters are allowed to evolve
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