This paper has implemented nonlinear control strategy for the single tilt tri-rotor aerial robot. Based on Newton-Euler’s laws, the linear and nonlinear mathematical models of tri-rotor UAVs are obtained. A numerical analysis using Newton-Raphson method is chosen for finding hovering equilibrium point. Back-stepping nonlinear controller design is based on constructing Lyapunov candidate function for closed-loop system. By imitating the linguistic logic of human thought, fuzzy logic controllers (FLCs) are designed based on control rules and membership functions, which are much less rigid than the calculations computers generally perform. Effectiveness of the controllers design scheme is shown through nonlinear simulation model on each channel.

Due to the rapid development of science and technology in recent times, many effective controllers are designed and applied successfully to complicated systems. The significant task of controller design is to determine optimized control gains in a short period of time. With this purpose in mind, a combination of the particle swarm optimization (PSO)-based algorithm and the evolutionary programming (EP) algorithm is introduced in this article. The benefit of this integration algorithm is the creation of new best-parameters for control design schemes. The proposed controller designs are then demonstrated to have the best performance for nonlinear micro air vehicle models.

In this study, the Genetic Algorithm operability is assigned to optimize the proportional-integral-derivative controller parameters for both simulation and real-time operation of quadcopter flight motion. The optimized proportionalintegral-derivative gains, using Genetic Algorithm to minimum the fitness function via the integral of time multiplied by absolute error criterion, are then integrated to control the quadcopter flight motion. In addition, the proposed controller design is successfully implemented to the experimental real-time flight motion. The performance results are proven that the highly effective stability operation and the reliable of waypoint tracking.

In this article, the particle swarm optimization algorithm is presented using an improved stochastic variant strategy to optimize the gains of the fuzzy proportional-integral-derivative controller. The particle swarm optimization algorithm aims to renew the elite parameters for the schemes of fuzzy proportional-integral-derivative controls. The integral of time multiplied by absolute error criterion is used to estimate the system performance due to saving the setting and operation time. Facing nonlinear quadrotor attitude control models, the results demonstrate the proposed scheme with better performance. KeywordsFuzzy proportional-integral-derivative controller, particle swarm optimization, improved stochastic variant strategy, integral of time multiplied by absolute error, quadrotor Date