Combining fuzzy, PID and regulation control for an autonomous mini-helicopter

Sánchez, Edgar N.; Becerra, Héctor M.; Vélez, Carlos M.

doi:10.1016/j.ins.2006.10.001

Cited by 92 publications

(58 citation statements)

References 13 publications

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“…8,9,11 The proportional-integral-derivative (PID) controllers 3,8,9,[12][13][14][15] are of low cost, uncomplicated structure, and easy design, which are applied to many hi-tech fields, from industrial machine to biped robot, and especially in UAV systems. However, the controller methods such as PID, backstepping, and fuzzy PID approaches for the UAVs 10,11,16 have problems in practical applications for better performance, robustness, and less oscillation.…”

Section: Introductionmentioning

confidence: 99%

Particle swarm optimization algorithm reinforced fuzzy proportional–integral–derivative for a quadrotor attitude control

Chiou

Tran

Shieh

et al. 2016

Advances in Mechanical Engineering

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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

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Section: Introductionmentioning

confidence: 99%

Particle swarm optimization algorithm reinforced fuzzy proportional–integral–derivative for a quadrotor attitude control

Chiou

Tran

Shieh

et al. 2016

Advances in Mechanical Engineering

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“…Therefore no guarantee that the fuzzy system obtained will have sufficiently good performance. In many cases, the system's performance, however, can be improved by further tuning the membership functions and selecting suitable fuzzification and defuzzification methods [10,[12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Design Hybrid Evolutionary PSO Aiding Miniature Aerial Vehicle Controllers

Chiou

Tran

Peng

2015

Mathematical Problems in Engineering

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Based on some sort of simplified fuzzy reasoning methods and PID parameters, many fuzzy-PID controller schemes are applied to control the complicated systems, recently. Mathematical optimization calculations are making in encouraging EP with PSO yielding a higher-quality solution. In this paper, a novel HEPSO algorithm as an improved variant of stochastic optimization strategy PSO, which assigns optimization to fuzzy-PID control gains, is established. The benefit integration design of the HEPSO algorithm structure is generating and updating the new parameters for the fuzzy-PID control schemes in the short setting and operation time. The proposed controllers have demonstrated performance to MAV models.

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“…In hovering conditions, the system is assumed linear (or linearized), thus multivariable linear control techniques such as Linear Quadratic Regulator (LQR) and H ∞ can be applied. Edgar, Hector and Carlos (2007) propose a flight control structure by combining PID, fuzzy and regulation control, using a nonlinear MIMO model for an X-Cell mini-helicopter platform.…”

Section: Introductionmentioning

confidence: 99%