An Novel Optimal PID Tuning and On-Line Tuning Based on Artificial Bee Colony Algorithm

Gao, Fei; Qi, Yibo; Yin, Qiang; Xiao, Jiaqing

doi:10.1109/cise.2010.5677047

Cited by 13 publications

(21 citation statements)

References 5 publications

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“…16 The reason for choosing this algorithm is that ABC is considered able to find PID gains that are close to the optimal solution Gao. 20 The optimization was performed for a fitness function consisting of the weighted sum of the accumulated attitude quaternion error and the accumulated torque:…”

Section: A Optimization Of Pidmentioning

confidence: 99%

Quaternion Error Based Optimal Attitude Control Applied to Pinpoint Landing

Ghiglino

Lappas

2015

AIAA Guidance, Navigation, and Control Conference

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The problem of Unmanned Aerial Vehicles (UAV) trajectory tracking has been studied widely and in depth, including the application of optimal control techniques to this problem, which has also been covered frequently in literature, although mostly to simplified models of the UAV and therefore with limited range of operation. The use of more complex mathematical models for UAV control, on the other hand, is less common and practically all the available research makes use of sub-optimal control techniques. Furthermore, the application of optimal control techniques to more accurate UAV models has seldom been attempted before, and even less for quaternion attitude-based models in particular. In this paper we propose the use of two proper optimal control algorithms that rely on attitude quaternion error and its dynamics. These two control algorithms share a feature which is that although the underlying linearized model on which they are based is model-independent, the LQR cost matrices are dependent on both the particularities of the autonomous vehicle and on the UAV state, and more specifically, on the angular and linear velocities. Therefore, the tuning of these controllers becomes the tuning of cost matrices, which is a priori a simpler task than adapting a more complex controller algorithm to pinpoint landing. On the other hand, these control algorithms are based on exact linearization underlying models that, for close tracking conditions, behave effectively like linear time invariant systems. The advantage of this is two-fold. On one hand, the calculation requirements for these controllers are lighter and, more importantly, the adaption of nonlinearities specific to UAV pinpoint landing is relatively simpler. The authors of this research consider these two features to be highly useful in pinpoint landing control. NomenclatureB = Body frame [χ] BI = [φ, θ, ψ] = Euler angles from B to I δ c , δ r , δ p , δ y = Thrust, roll, pitch, yaw commands e = Error (generic) [F A ] B = [F Ax , F Ay , F Az ] = Aerodynamic forces (expressed in B) [F P ] B = [F P x , F P y , F P z ] = Propulsive forces (expressed in B) g = Gravitational acceleration due to the Earth I = Inertial frame I B Bcm B = diag {[I xx , I yy , I zz ]} = Inertial tensor of Bcm with respect to B (expressed in B) m = Mass of UAV ω = Linear control bandwidth I ω B B = [p, q, r] = Angular rate from B to I (expressed in B) [q] BI = [q 0 , q 1 , q 2 , q 3 ] = Quaternion vector from B to I r Bcm/Io I = [x, y, z] = Position of Bcm from Io (expressed in I) v I Bcm B = [u, v, w] = Velocity of Bcm with respect to I (expressed in B) [x c , y c , z c , ψ c ] = Position and yaw commanded setpoints ζ = Damping ratio 1 Control

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Section: A Optimization Of Pidmentioning

confidence: 99%

Quaternion Error Based Optimal Attitude Control Applied to Pinpoint Landing

Ghiglino

Lappas

2015

AIAA Guidance, Navigation, and Control Conference

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“…Recently, many researchers turn to bio-inspired optimization algorithms to solve the control parameter optimization problem [2,3,1,11,7]. Bio-inspired optimization algorithm is a flourishing field today.…”

Section: Introductionmentioning

confidence: 99%

Simplified brain storm optimization approach to control parameter optimization in F/A-18 automatic carrier landing system

Duan

2015

Aerospace Science and Technology

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“…Gao et al, however, focused on both online and offline tuning, offering practical explanations and very concrete strategies to perform online tuning [2]. They present a simple but effective algorithm for tuning and online tuning PID controllers, in which the control gains are resolved by ABC.…”

mentioning

confidence: 99%

“…They present a simple but effective algorithm for tuning and online tuning PID controllers, in which the control gains are resolved by ABC. The fundamental idea presented by Gao et al is to convert the controller output uP; I; D (where P, I, and D are the PID gains) into a series of multimodal nonnegative functions, whose minimums can be optimally determined by ABC [2].…”

mentioning

confidence: 99%

“…The article by Gao et al [2] has been the base work for the research presented here, where the authors have not only implemented it and tested it, but also produced some significant improvements and extensions to make it a more robust and reliable self-tuning technique. Although [2] does not perform any real-time experimental trials, our focus has been exactly that: Improve [2] to use the online ABC tuning algorithm in a real-life experimental setup.…”

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confidence: 99%

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Online Evolutionary Swarm Algorithm for Self-Tuning Unmanned Flight Control Laws

Ghiglino

Forshaw

Lappas

2015

Journal of Guidance, Control, and Dynamics

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Nomenclature a p , b p , c p = fitness function weightings B = body frame B cm = center of mass of the vehicle in B D = D gain for proportional integral derivative e = error, generic Fe = fitness function I = inertial frame I B Bcm B = inertial tensor of B cm with respect to B, kg · m 2 , expressed in B I, k i = I gain for proportional integral derivative P, k p = P gain for proportional integral derivative q e = quaternion error from B to I r = position, generic, m u = input to the plant v = velocity, generic, m∕s v x , v y , v z = velocity of B cm with respect to I, m∕s, expressed in B x, y, z = position of B cm from I o , m, expressed in I x c , y c , z c= position setpoints, m δ c , δ r , δ p , δ y = thrust, roll, pitch, yaw commands ζ = damping ratio ϕ, θ, ψ = Euler angles from B to I, deg ψ c = yaw setpoint, deg ω = linear control bandwidth I ω B B = angular rate from B to I, rad∕s, expressed in B

show abstract

An Novel Optimal PID Tuning and On-Line Tuning Based on Artificial Bee Colony Algorithm

Cited by 13 publications

References 5 publications

Quaternion Error Based Optimal Attitude Control Applied to Pinpoint Landing

Quaternion Error Based Optimal Attitude Control Applied to Pinpoint Landing

Simplified brain storm optimization approach to control parameter optimization in F/A-18 automatic carrier landing system

Online Evolutionary Swarm Algorithm for Self-Tuning Unmanned Flight Control Laws

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