Study on flapping mechanism of birds : Optimal learning control for the maximum thrust generation in hovering

Service load replication performed on multiaxial hydraulic test rigs has been widely applied in automotive engineering for durability testing in laboratory. The frequency-domain off-line iterative learning control is used to generate the desired drive file, i.e. the input signals which drive the actuators of the test rig. During the iterations an experimentally identified linear frequency-domain system model is used. As the durability test rig and the specimen under test have a strong nonlinear behavior, a large number of iterations are needed to generate the drive file. This process will cause premature deterioration to the specimen unavoidably. In order to accelerate drive file construction, a method embedding complex conjugate gradient algorithm into the conventional off-line iterative learning control is proposed to reproduce the loading conditions. The basic principle and monotone convergence of the method is presented. The drive signal is updated according to the complex conjugate gradient and the optimal learning gain. An optimal learning gain can be obtained by an estimate loop. Finally, simulations are carried out based on the identified parameter model of a real spindle-coupled multiaxial test rig. With real-life spindle forces from the wheel force transducer in the proving ground test to be replicated, the simulation results indicate that the proposed conventional off-line iterative learning control with complex conjugate gradient algorithm allows generation of drive file more rapidly and precisely compared with the state-of-the-art off-line iterative learning control. Few have been done about the proposed method before. The new method is not limited to the durability testing and can be extended to other systems where repetitive tracking task is required.

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3A1-X03 Relationship between Maximum Thrust and Wing Shape of Flapping Mechanism Driven by Magnetic Field(Bio-Mimetics and Bio-Mechatronics)

The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) ◽

10.1299/jsmermd.2014._3a1-x03_1 ◽

2014 ◽

Vol 2014 (0) ◽

pp. _3A1-X03_1-_3A1-X03_3

Author(s):

Yuki HIGASHI ◽

Takahiro MIZUNO ◽

Takashi HONDA

Keyword(s):

Magnetic Field ◽

Wing Shape ◽

Flapping Mechanism ◽

Maximum Thrust

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Micro Pulsatile Jets for Thrust Optimization

Fluids Engineering ◽

10.1115/imece2004-62042 ◽

2004 ◽

Cited By ~ 3

Author(s):

Tiffany J. Finley ◽

Kamran Mohseni

Keyword(s):

Actuation Voltage ◽

Orifice Diameter ◽

Vortex Rings ◽

Wave Shape ◽

Electromagnetic Actuation ◽

Thrust Generation ◽

Circular Orifice ◽

Formation Number ◽

Cylindrical Cavities ◽

Maximum Thrust

Thrust optimization of micro-synthetic pulsatile jets is studied. Cylindrical cavities with a small circular orifice at one end, and a vibrating diaphragm at the other are used for thrust generation. The governing parameters are identified and the tradeoffs between electrostatic, piezoelectric, and electromagnetic actuation methods are investigated. Optimization of the micro jets requires a solution that gives maximum diaphragm displacement while minimizing voltage. The size of the orifice diameter is chosen to maintain a formation number of 4, at which the length of an expelled slug of fluid from the exit orifice is four times the diameter of orifice. This relationship maximizes the circulation and impulse in the leading vortex rings generated by the actuator. To examine the effects of cavity dimensions, a number of actuators are constructed out of aluminum with various cavity diameter, cavity height, and orifice diameters. Piezoelectric disks bonded to brass shims are used for actuation. The jets are tested in air at various actuation voltage and wave-shape functions. Maximum thrust generation is achieved at the resonant frequency of the cavity. Hot wire anemometry is used to further characterize the jet flow field. An investigation into electrostatic, piezoelectric, and electromagnetic diaphragm actuation methods revealed that electromagnetic actuation provides the maximum diaphragm displacement using a constant voltage.

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Multivariable Optimal Learning Control of Wafer Temperatures in a Commercial RTP Equipment

The Canadian Journal of Chemical Engineering ◽

10.1002/cjce.5450830227 ◽

2008 ◽

Vol 83 (2) ◽

pp. 371-376 ◽

Cited By ~ 5

Author(s):

Moon K. Cho ◽

Sang R. Joo ◽

Seung H. Won ◽

Kwang S. Lee

Keyword(s):

Learning Control ◽

Optimal Learning

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Optimal Learning Controller Design Using Particle Swarm Optimization: Applied to CSI System

Iraqi Journal for Electrical And Electronic Engineering ◽

10.37917/ijeee.16.1.13 ◽

2020 ◽

Vol 16 (1) ◽

pp. 104-112

Author(s):

Khulood Omran ◽

Abdul-Basset Al-Hussein ◽

Basil Jassim

Keyword(s):

Particle Swarm Optimization ◽

Particle Swarm ◽

Learning Control ◽

Iterative Learning ◽

Constant Current ◽

Switching Frequency ◽

Optimization Approach ◽

Output Current ◽

Optimal Learning ◽

Swarm Optimization

In this article, a PD-type iterative learning control algorithm (ILC) is proposed to a nonlinear time-varying system for cases of measurement disturbances and the initial state errors. The proposed control approach uses a simple structure and has an easy implementation. The iterative learning controller was utilized to control a constant current source inverter (CSI) with pulse width modulation (PWM); subsequently the output current trajectory converged the sinusoidal reference signal and provided constant switching frequency. The learning controller's parameters were tuned using particle swarm optimization approach to get best optimal control for the system output. The tracking error limit is achieved using the convergence exploration. The proposed learning control scheme was robust against the error in initial conditions and disturbances which outcome from the system modeling inaccuracies and uncertainties. It could correct the distortion of the inverter output current waveform with less computation and less complexity. The proposed algorithm was proved mathematically and through computer simulation. The proposed optimal learning method demonstrated good performances.

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