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Derivative-Free Observability Analysis for Sensor Placement Optimization of Bioinspired Flexible Flapping Wing
Bingyu Jin1, Hao Xu2, Jicheng Peng2
1College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
Biomimetics (Basel, Switzerland)
|November 22, 2022
Summary
A new method enhances observability analysis for bioinspired flexible flapping wings. This approach offers quantitative insights and optimizes sensor placement for micro-aerial vehicles.
Area of Science:
- Robotics and Bio-inspired Engineering
- Control Systems and Estimation Theory
- Aerospace Engineering
Background:
- Traditional observability analysis methods struggle with quantitative metrics, computational complexity, and stochastic systems.
- Bioinspired flexible flapping wing systems present unique challenges for state estimation due to their complex dynamics.
- Accurate state estimation is crucial for the control and navigation of micro-aerial vehicles.
Purpose of the Study:
- To develop a novel, derivative-free observability analysis method for bioinspired flexible flapping wing systems.
- To address the limitations of traditional methods, including lack of quantitative indices and applicability to stochastic systems.
- To enable precise assessment of state observability and the impact of stochastic noise.
Main Methods:
- Utilized generalized polynomial chaos expansion to formulate a surrogate model.
- Calculated the observability coefficient matrix and stated the observability rank condition.
- Proposed new observability indices to quantify system observability.
Main Results:
- The proposed method provides quantitative observability indices, overcoming traditional limitations.
- It effectively assesses the observability of individual states and the influence of stochastic noise.
- Validation demonstrated equivalence with traditional methods and superior performance on the Lorenz system.
Conclusions:
- The novel method offers a robust and accurate approach to observability analysis for complex systems.
- It facilitates sensor placement optimization, as demonstrated on a bioinspired flexible wing system.
- The findings support the development of more capable and reliable flapping wing micro-aerial vehicles.
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