Control design of spinning rockets based on co-evolutionary optimization

Lee, Ho-Il; Sun, Byung-Chan; Tahk, Min-Jea; Lee, Hungu

doi:10.1016/s0967-0661(00)00102-7

Cited by 16 publications

(12 citation statements)

References 9 publications

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“…Furthermore, for the terminal flight, the line of sight vector and the gravity vector are to be employed. For the intermediate phases, f l, the unitary vectors are defined by expressions (11) and (12).…”

Section: Attitude Determination Algorithmmentioning

confidence: 99%

“…In [10] a target-follower engagement is considered, in which the target is followed while it tries to prevent interception. An attitude control-framework device for a spinning sounding rocket, which depends on a proportional, integral, and derivative (PID) controller, is created in [11]. Proportional-derivative GNC laws for the terminal phases of flight are proposed in [12,13].…”

Section: Introductionmentioning

confidence: 99%

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Applying Neural Networks in Aerial Vehicle Guidance to Simplify Navigation Systems

2020

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The Guidance, Navigation and Control (GNC) of air and space vehicles has been one of the spearheads of research in the aerospace field in recent times. Using Global Navigation Satellite Systems (GNSS) and inertial navigation systems, accuracy may be detached from range. However, these sensor-based GNC systems may cause significant errors in determining attitude and position. These effects can be ameliorated using additional sensors, independent of cumulative errors. The quadrant photodetector semiactive laser is a good candidate for such a purpose. However, GNC systems’ development and construction costs are high. Reducing costs, while maintaining safety and accuracy standards, is key for development in aerospace engineering. Advanced algorithms for getting such standards while eliminating sensors are cornerstone. The development and application of machine learning techniques to GNC poses an innovative path for reducing complexity and costs. Here, a new nonlinear hybridization algorithm, which is based on neural networks, to estimate the gravity vector is presented. Using a neural network means that once it is trained, the physical-mathematical foundations of flight are not relevant; it is the network that returns dynamics to be fed to the GNC algorithm. The gravity vector, which can be accurately predicted, is used to determine vehicle attitude without calling for gyroscopes. Nonlinear simulations based on real flight dynamics are used to train the neural networks. Then, the approach is tested and simulated together with a GNC system. Monte Carlo analysis is conducted to determine performance when uncertainty arises. Simulation results prove that the performance of the presented approach is robust and precise in a six-degree-of-freedom simulation environment.

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Section: Attitude Determination Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Applying Neural Networks in Aerial Vehicle Guidance to Simplify Navigation Systems

2020

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“…Lee et al designed an autopilot for two-stage rockets by combining the gain scheduling method regarding time as the scheduling variable and the co-evolutionary augmented Lagrangian method for optimizing the controller parameters. 11 However, the two autopilot design methods for rockets did not take the perturbation of the parameters besides the scheduling variables into consideration.…”

Section: Introductionmentioning

confidence: 99%

Robust gain scheduled longitudinal autopilot design for rockets during the sustaining phase

Guo

Yao

Zhang

2016

Proceedings of the Institution of Mechanical Engineers, Part I:

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This paper explores the design of a pitch axis load factor autopilot for a class of rockets during the sustaining phase. The existence of the thrust enhances the time-variant characteristic of the rockets mathematical model which contributes to the difficulty of the autopilot design. According to the longitudinal dynamic model of the 122 mm rocket and the design goal of cruising at a constant height, a global autopilot is designed via robust gain scheduling technology regarding time as the scheduling parameter. In contrast to the full-order and the reduced-order mixed sensitivity [Formula: see text] linear compensators, it is a fixed-structure controller consisting of one proportional-integral, two proportional controllers and one first-order filter. With respect to the flight condition uncertainty throughout the sustaining phase, the robust stability is analyzed based on the structured singular value. Simulation results show that the autopilot is insensitive to measurement noises and that the flight path of the rocket can be held at a constant height in different launching angles with an additional simple height controller.

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“…This approach effectively decouples the roll dynamics from the pitch and yaw avoiding complex controllers and advanced analysis (e.g. [3]). To provide an intermediate step from simulation to a rocket launch, a vertical wind tunnel was built with a fan to suck air past a rocket airframe with aluminium actuator fins.…”

Section: Introductionmentioning

confidence: 99%

Minimal modeling of rocket roll response and aerodynamic disturbance in wind tunnel

Hann

Rao

Snowdon

et al. 2011

2011 9th IEEE International Conference on Control and Automation (ICCA)

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The roll dynamics of a 5 kg, 1.3 m high sounding rocket are analyzed in a vertical wind tunnel. Significant turbulence in the tunnel makes the system identification of the effective inertia, damping and asymmetry with respect to roll challenging. A novel method is developed which decouples the disturbance from the rocket frame's intrinsic roll dynamics and allows accurate prediction of roll rate and angle. The parameter identification method is integral-based, and treats wind disturbances as equivalent to a movement in the actuator fins. The method is robust, requires minimal computation, and gave realistic disturbance distributions reflecting the randomness of the turbulent wind flow. Two models, one with constant damping and one with fin angle dependent damping were considered. The mean absolute roll rate of the rocket frame observed in experiments was 16.4 degree/s and both models predicted the roll rate with a mean absolute error less than 0.10 degrees/s with a standard deviation less than 0.08 degrees/s. The roll angle (measured by an encoder), was tracked by the model with a mean absolute error less than 0.20 degrees and a standard deviation less than 0.15 degrees. These results prove the concept of this minimal modeling approach which will be extended to varying wind speed, and pitch and yaw dynamics in the future.

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Control design of spinning rockets based on co-evolutionary optimization

Cited by 16 publications

References 9 publications

Applying Neural Networks in Aerial Vehicle Guidance to Simplify Navigation Systems

Applying Neural Networks in Aerial Vehicle Guidance to Simplify Navigation Systems

Robust gain scheduled longitudinal autopilot design for rockets during the sustaining phase

Minimal modeling of rocket roll response and aerodynamic disturbance in wind tunnel

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