Development of a robust deep recurrent neural network controller for flight applications

Neural network-based model reference adaptive control (MRAC) is an effective architecture used in the flight control community to combat significant uncertainties where the structure of the uncertainty is unknown. In our previous work, a novel adaptive control architecture called sparse neural network (SNN) was developed in order to improve long-term learning and transient performance of flight vehicles with persistent uncertainties which lie in various regions throughout the operating regime. The SNN is designed to operate with small learning rates in order to avoid high-frequency oscillations and utilizes only a small number of active neurons in the adaptive controller in order to reduce the computational burden on the processor. In this paper, we enhance the SNN architecture by developing an innovative adaptive control term which is used to mitigate a control effectiveness matrix. Furthermore, we design a robust control term and a strict dwell time condition in order to ensure stability while switching between segments. We demonstrate the effectiveness of the SNN approach by controlling a sophisticated hypersonic vehicle with flexible body effects.

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“…The axial and normal body forces (A, N) and pitching moment (M) can be approximated by (see Ref. [35,34]):…”

Section: Hypersonic Flight Vehicle Dynamics With Flexible Body Efmentioning

confidence: 99%

A Sparse Neural Network Approach to Model Reference Adaptive Control with Hypersonic Flight Applications

Nivison

Khargonekar

2018

2018 AIAA Guidance, Navigation, and Control Conference

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“…Unlike the conventional feedforward neural networks, its current processing of the input data relays on the outputs of the previous time steps; meanwhile, its current state is transformed as the input value for the next state [11,12]. Benefitting from its specific structure, it has been applied in many real applications, such as time-series market data processing, text generation and machine translation [13][14][15][16]. Considering these advantages, the RNN model can be as a powerful tool, adopted to our temperature control system to process the time series data for achieving desired control performance.…”

Section: Introductionmentioning

confidence: 99%

Recurrent Neural Network-Based Temperature Control System Weight Pruning Based on Nonlinear Reconstruction Error

Liu

Kawaguchi

et al. 2021

Processes

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Recurrent Neural Networks (RNNs) have been widely applied in various fields. However, in real-world application, because most devices like mobile phones are limited to the storage capacity when processing real-time information, an over-parameterized model always slows down the system speed and is not suitable to be employed. In our proposed temperature control system, the RNN-based control model processes the real-time temperature signals. It is necessary to compress the trained model with acceptable loss of control performance for further implementation in the actual controller when the system resource is limited. Inspired by the layer-wise neuron pruning method, in this paper, we apply the nonlinear reconstruction error (NRE) guided layer-wise weight pruning method on the RNN-based temperature control system. The control system is established based on MATLAB/Simulink. In order to compress the model size to save the memory capacity of temperature controller devices, we first prove the validity of the proposed reference-model (ref-model) guided RNN model for real-time online data processing on an actual temperature object; relative experiments are implemented based on a digital signal processor. On this basis, we then verified the NRE guided layer-wise weight pruning method on the well-trained temperature control model. Compared with the classical pruning method, experiment results indicate that the pruned control model based on NRE guided layer-wise weight pruning can effectively achieve the high accuracy at targeted sparsity of the network.

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“…Gaussian process was also studied to check the conformance of flight trajectory in [22]. On the other hand, neural networks were also developed to predict flight trajectory by fitting the data distribution (i.e., flight transition patterns) from training samples automatically, such as deep neural networks (DNNs) [23,24], recurrent neural networks (RNNs) [7,25,26], and long short-term memory block [27][28][29]. The machine learning-based approaches have recently been the most popular ones and obtained promising performance for many applications.…”

Section: Introductionmentioning

confidence: 99%

A Deep Gaussian Process-Based Flight Trajectory Prediction Approach and Its Application on Conflict Detection

2020

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In this work, a deep Gaussian process (DGP) based framework is proposed to improve the accuracy of predicting flight trajectory in air traffic research, which is further applied to implement a probabilistic conflict detection algorithm. The Gaussian distribution is applied to serve as the probabilistic representation for illustrating the transition patterns of the flight trajectory, based on which a stochastic process is generated to build the temporal correlations among flight positions, i.e., Gaussian process (GP). Furthermore, to deal with the flight maneuverability of performing controller’s instructions, a hierarchical neural network architecture is proposed to improve the modeling representation for nonlinear features. Thanks to the intrinsic mechanism of the GP regression, the DGP model has the ability of predicting both the deterministic nominal flight trajectory (NFT) and its confidence interval (CI), denoting by the mean and standard deviation of the prediction sequence, respectively. The CI subjects to a Gaussian distribution, which lays the data foundation of the probabilistic conflict detection. Experimental results on real data show that the proposed trajectory prediction approach achieves higher prediction accuracy compared to other baselines. Moreover, the conflict detection approach is also validated by a obtaining lower false alarm and more prewarning time.

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Development of a robust deep recurrent neural network controller for flight applications

Cited by 16 publications

References 12 publications

A Sparse Neural Network Approach to Model Reference Adaptive Control with Hypersonic Flight Applications

A Sparse Neural Network Approach to Model Reference Adaptive Control with Hypersonic Flight Applications

Recurrent Neural Network-Based Temperature Control System Weight Pruning Based on Nonlinear Reconstruction Error

A Deep Gaussian Process-Based Flight Trajectory Prediction Approach and Its Application on Conflict Detection

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