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The Development of Neural Network Based System Identification and Adaptive Flight Control for an AutonomousHelicopter System

机译：基于神经网络的自主直升机系统识别与自适应飞行控制技术的发展

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摘要

This thesis presents the development of self adaptive flight controller for an unmanned helicopter system under hovering manoeuvre. The neural network (NN) based model predictive control (MPC) approach is utilised in this work. We use this controller due to its ability to handle system constraints and the time varying nature of the helicopter dynamics. The non-linear NN based MPC controller is known to produce slow solution convergence due to high computation demand in the optimisation process. To solve this problem, the automatic flight controller system is designed using the NN based approximate predictive control (NNAPC) approach that relies on extraction of linear models from the non-linear NN model at each time step. The sequence of control input is generated using the prediction from the linearised model and the optimisation routine of MPC subject to the imposed hard constraints. In this project, the optimisation of the MPC objective criterion is implemented using simple and fast computation of the Hildreth's Quadratic Programming (QP) procedure.The system identification of the helicopter dynamics is typically performed using the time regression network (NNARX) with the input variables. Their time lags are fed into a static feed-forward network such as the multi-layered perceptron (MLP) network. NN based modelling that uses the NNARX structure to represent a dynamical system usually requires a priori knowledge about the model order of the system. Low model order assumption generally leads to deterioration of model prediction accuracy. Furthermore, massive amount of weights in the standard NNARX model can result in an increased NN training time and limit the application of the NNARX model in a real-time application. In this thesis, three types of NN architectures are considered to represent the time regression network: the multi-layered perceptron (MLP), the hybrid multi-layered perceptron (HMLP) and the modified Elman network. The latter two architectures are introduced to improve the training time and the convergence rate of the NN model. The model structures for the proposed architecture are selected using the proposed Lipschitz coefficient and k-cross validation methods to determine the best network configuration that guarantees good generalisation performance for model prediction.Most NN based modelling techniques attempt to model the time varying dynamics of a helicopter system using the off-line modelling approach which are incapable of representing the entire operating points of the flight envelope very well. Past research works attempt to update the NN model during flight using the mini-batch Levenberg-Marquardt (LM) training. However, due to the limited processing power available in the real-time processor, such approaches can only be employed to relatively small networks and they are limited to model uncoupled helicopter dynamics. In order to accommodate the time-varying properties of helicopter dynamics which change frequently during flight, a recursive Gauss-Newton (rGN) algorithm is developed to properly track the dynamics of the system under consideration.It is found that the predicted response from the off-line trained neural network model is suitable for modelling the UAS helicopter dynamics correctly. The model structure of the MLP network can be identified correctly using the proposed validation methods. Further comparison with model structure selection from previous studies shows that the identified model structure using the proposed validation methods offers improvements in terms of generalisation error. Moreover, the minimum number of neurons to be included in the model can be easily determined using the proposed cross validation method. The HMLP and modified Elman networks are proposed in this work to reduce the total number of weights used in the standard MLP network. Reduction in the total number of weights in the network structure contributes significantly to the reduction in the computation time needed to train the NN model. Based on the validation test results, the model structure of the HMLP and modified Elman networks are found to be much smaller than the standard MLP network. Although the total number of weights for both of the HMLP and modified Elman networks are lower than the MLP network, the prediction performance of both of the NN models are on par with the prediction quality of the MLP network.The identification results further indicate that the rGN algorithm is more adaptive to the changes in dynamic properties, although the generalisation error of repeated rGN is slightly higher than the off-line LM method. The rGN method is found capable of producing satisfactory prediction accuracy even though the model structure is not accurately defined. The recursive method presented here in this work is suitable to model the UAS helicopter in real time within the control sampling time and computational resource constraints. Moreover, the implementation of proposed network architectures such as the HMLP and modified Elman networks is found to improve the learning rate of NN prediction. These positive findings inspire the implementation of the real time recursive learning of NN models for the proposed MPC controller. The proposed system identification and hovering control of the unmanned helicopter system are validated in a 6 degree of freedom (DOF) safety test rig. The experimental results confirm the effectiveness and the robustness of the proposed controller under disturbances and parameter changes of the dynamic system.
机译：本文提出了一种在悬停机动条件下的无人机系统自适应飞行控制器的发展。在这项工作中利用了基于神经网络（NN）的模型预测控制（MPC）方法。我们使用此控制器的原因是它具有处理系统约束的能力以及直升机动力学的时变特性。已知基于非线性NN的MPC控制器由于优化过程中的高计算需求而产生缓慢的解决方案收敛。为了解决这个问题，使用基于NN的近似预测控制（NNAPC）方法设计了自动飞行控制器系统，该方法依赖于每个时间步从非线性NN模型中提取线性模型。控制输入​​的序列是使用线性化模型中的预测和MPC的优化例程（受强加的硬约束）生成的。在本项目中，MPC客观标准的优化是通过希尔德雷斯的二次规划（QP）程序的简单快速计算来实现的。直升机动力学的系统识别通常是使用时间回归网络（NNARX）和输入变量进行的。它们的时间滞后被馈送到静态前馈网络，例如多层感知器（MLP）网络。使用NNARX结构表示动态系统的基于NN的建模通常需要有关系统模型顺序的先验知识。较低的模型阶数假设通常会导致模型预测准确性下降。此外，标准NNARX模型中的大量权重可能导致NN训练时间增加，并限制了NNARX模型在实时应用中的应用。本文考虑了三种类型的NN体系结构来表示时间回归网络：多层感知器（MLP），混合多层感知器（HMLP）和改进的Elman网络。引入后两种架构以改善训练时间和神经网络模型的收敛速度。使用拟议的Lipschitz系数和k-cross验证方法选择拟议架构的模型结构，以确定最佳网络配置，以确保良好的泛化性能以进行模型预测。大多数基于NN的建模技术都试图对直升机的时变动力学进行建模系统采用离线建模方法，无法很好地表示飞行包线的整个工作点。过去的研究工作试图使用微型批次Levenberg-Marquardt（LM）训练来更新飞行过程中的NN模型。但是，由于实时处理器中可用的处理能力有限，因此此类方法只能用于相对较小的网络，并且仅限于对未耦合的直升机动力学进行建模。为了适应在飞行过程中经常变化的直升机动力学的时变特性，开发了一种递归的高斯牛顿（rGN）算法来正确跟踪所考虑的系统动力学。在线训练的神经网络模型适用于正确建模UAS直升机动力学。使用所提出的验证方法可以正确识别MLP网络的模型结构。与先前研究中的模型结构选择的进一步比较表明，使用建议的验证方法确定的模型结构在泛化误差方面提供了改进。此外，可以使用建议的交叉验证方法轻松确定要包含在模型中的神经元的最小数量。在这项工作中提出了HMLP和改进的Elman网络，以减少标准MLP网络中使用的权重总数。网络结构中权重总数的减少极大地减少了训练NN模型所需的计算时间。根据验证测试结果，发现HMLP和改进的Elman网络的模型结构比标准MLP网络小得多。尽管HMLP网络和改进的Elman网络的权重总数均低于MLP网络，但两种NN模型的预测性能均与MLP网络的预测质量相当。尽管重复的rGN的泛化误差比离线LM方法稍高，但rGN算法对动态特性的变化更适应。即使模型结构未正确定义，rGN方法也能够产生令人满意的预测精度。本文中介绍的递归方法适用于在控制采样时间和计算资源约束范围内对UAS直升机进行实时建模。此外，发现提议的网络架构（例如HMLP和改进的Elman网络）的实施可提高NN预测的学习率。这些积极的发现启发了所提出的MPC控制器对NN模型的实时递归学习的实施。拟议的系统识别和无人直升机系统的悬停控制在6自由度（DOF）安全测试装置中得到验证。实验结果证实了所提出的控制器在动态系统的干扰和参数变化下的有效性和鲁棒性。

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    Shamsudin Syariful Syafiq;

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  • 年度 2013
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