Robust adaptive control of nonaffine nonlinear plants with small input signal changes

Assuming small input signal magnitudes, ARMA models can approximate the NARMA model of nonaffine plants. Recently, NARMA-L1 and NARMA-L2 approximate models were introduced to relax such input magnitude restrictions. However, some applications require larger input signals than allowed by ARMA, NARMA-L1 and NARMA-L2 models. Under certain assumptions, we recently developed an affine approximate model that eliminates the small input magnitude restriction and replaces it with a requirement of small input changes. Such a model complements existing models. Using this model, we present an adaptive controller for discrete nonaffine plants with unknown system equations, accessible input-output signals, but inaccessible states. Our approximate model is realized by a neural network that learns the unknown input-output map online. A deadzone is used to make the weight update algorithm robust against modeling errors. A control law is developed for asymptotic tracking of slowly varying reference trajectories.     

Adaptive output feedback control methodology applicable to non-minimum phase nonlinear systems

An adaptive output feedback control methodology is developed for a class of uncertain multi-input multi-output nonlinear systems using linearly parameterized neural networks. The methodology can be applied to non-minimum phase systems if the non-minimum phase zeros are modeled to a sufficient accuracy. The control architecture is comprised of a linear controller and a neural network. The neural network operates over a tapped delay line of memory units, comprised of the system's input/output signals. The adaptive laws for the neural-network weights employ a linear observer of the nominal system's error dynamics. Ultimate boundedness of the error signals is shown through Lyapunov's direct method. Simulations of an inverted pendulum on a cart illustrate the theoretical results.     

Output recurrent wavelet neural network-based adaptive backstepping controller for a class of MIMO nonlinear non-affine uncertain systems

In this paper, an adaptive backstepping control problem is proposed for a class of multiple-input-multiple-output nonlinear non-affine uncertain systems. An output recurrent wavelet neural network (ORWNN) is used to approximate the unknown nonlinear functions to develop the proposed adaptive backstepping controller. The proposed ORWNN combines the advantages of wavelet-based neural network, fuzzy neural network, and output feedback layer to achieve higher approximation accuracy and faster convergence. According to the estimation of ORWNN, the control scheme is designed by backstepping approach such that the system outputs follow the desired trajectories. Based on the Lyapunov approach, our approach guarantees that the system outputs converge to a small neighborhood of the references signals, that is, all signals of the closed-loop system are semi-globally uniformly ultimately bounded. Finally, simulation results including double pendulums system and two inverted pendulums on carts system are shown to demonstrate the performance and effectiveness of our approach.     

二级倒立摆的Sugeno型模糊神经网络控制

为了提高二级倒立摆系统实时控制的响应速度和稳定性,在设计Mamdani型模糊推理规则控制器控制倒立摆系统稳定的基础上,设计了一种更有效率的基于Sugeno型模糊推理规则的模糊神经网络控制器.该控制器使用BP神经网络和最小二乘法的混合算法进行参数训练.能够准确归纳输入输出量的模糊隶属度函数和模糊逻辑规则.通过与Mamdani型控制器的仿真对比及实际控制实验结果,表明该Sugeno型模糊神经网络控制器时二级倒立摆实验装置的控制具有良好的稳定性、快速性和较高的控制精度.     

Adaptive control of a class of nonlinear discrete-time systemsusing neural networks

Layered neural networks are used in a nonlinear self-tuning adaptive control problem. The plant is an unknown feedback-linearizable discrete-time system, represented by an input-output model. To derive the linearizing-stabilizing feedback control, a (possibly nonminimal) state-space model of the plant is obtained. This model is used to define the zero dynamics, which are assumed to be stable, i.e., the system is assumed to be minimum phase. A linearizing feedback control is derived in terms of some unknown nonlinear functions. A layered neural network is used to model the unknown system and generate the feedback control. Based on the error between the plant output and the model output, the weights of the neural network are updated. A local convergence result is given. The result says that, for any bounded initial conditions of the plant, if the neural network model contains enough number of nonlinear hidden neurons and if the initial guess of the network weights is sufficiently close to the correct weights, then the tracking error between the plant output and the reference command will converge to a bounded ball, whose size is determined by a dead-zone nonlinearity. Computer simulations verify the theoretical result     

基于神经网络观测器的飞控传感器在线故障诊断

为提高无人机飞行安全可靠性,针对飞行控制系统中常出现的传感器故障以及非线性气动力模型参数难以确定的问题,提出了基于BP神经网络观测器估计的故障诊断方法;引用LM改进算法对网络参数进行调整,构造了神经网络观测器模型逼近非线性系统,并运用于飞行控制系统进行在线数字仿真,对垂直陀螺输出卡死故障、恒偏差故障和恒增益故障分别进行仿真分析;仿真结果表明,所设计神经网络观测器可以有效估计系统输出,在线诊断传感器故障。     

A new approach for minimum phase output definition

This paper presents a novel method for output redefinition for linear systems. The approach also determines possible relative degrees for the systems corresponding to any new output vector. To guarantee the minimum phase property with a prescribed relative degree, a set of new conditions is introduced. A key feature of these conditions is that there is no need to any form of transformations which make the scheme suitable for optimisation problems in control to ensure the minimum phase property. Moreover, the results are useful for sensor placement problems and for obtaining minimum phase approximations of non-minimum phase systems. Numerical examples including an example of unmanned aerial vehicle systems are given to demonstrate the effectiveness of the methodology.     

A recurrent neural network for solving Sylvester equation with time-varying coefficients

Presents a recurrent neural network for solving the Sylvester equation with time-varying coefficient matrices. The recurrent neural network with implicit dynamics is deliberately developed in the way that its trajectory is guaranteed to converge exponentially to the time-varying solution of a given Sylvester equation. Theoretical results of convergence and sensitivity analysis are presented to show the desirable properties of the recurrent neural network. Simulation results of time-varying matrix inversion and online nonlinear output regulation via pole assignment for the ball and beam system and the inverted pendulum on a cart system are also included to demonstrate the effectiveness and performance of the proposed neural network.     

神经网络直接逆控制在无人机飞控系统中的应用研究

针对无人机发生故障时系统的非线性耦合特性,提出了一种基于神经网络直接逆控制的飞行控制方法,用于在飞机发生故障时进行控制律重构以恢复对飞机的控制;根据无人机发生故障时非线性运动的特点,确定适当的神经网络结构建立无人机非线性系统的逆系统,并与被控对象串连可以对非线性耦合系统进行线性化解耦,然后引入PID反馈控制进一步提高神经网络逆控制系统的性能;仿真分析表明该方法在无人机发生故障采用传统PID控制失效时,在误差允许范围内可以用来对无人机进行控制。     

一类不确定非线性系统的鲁棒自适应轨迹线性化控制

针对一类不确定非线性系统,研究了一种新的鲁棒自适应轨迹线性化控制方案.利用径向基神经网络的在线逼近能力以及被控对象分析模型的有用信息设计一种径向基神经网络干扰观测器来估计系统中存在的不确定性.观测器输出用于设计补偿控制律抵消不确定性对系统性能的影响,鲁棒自适应控制律用于克服逼近误差.采用Lyapunov方法严格证明了在自适应调节律作用下闭环系统所有误差信号最终有界.最后利用倒立摆系统验证了新方法的有效性.     

Min-max hyperellipsoidal clustering for anomaly detection in network security.

A novel hyperellipsoidal clustering technique is presented for an intrusion-detection system in network security. Hyperellipsoidal clusters toward maximum intracluster similarity and minimum intercluster similarity are generated from training data sets. The novelty of the technique lies in the fact that the parameters needed to construct higher order data models in general multivariate Gaussian functions are incrementally derived from the data sets using accretive processes. The technique is implemented in a feedforward neural network that uses a Gaussian radial basis function as the model generator. An evaluation based on the inclusiveness and exclusiveness of samples with respect to specific criteria is applied to accretively learn the output clusters of the neural network. One significant advantage of this is its ability to detect individual anomaly types that are hard to detect with other anomaly-detection schemes. Applying this technique, several feature subsets of the tcptrace network-connection records that give above 95% detection at false-positive rates below 5% were identified.     

无人机系统的故障诊断神经网络专家系统

将人工神经网络与专家系统集成应用于无人机系统故障诊断,构建一个无人机系统的智能故障诊断系统,给出系统的结构组成,详细描述神经网络专家系统的工作原理.仿真结果表明,该方法应用于无人机系统故障诊断是有效的.     

一类非线性不确定系统的最小方差神经控制

针对一类非线性不确定系统,提出一种新的基于神经网络动态补偿的最小方差控制方法（ＭＶＮＮＣ）。这种控制系统将传统的最小方差控制技术与神经网络优良的非线性逼近能力相结合,从而能有效地消除不确定性引起的控制误差,仿真实验表明,这种最小方差神经控制系统具有较强的鲁棒性和良好的动态性能。     

Experimental results on discrete-time nonlinear adaptive trackingcontrol of a flexible-link manipulator

The aim of this paper is to develop and implement a nonlinear adaptive control scheme for a single-link flexible manipulator. The controller is designed based on a discrete-time nonlinear model of the arm. The model is derived by using the forward difference method (Euler approximation). The output redefinition concept is then used so that the associated zero dynamics corresponding to the new output is guaranteed to be exponentially stable. An indirect adaptive linearizing controller is developed for the resulting minimum phase system where the "payload mass" is assumed to be unknown but its upper bound is assumed to be known a priori. The performance of the adaptively controlled closed-loop system is investigated by both numerical simulations and experimental results. The proposed controller is also compared experimentally with those of nonadaptive feedback linearization and conventional proportional derivative (PD) control strategies.     

基于多传感器数据融合的无人机GPS欺骗检测研究

本文针对实际过程中可能存在的无人机GPS欺骗情况,提出了基于多传感器数据融合的GPS欺骗检测方法。该方法通过比较多传感器惯性导航系统加上Elman神经网络修正得到的位置信息与GPS输出位置信息,从而判断无人机GPS是否受到欺骗。该方法有两个创新点,第一个是使用Elman神经网络,在不增加传感器成本的基础上其有助于提高惯导系统输出位置信息的精度;第二个创新点是使用带延迟的扩展卡尔曼滤波器,用于解决多传感器数据不同步的问题。实验结果表明,本文提出的方法能有效的检测出GPS欺骗,从而保证无人机的安全飞行。     

Neural network control for position tracking of a two-axis inverted pendulum system: experimental studies.

In this paper, experimental studies of a decentralized neural network control scheme of the reference compensation technique applied to control a 2-degrees-of-freedom (2-DOF) inverted pendulum on an x - y plane are presented. Each axis is controlled by two separate neural network controllers to have a decoupled control structure. Neural network controllers are applied not only to balance the angle of pendulum, but also to control the position tracking of the cart. The decoupled control structure can compensate for uncertainties and cancel coupling effects. Especially, a circular trajectory tracking task is tested for position tracking control of the cart while maintaining the angle of the pendulum. Experimental result shows that position control of the inverted pendulum and cart is successful.     

RS-GA神经网络无人机受风情况估计

针对现有无人机（Unmanned Aerial Vehicle,UAV）风场估计方法中存在的计算复杂、需额外搭载传感器等问题,提出基于粗糙集遗传神经网络的无人机受风状态估计方法。该方法利用粗糙集分析方法对无人机上采集的姿态信息数据集进行约简;利用遗传算法全局搜索能力强的特点优化神经网络的初始权值;用简化的无人机数据集训练神经网络即得到所需神经网络风场估计模型。仿真结果表明,该方法具有较高的识别率以及较短的训练时间,证明了其在无人机风场估计上应用的有效性。     

Adaptive Control Based on Neural Networks for an Uncertain 2-DOF Helicopter System With Input Deadzone and Output Constraints

In this paper, a study of control for an uncertain 2-degree of freedom (DOF) helicopter system is given. The 2-DOF helicopter is subject to input deadzone and output constraints. In order to cope with system uncertainties and input deadzone, the neural network technique is introduced because of its capability in approximation. In order to update the weights of the neural network, an adaptive control method is utilized to improve the system adaptability. Furthermore, the integral barrier Lyapunov function (IBLF) is adopt in control design to guarantee the condition of output constraints and boundedness of the corresponding tracking errors. The Lyapunov direct method is applied in the control design to analyze system stability and convergence. Finally, numerical simulations are conducted to prove the feasibility and effectiveness of the proposed control based on the model of Quanser's 2-DOF helicopter.      

SoHyFIS-Yager: A self-organizing Yager based Hybrid neural Fuzzy Inference System

The Hybrid neural Fuzzy Inference System (HyFIS) is a multilayer adaptive neural fuzzy system for building and optimizing fuzzy models using neural networks. In this paper, the fuzzy Yager inference scheme, which is able to emulate the human deductive reasoning logic, is integrated into the HyFIS model to provide it with a firm and intuitive logical reasoning and decision-making framework. In addition, a self-organizing gaussian Discrete Incremental Clustering (gDIC) technique is implemented in the network to automatically form fuzzy sets in the fuzzification phase. This clustering technique is no longer limited by the need to have prior knowledge about the number of clusters present in each input and output dimensions. The proposed self-organizing Yager based Hybrid neural Fuzzy Inference System (SoHyFIS-Yager) introduces the learning power of neural networks to fuzzy logic systems, while providing linguistic explanations of the fuzzy logic systems to the connectionist networks. Extensive simulations were conducted using the proposed model and its performance demonstrates its superiority as an effective neuro-fuzzy modeling technique.     

未知输出反馈非线性时滞系统自适应神经网络跟踪控制

An adaptive output feedback neural network tracking controller is designed for a class of unknown output feedback nonlinear time-delay systems by using backstepping technique. Neural networks are used to approximate unknown time-delay functions. Delay-dependent filters are introduced for state estimation. The domination method is used to deal with the smooth time-delay basis functions. The adaptive bounding technique is employed to estimate the upper bound of the neural network reconstruction error. Based on Lyapunov-Krasoviskii functional, the semi-global uniform ultimate boundedness (SGUUB) of all the signals in the closed-loop system is proved. The arbitrary output tracking accuracy is achieved by tuning the design parameters and the neural node number. The feasibility is investigated by an illustrative simulation example.