Nonlinear Control VIA Generalized Feedback Linearization Using Neural Networks

A novel approach to nonlinear control, called Generalized Feedback Linearization (GFL), is presented. This new strategy overcomes one important drawback of the well known Feedback Linearization strategy, in the sense that it is able to handle a broader class of nonlinear systems, namely those having unstable zero dynamics. It is shown that the use of a nonlinear predictor for the system output is a key feature in the derivation of the control strategy. For certain types of systems this predictor can be found as a nonlinear function of the system input and output, allowing an output feedback control solution. The use of Artificial Neural Networks (ANN) to directly parameterize the predictor of the controlled variable when an explicit model for the system is not available, is investigated via computer simulations. This approach is based on the functional approximation capability of multi layer ANN.     

基于线性化反馈的径向神经滑模控制器设计

针对非线性系统,采用径向神经网络逼近及自适应控制方法,利用线性化反馈技术,设计一种自适应神经滑模控制器。滑模变结构控制具有独特的鲁棒性能以及对匹配不确定性和外干扰的完全自适应等特点,但容易出现系统抖振问题,将神经网络应用于滑模变结构控制系统的设计中,系统抖振得到抑制。仿真结果也表明将神经网络与滑模控制相结合的方法是行之有效的。     

基于CMAC 神经网络的一类M I MO非线性系统的自适应反馈线性化

在已知系统标称模型的基础上,将ＣＭＡＣ;神经网络用于一类状态反馈可线性化的ＭＩＭＯ连续时间不确定性非线性系统的鲁棒自适应反馈线性化,使系统获得要求的跟踪性能。在很弱的假设条件下,应用李雅普诺夫稳定性理论证明了闭环系统内的所有信号为一致最终有界。仿真算例验证了该方法的正确性与有效性。     

模糊CMAC神经网络用于MIMO非线性系统的反馈线性化

针对一类多输入多输出（ＭＩＭＯ）连续时间非线性系统,应用模糊ＣＭＡＣ神经网络,给出一种状态反馈控制器,用于使状态反馈可线笥化的未知的非线性动态系统儿得要求的患 很弱的假设条件下,应用李雅普诺夫稳定性理论严格地证明了闭环系统内的所有信号为一致最终有界（ＵＵＢ）。     

Approximate Feedback Linearisation Using Multilayer Neural Networks

In this paper we introduce the approximate feedback linearisation using multilayer feedforward neural networks. We propose to approximate a basis of the one-dimensional codistribution of an affine nonlinear system with the derivative of a multilayer neural network [6] and form a change of coordinates with n multilayer neural networks [5]. In this paper we will prove that the transformation can define a local diffeomorphism, with which a local stabilising feedback law can be designed for a kind of non-linearisable nonlinear systems.     

基于神经网络的反馈式自适应逆控制系统

由于在建立非线性逆模型时采用带有复杂非线性函数的滤波器来完成,而由此带来了结构复杂、运算量大等缺点,在实际运用上受到了很多限制.为此,采用较为简单的线性逆控制方式与单层神经网络相结合构成逆控制结构,其良好的非线性特性使系统具有逼近任何非线性模型的能力,且结构简单实用.应用举例表明这种方法在运算量和控制性能上均取得了非常好的效果.     

Approximate Adaptive Output Feedback Stabilization via Passivation of MIMO Uncertain Systems Using Neural Networks

An adaptive output feedback neural network controller is designed, which is capable of rendering affine-in-the-control uncertain multi-input–multi-output nonlinear systems strictly passive with respect to an appropriately defined set. Consequently, a simple output feedback is employed to stabilize the system. The controlled system need not be in normal form or have a well-defined relative degree. Without requiring a zero-state detectability assumption, uniform ultimate boundedness, with respect to an arbitrarily small set, of both the system's state and the output is guaranteed, along with boundedness of all other signals in the closed loop. To effectively avoid possible division by zero, the proposed adaptive controller is of switching type. However, its continuity is guaranteed, thus alleviating drawbacks connected to existence of solutions and chattering phenomena. Simulations illustrate the approach.      

反馈RBF神经网络在动态系统建模中的应用

实际过程对象一般是动态非线性系统,然而前向神经元网络很难对动态系统进行建模,为解决这一问题,在RBF网络中引入输出反馈,使其适用于动态系统建模.为更有效地确定反馈RBF网络隐含层节点的个数,引入样本密度以及样本与输出目标的关联度,用较少的神经元实现网络的训练目标.仿真结果表明反馈RBF网络具有训练快,对样本需求少等特点;与其他建模方法的比较以及对实际对象的建模表明,反馈RBF网络对动态非线性系统建模是有效的、可行的.     

Adaptive Output Feedback Control of Flexible-Joint Robots Using Neural Networks: Dynamic Surface Design Approach

In this paper, we propose a new robust output feedback control approach for flexible-joint electrically driven (FJED) robots via the observer dynamic surface design technique. The proposed method only requires position measurements of the FJED robots. To estimate the link and actuator velocity information of the FJED robots with model uncertainties, we develop an adaptive observer using self-recurrent wavelet neural networks (SRWNNs). The SRWNNs are used to approximate model uncertainties in both robot (link) dynamics and actuator dynamics, and all their weights are trained online. Based on the designed observer, the link position tracking controller using the estimated states is induced from the dynamic surface design procedure. Therefore, the proposed controller can be designed more simply than the observer backstepping controller. From the Lyapunov stability analysis, it is shown that all signals in a closed-loop adaptive system are uniformly ultimately bounded. Finally, the simulation results on a three-link FJED robot are presented to validate the good position tracking performance and robustness of the proposed control system against payload uncertainties and external disturbances.     

基于神经网络的反馈线性化非对称系统

针对对称阀控非对称缸电液伺服系统的非线性,为了提高系统的控制精度,在分析该系统的固有特性的基础上提出了反馈线性化控制策略,分析了反馈线性化系统的稳定性,并采用神经网自适应补偿控制对不确定参数进行补偿,最后选择合理参数进行仿真实验。实验表明:基于神经网络技术的反馈线性化阀控非对称系统不但稳定性好、控制精度高,而且系统的跟踪性能优越,适用于实时控制的场合。     

Output Feedback Control of a Quadrotor UAV Using Neural Networks

In this paper, a new nonlinear controller for a quadrotor unmanned aerial vehicle (UAV) is proposed using neural networks (NNs) and output feedback. The assumption on the availability of UAV dynamics is not always practical, especially in an outdoor environment. Therefore, in this work, an NN is introduced to learn the complete dynamics of the UAV online, including uncertain nonlinear terms like aerodynamic friction and blade flapping. Although a quadrotor UAV is underactuated, a novel NN virtual control input scheme is proposed which allows all six degrees of freedom (DOF) of the UAV to be controlled using only four control inputs. Furthermore, an NN observer is introduced to estimate the translational and angular velocities of the UAV, and an output feedback control law is developed in which only the position and the attitude of the UAV are considered measurable. It is shown using Lyapunov theory that the position, orientation, and velocity tracking errors, the virtual control and observer estimation errors, and the NN weight estimation errors for each NN are all semiglobally uniformly ultimately bounded (SGUUB) in the presence of bounded disturbances and NN functional reconstruction errors while simultaneously relaxing the separation principle. The effectiveness of proposed output feedback control scheme is then demonstrated in the presence of unknown nonlinear dynamics and disturbances, and simulation results are included to demonstrate the theoretical conjecture.      

An Efficient Adaptive Hierarchical Sliding Mode Control Strategy Using Neural Networks for 3D Overhead Cranes

Machine Intelligence Research - In this paper, a new adaptive hierarchical sliding mode control scheme for a 3D overhead crane system is proposed. A controller is first designed by the use of a...     

基于神经网络的非线性连续系统的自适应调节器

对于一类连续时间的非线性动态系统x=f(x)+Bu+d,当系统中的非线性函数f(x)满足线性增长条件时,首先证明了f(x)中的x落入一紧集中,然后根据神经网络的逼近性质,给出了自适应调节器的设计方法.利用李雅普诺夫稳定性理论,证明了控制算法是全局稳定的,闭环系统的状态是一致最终有界的.     

机械手的神经网络稳定自适应控制

本文针对机械手轨迹跟随控制问题，提出了一种稳定的神经网络自适应控制器设计方法，这里机械的非线性动力学假设是未知的，提出方法是神经网络方法和扇区自适应变结构控制方法的集成，扇区变结构控制的作用有两个，其一是在系统神经网络控制失灵的情形下提供闭环系统的全局稳定性；其二是在神经网络的近似域内改进系统的跟随性能，本文采用李雅普诺夫稳定理论给出了的稳定性和跟随误差收敛性的证明，并且通过数字仿真验证了提出方法     

自适应模糊神经网络控制器设计的线性化方法

基于Ｔ－Ｓ模糊推理系统模型构造一个简化形式的Ｆｕｚｚｙ神经网络（ＦＮＮ），应用Ｓｔｏｎｅ－Ｗｅｉｅｒｓｔｒａｓｓ逼近定理证明了这种ＦＮＮ网络对非线性连续函数的全局逼近性质，并利用Ｃｌａｒｋｅ一步加权最优预报控制性能指标及前向ＦＮＮ网络辨识器模型的线性化思想，提出一种间接Ｆｕｚｚｙ神经网络自适应控制算法，仿真结果证实了该算法的有效性。     

采样非线性系统的神经网络稳定自适应控制

针对一类动力学未知或难以建模的采样非线性系统,提出了一种基于神经网络的跟随控制器稳定自适应控制方法.控制器采用径向基函数神经网络近似对象的动力学非线性,神经网络参数的自适应规律由稳定理论得到.文中给出了系统稳定性和跟随误差收敛性的证明,并通过仿真实例揭示了所提方法的性能.     

Dual Adaptive Dynamic Control of Mobile Robots Using Neural Networks

This paper proposes two novel dual adaptive neural control schemes for the dynamic control of nonholonomic mobile robots. The two schemes are developed in discrete time, and the robot's nonlinear dynamic functions are assumed to be unknown. Gaussian radial basis function and sigmoidal multilayer perceptron neural networks are used for function approximation. In each scheme, the unknown network parameters are estimated stochastically in real time, and no preliminary offline neural network training is used. In contrast to other adaptive techniques hitherto proposed in the literature on mobile robots, the dual control laws presented in this paper do not rely on the heuristic certainty equivalence property but account for the uncertainty in the estimates. This results in a major improvement in tracking performance, despite the plant uncertainty and unmodeled dynamics. Monte Carlo simulation and statistical hypothesis testing are used to illustrate the effectiveness of the two proposed stochastic controllers as applied to the trajectory-tracking problem of a differentially driven wheeled mobile robot.     

Stable Sampled-data Adaptive Control of Robot Arms Using Neural Networks

Stable neural network-based sampled-data indirect and direct adaptivecontrol approaches, which are the integration of a neural network (NN)approach and the adaptive implementation of the discrete variable structurecontrol, are developed in this paper for the trajectory tracking control ofa robot arm with unknown nonlinear dynamics. The robot arm is assumed tohave an upper and lower bound of its inertia matrix norm and its states areavailable for measurement. The discrete variable structure control servestwo purposes, i.e., one is to force the system states to be within the stateregion in which neural networks are used when the system goes out of neuralcontrol; and the other is to improve the tracking performance within the NNapproximation region. Main theory results for designing stable neuralnetwork-based sampled data indirect and direct adaptive controllers aregiven, and the extension of the proposed control approaches to the compositeadaptive control of a flexible-link robot is discussed. Finally, theeffectiveness of the proposed control approaches is illustrated throughsimulation studies.     

基于递归神经网络的一类非线性无模型系统的自适应控制

给出了基于递归神经网络非线性无模型的自适应控制方案，它具有灵活、简单、方法等特点，可以处理传统方法和非线性无模型系统自适应控制方法不能控制或控制效果不理想的非线性对象。理论分析和仿真结果证明了这种方法的优越性。