基于观测器的伺服系统反馈线性化控制

基于重载且负载大范围变化的伺服系统提出高精度数学模型,建立扩展卡尔曼观测器对速度和模型中参数进行观测,使用基于模型的反馈线性化方法准确地将模型线性化,并使用线性控制方法设计高精度控制器。该策略的应用不但避免了传感器的测量时带来的误差,同时,在参数准确的条件下能够得到更高的控制精度。仿真实验结果表明:运用所设计的基于扩展卡尔曼观测器的反馈线性化控制策略不仅能够准确地对速度状态和参数进行观测,同时系统在跟踪性能方面也取得了较好的结果。     

Cartesian反馈线性化技术的延时补偿设计

Cartesian反馈线性化技术是减小射频功率放大器非线性失真的一种有效方式,但信号传输延时却严重影响反馈系统的稳定性,使得Cartesian反馈线性化技术受到带宽的限制.采用Smith预估补偿方法对Cartesian环路进行延时补偿设计来降低时间延时对反馈系统稳定性的影响,并给出了理论分析.计算机仿真结果也表明采用延时补偿设计的Cartesian反馈系统可以使本来早已不稳定的系统保持稳定,而且对输出功率在不同回退级时的线性化性能都有不同程度的改善.     

深海集矿机集矿头高度模糊PID控制

集矿头高度调节采用阀控非对称液压缸结构,是一个非线性系统。集矿头上升和下降两个方向的动态特性不一致,而且电液比例阀存在动作死区。为改善系统的动态性能,设计一个带死区补偿的非对称模糊PID控制器,实验表明这种方法不仅有效地解决了系统的不对称性,而且改善了集矿头高度调节的动态性能。     

基于反馈线性化的TCSC滑模控制

针对含可控串联补偿(Thyristor Controlled Series Compensation,TCSC)的电力系统非线性强及易受外部扰动的特点,应用反馈线性化方法和滑模变结构控制理论,设计了提高系统稳定性的可控串联补偿滑模控制器。通过引入反馈控制变量,基于状态反馈线性化理论实现了对非线性模型的精确线性化。采用极点配置方法设计滑模切换函数,从理论上保证发电机转子方程具有期望的极点。为了减小抖振采用指数趋近律和准滑动模态方法求取滑模控制律,使得设计的可控串联补偿非线性控制律形式简洁,鲁棒性好。为了验证该控制策略的有效性,在Matlab/Simulink环境下建立了基于反馈线性化的可控串联补偿滑模控制系统仿真模型,进行了仿真研究。仿真结果表明,与传统的控制方式相比,设计的控制器能有效地阻尼系统振荡,增强系统的暂态稳定性。     

基于状态反馈的功率放大器线性化方法

笛卡尔座标反馈线性化技术是减小RF功率放大器非线性失真的一种有效方式,但在高频输入下信号传输延时使系统类似于一高阶系统,严重影响反馈系统的稳定性,一般只适用于窄带系统。为提高系统性能,提出了一种新的状态反馈的线性化技术,并通过二次优化实现参数的优化配置并进行仿真。仿真结果表明通过新方法设计的Cartesian回路增强了系统的稳定性,提高了反馈线性化技术的线性化带宽,而且设计的系统对延时的变化具有较强的鲁棒性。     

反馈线性化控制器与id=0矢量控制器的比较

近年来,交流调速理论和新型控制理论的研究工作随着电力电子、电机制造业技术迅猛发展而不断深入,永磁同步电动机控制系统广泛应用在要求高控制精度和高可靠性的场合.文中基于id=0矢量控制与反馈线性化控制的理论分别设计两种控制器,并对其模型进行仿真比较得到:当系统参数不变时反馈线性化控制器的响应时间快,超调量小,抗扰动能力强,稳态误差小.当系统参数不确定时,一旦负载突变,反馈线性化控制器对参数的依赖性和其变化的敏感性均增强.     

反馈可线性化系统的一种新的自适应调节器设计

针对反馈可线性化系统,利用含估计参数的非线性反馈及微分同胚变换,给出一种新的,自 适应调节器设计方案.它不要求线性化微分同胚变换后系统具有特定形式,对系统所含非线 性也不作限制,它只要求变换后系统的两个特定函数矩阵在点点均为能控(稳)对.该算法的 渐近稳定性由文中定理证明.     

基于自适应模糊控制器和非线性扰动观测器的永磁直线同步电机反馈线性化控制

由于永磁直线同步电机(PMLSM)伺服系统应用于一些高精密场合,因此克服系统存在的负载扰动、参数变化等不确定性影响是提高系统性能的关键.针对不确定性问题,采用一种基于自适应模糊控制器(AFC)和非线性扰动观测器(NDO)的反馈线性化控制方法.首先设计反馈线性化控制器(FLC)实现系统的线性化,便于位置跟踪;其次采用NDO估计并补偿系统的不确定性,提高跟踪精度.但在实际运行过程中观测器增益较难选取,极易产生较大的观测误差,为此,采用AFC方法逼近NDO的观测误差,通过自适应律动态调整模糊规则,改善模糊控制器的学习能力,增强系统的鲁棒性,并用李雅普诺夫定理保证系统闭环稳定性.实验结果表明,与基于DOB和NDO的反馈线性化位置控制相比,该方法能够明显提高系统的跟踪性和鲁棒性.     

考虑非线性执行器的补偿逼近模型控制

考虑执行器的非线性, 研究了一种带补偿的逼近模型控制系统. 该控制系统包含逼近模型控制器与补偿器.逼近模型控制器根据对象的输入输出线性化关系直接得出控制律, 并由支持向量机辨识对象模型来实现. 补偿部分采用反馈环节来提高系统的鲁棒稳定性, 并采用在线估计得到的逆模型来抵消执行器的非线性特征. 文章分析了该控制系统的稳定性, 针对励磁系统的仿真实验验证了其有效性能.     

基于全调节RBF神经网络的远程网络控制器设计

针对一类具有参数不确定项的非线性网络控制系统,提出一种基于全调节RBF神经网络的反馈线性化与远程状态反馈控制方法相结合的控制策略.该控制策略首先通过设计全调节RBF神经网络的权值W,中心值φ和影响范围σ的调节律,在线补偿系统的非线性及参数不确定项;然后利用状态反馈控制解决时延条件下的网络控制问题.通过Lyapunov稳定性理论给出了系统的稳定性定理,并通过仿真实验验证了该方法的有效性.     

Feedback-Linearization-Based Neural Adaptive Control for Unknown Nonaffine Nonlinear Discrete-Time Systems

A new feedback-linearization-based neural network (NN) adaptive control is proposed for unknown nonaffine nonlinear discrete-time systems. An equivalent model in affine-like form is first derived for the original nonaffine discrete-time systems as feedback linearization methods cannot be implemented for such systems. Then, feedback linearization adaptive control is implemented based on the affine-like equivalent model identified with neural networks. Pretraining is not required and the weights of the neural networks used in adaptive control are directly updated online based on the input–output measurement. The dead-zone technique is used to remove the requirement of persistence excitation during the adaptation. With the proposed neural network adaptive control, stability and performance of the closed-loop system are rigorously established. Illustrated examples are provided to validate the theoretical findings.      

互联非线性系统反馈主导控制设计方法及汽门开度控制应用

分析了电力系统非线性的数学性质,指出电力系统非线性是一种有界非线性.在此基础上,将反馈主导方法(feedback domination method,FDM)引入多机电力系统非线性控制.该方法与反馈线性化方法不同;反馈线性化方法是通过反馈将原非线性系统转化为线性系统,反馈主导方法则是通过反馈将原非线性系统转换为特定形式的非线性系统,该特定形式的非线性系统的动态由反馈引入的非线性部分主导.以多机系统非线性汽门控制问题为例,设计了反馈主导非线性汽门控制器,该控制器仅包含本地量测量,易于实现.数值仿真表明,多机系统反馈主导非线性汽门控制器可显著提高电力系统暂态稳定性.     

Nonlinear adaptive control using neural networks and its application to CSTR systems

In this paper, adaptive tracking control is considered for a class of general nonlinear systems using multilayer neural networks (MNNs). Firstly, the existence of an ideal implicit feedback linearization control (IFLC) is established based on implicit function theory. Then, MNNs are introduced to reconstruct this ideal IFLC to approximately realize feedback linearization. The proposed adaptive controller ensures that the system output tracks a given bounded reference signal and the tracking error converges to an -neighborhood of zero with being a small design parameter, while stability of the closed-loop system is guaranteed. The effectiveness of the proposed controller is illustrated through an application to composition control in a continuously stirred tank reactor (CSTR) system.     

Robust Stable Fuzzy Control via Fuzzy Modeling and Feedback Linearization with Its Applications to Controlling Uncertain Single-link Flexible Joint Manipulators

In this paper, a robust stable fuzzy control design based on feedback linearization is presented. Takagi–Sugeno fuzzy model is used as representing the nonlinear plant model and uncertainty is assumed to be included in the model structure with known bounds. For this structured uncertainty, the closed system can be analyzed by applying the perturbation system stability analysis to the fuzzy feedback linearization systems and a sufficient condition is derived to guarantee the stability of the closed-loop system with bounded parameter uncertainties. Based on the developed analysis method, we can design a robust fuzzy controller by choosing the control parameters satisfying the robust stability condition.     

Adaptive neural decentralized control for strict feedback nonlinear interconnected systems via backstepping

An approximation based adaptive neural decentralized output tracking control scheme for a class of large-scale unknown nonlinear systems with strict-feedback interconnected subsystems with unknown nonlinear interconnections is developed in this paper. Within this scheme, radial basis function RBF neural networks are used to approximate the unknown nonlinear functions of the subsystems. An adaptive neural controller is designed based on the recursive backstepping procedure and the minimal learning parameter technique. The proposed decentralized control scheme has the following features. First, the controller singularity problem in some of the existing adaptive control schemes with feedback linearization is avoided. Second, the numbers of adaptive parameters required for each subsystem are not more than the order of this subsystem. Lyapunov stability method is used to prove that the proposed adaptive neural control scheme guarantees that all signals in the closed-loop system are uniformly ultimately bounded, while tracking errors converge to a small neighborhood of the origin. The simulation example of a two-spring interconnected inverted pendulum is presented to verify the effectiveness of the proposed scheme.     

一种基于神经网络的非线性飞行控制系统设计方案研究

在全状态反馈的前提下，设计了一种基于在线神经网络和反馈线性化的非线性直接自适应控制器。本文首先利用多重尺度摄动与动态逆技术结合，设计了无人驾驶飞机的解析动态逆控制器；然后引入一个单隐层在线神经网络来修正各种因素引起的状态误差，并证明了控制器的稳定性。最后对在线网络的实现做了详细描述。仿真分析表明，该方案具有很强的鲁棒性和对故障状态的适应性。     

Sliding Mode Neuro Adaptive Control in Trajectory Tracking for Mobile Robots

In this work a neural indirect sliding mode control method for mobile robots is proposed. Due to the nonholonomic property and restricted mobility, the trajectory tracking of this system has been one of the research topics for the last ten years. The proposed control structure combines a feedback linearization model, based on a kinematics nominal model, and a practical design that combines an indirect neural adaptation technique with sliding mode control to compensate the dynamics of the robot. Using an online adaptation scheme, a neural sliding mode controller is used to approximate the equivalent control in the neighbourhood of the sliding manifold. A sliding control is appended to ensure that the neural sliding mode control can achieve a stable closed-loop system for the trajectory-tracking control of a mobile robot with unknown nonlinear dynamics. The proposed design simultaneously guarantees the stability of the adaptation of the neural nets and obtains suitable equivalent control when the parameters of the robot model are unknown in advance. The robust adaptive scheme is applied to a mobile robot and shown to be able to guarantee that the output tracking error will converge to zero.     

A harmonic balance approach to robust neural control of MIMO nonlinear processes applied to a distillation column

Robust stability and performance are the two most basic features of feedback control process. The harmonic balance analysis based on the describing function technique enables to analyze the stability of limit cycles arising from a closed loop control process operating over nonlinear plants. In this work a robust stability analysis based on the harmonic balance is presented and applied to a neural network controller in series with a dynamic multivariable nonlinear plant under generic Lur’e configuration. The neural controller is replaced by its sinusoidal input describing function while a linearized model is derived to represent the nonlinear plant dynamics. The uncertainty induced by the high harmonics effect for the neural controller, together with the neglected nonlinear dynamics due to plant linearization are incorporated in the robustness analysis as structured norm bounded uncertainties. Stability and robustness conditions for the neural closed loop control system are discussed using the harmonic balance equation together with the structured singular values of the uncertainty. The application to a multivariable binary distillation column under feedback neurocontrol illustrates the usefulness of the robustness approach here developed to predict the absence of limit cycles, which of course is subject to the usual restrictions of the describing function method.     

Robust neural control for robotic manipulators

A robust neural control scheme for mechanical manipulators is presented. The design basically consists of an adaptive neural controller which implements a feedback linearization control law for a generic manipulator with unknown parameters, and a sliding-mode control which robustifies the design and compensates for the neural approximation errors. It is proved that the resulting closed-loop system is stable and that the trajectory-tracking control objective is achieved. Some simulation results are also provided to evaluate the design.     

基于神经网络补偿的非线性时滞系统时滞正反馈控制

A new adaptive time-delay positive feedback controller (ATPFC) is presented for a class of nonlinear time-delay systems. The proposed control scheme consists of a neural networks-based identification and a time-delay positive feedback controller. Two high-order neural networks (HONN) incorporated with a special dynamic identification model are employed to identify the nonlinear system. Based on the identified model, local linearization compensation is used to deal with the unknown nonlinearity of the system. A time-delay-free inverse model of the linearized system and a desired reference model are utilized to constitute the feedback controller, which can lead the system output to track the trajectory of a reference model. Rigorous stability analysis for both the identification and the tracking error of the closed-loop control system is provided by means of Lyapunov stability criterion. Simulation results are included to demonstrate the effectiveness of the proposed scheme.