基于定转子电阻误差补偿的感应电动机自适应逆解耦控制研究

对于具有多变量、非线性、强耦合、慢时变等特征的异步电动机调速系统, 实现定子磁链与电磁转矩的高精度动态解耦是提高系统性能的关键. 首先通过非线性状态反馈建立感应电动机的积分逆模型, 并在此基础上提出了一个基于定、转子电阻误差补偿的感应电动机自适应逆解耦控制方法, 将补偿后的积分逆模型串联到对象的输入端建立广义被控对象. 复杂的感应电动机调速系统被解耦成电磁转矩与定子磁链的两个独立回路, 利用线性系统理论分别对独立回路进行综合设计, 实现定子磁链和电磁转矩对各自给定值的渐近跟踪. 利用Matlab进行了仿真实验, 实验结果验证了建议方案的有效性和可行性.     

基于模糊神经网络α阶逆系统的发酵过程多变量解耦控制

将逆系统方法与模糊神经网络相结合, 提出一种基于模糊神经网络®阶逆系统的发酵过程解耦控制方法. 在分析了系统可逆性的基础上, 利用模糊神经网络建立发酵过程的非线性逆模型, 然后将得到的模糊神经α阶逆系统与发酵过程串联复合成伪线性系统, 最后设计专家控制器实现高性能闭环解耦控制. 仿真结果表明, 提出的解耦控制方法能够适应发酵过程模型的不确定性和参数的时变性, 具有较强的鲁棒性, 克服了解析逆系统解耦控制方法依赖于过程模型和对模型参数的变化很敏感的缺点, 且结构简单, 易于实现.     

Computer generation of symbolic kinematics for robot manipulators

Computer generation of symbolic solutions for the direct and inverse robot kinematics is a desired capability not previously available to robotics engineers. In this article, we present a methodology for the design of a software system capable of solving the direct and inverse kinematics for n degree of freedom (dof) manipulators in symbolic form. The inputs to the system are the Denavit-Hartenberg parameters of the manipulator. The outputs of the system are the direct and inverse kinematics solutions in symbolic form. The system consists of a symbolic processor to perform matrix and algebraic manipulations and an expert system to solve the class of nonlinear equations involved in the solution of the inverse kinematics problem. The system can be used to study robot kinematics configurations whose inverse kinematics solutions are not known to exist a priori. Two examples are included to illustrate its capabilities. The first example provides explicit analytical solutions, previously believed nonexistent, for a 3 dof manipulator. A second example is included for a robot whose inverse kinematics solution requires intensive algebraic manipulations.     

Filtered inverse systems

In this paper, we propose a new type of inverse called the filtered inverse. This inverse reconstructs the approximate value of the input to the original system without differentiators. The definition and the existence condition of the filtered inverse are given first. Then the stability and minimal realization of the inverse are discussed. Also, the stability of conventional inverses without differentiators are examined to show the necessity for filtered inverses. Furthermore, the application of such an inverse to servo system synthesis is discussed.     

Inverse optimal control for strict‐feedforward nonlinear systems with input delays

We consider inverse optimal control for strict‐feedforward systems with input delays. A basic predictor control is designed for compensation for this class of nonlinear systems. Furthermore, the proposed predictor control is inverse optimal with respect to a meaningful differential game problem. For a class of linearizable strict‐feedforward system, an explicit formula for compensation for input delay, which is also inverse optimal with respect to a meaningful differential game problem, is also acquired. A cart with an inverted pendulum system is given to illustrate the validity of the proposed method.     

Adaptive fuzzy model based inverse controller design using BB-BC optimization algorithm

The use of inverse system model as a controller might be an efficient way in controlling non-linear systems. It is also a known fact that fuzzy logic modeling is a powerful tool in representing nonlinear systems. Therefore, inverse fuzzy model can be used as a controller for controlling nonlinear plants. In this context, firstly, a new fuzzy model based inverse controller design methodology is presented in this study. The design methodology introduced here is based on a recursive optimization procedure that searches for an optimal inverse model control signal at every sampling time. Since the task of optimization should be accomplished in between two sampling periods the use of a fast optimization algorithm becomes essential. For this reason, Big Bang-Big Crunch (BB-BC) optimization algorithm is used due to its low computational time and high global convergence properties. Even though, inverse model controllers may produce perfect control while operating in an open loop fashion, this open loop control would not be sufficient in the case of modeling mismatches or disturbances that might occur over the system. In order to overcome this problem, secondly, an on-line adaptation mechanism via BB-BC optimization algorithm is introduced in addition to BB-BC optimization based fuzzy model inverse controller. The adaptation mechanism is used to update the related parameters of the model while minimizing the absolute value of the instantaneous error between the system and model outputs. In this manner, the system output is somehow fed back, the overall control form can be considered as a closed-loop system. The new fuzzy model based inverse control scheme with the new online adaptation mechanism has been implemented and tested on the two real time processes; namely, heat transfer and pH processes and very satisfactory results has been reported.     

弯曲型导电聚合物驱动器逆模型控制

针对三层弯曲型导电聚合物驱动器,研究了一种无需外部传感反馈装置的逆模型控制方法。通过实验辨识获得驱动器系统传递函数准确,以驱动器系统的4阶传递函数建立的逆模型控制系统结构简单、易于实现。通过补偿驱动器位移漂移特性提高位移控制精度。实验结果表明：其所提出的具有位移漂移补偿的逆模型控制位移输出能够快速有效地跟踪驱动器的实际位移响应,同时精度符合控制要求。     

Adaptive control of plants with unknown hystereses

For a system with hysteresis, the authors present a parameterized hysteresis model and develop a hysteresis inverse. The authors then design adaptive controllers with an adaptive hysteresis inverse for plants with unknown hysteresis. A new adaptive controller structure is introduced which is capable of achieving a linear parameterization and a linear error model in the presence of a hysteresis nonlinearity. A robust adaptive law is used to update the controller parameters and hysteresis inverse parameters, which ensures the global boundedness of the closed-loop signals for a wide class of of hysteresis models. Simulations show that the use of the adaptive hysteresis inverse leads to major improvements of system performance     

基于支持向量机的非线性内模解耦控制

针对非线性内模控制在应用于多变量系统时逆模型难以建立的问题, 提出了支持向量机α阶逆系统的内模解耦控制方法. 该方法利用支持向量机辨识非线性系统的逆模型, 并将其串连在原系统之前, 运用逆系统方法的思想, 将一个多变量、非线性、强耦合的复杂系统通过反馈线性化解耦成多个相互独立的单输入单输出的伪线性复合子系统. 对求得的伪线性系统采用内模控制方法进行控制. 仿真试验表明该方法不需要系统精确的数学模型, 较一般的逆系统方法鲁棒稳定性好, 设计简单, 跟踪精度高, 是解决多变量非线性系统控制的一种可行的理论方法.     

Adaptive fuzzy robust control of PMSM with smooth inverse based dead-zone compensation

It is a challenging work to design high precision/high performance motion controller for permanent magnet synchronous motor (PMSM) due to some difficulties, such as varying operating conditions, parametric uncertainties and external disturbances. In order to improve tracking control performance of PMSM, this paper proposes an adaptive fuzzy robust control (AFRC) algorithm with smooth inverse based dead-zone compensation. Instead of nonsmooth dead-zone inverse which would cause the possible control signal chattering phenomenon, a new smooth dead-zone inverse is proposed for non-symmetric dead-zone compensation in PMSM system. AFRC controller is synthesized by combining backstepping technique and small gain theorem. Discontinuous projectionbased parameter adaptive law is used to estimate unknown system parameters. The Takagi-Sugeno fuzzy logic systems are employed to approximate the unstructured dynamics. Robust control law ensures the robustness of closed loop control system. The proposed AFRC algorithm with smooth inverse based dead-zone compensation is verified on a practical PMSM control system. The comparative experimental results indicate that the smooth inverse for non-symmetric dead-zone nonlinearity can effectively avoid the chattering phenomenon which would be caused by nonsmooth dead-zone inverse, and the proposed control strategy can improve the PMSM output tracking performance.     

永磁同步电机调速系统的多维泰勒网逆控制

针对永磁同步电机(PMSM)的高性能控制问题,在充分考虑时变特性、不确定性以及测量噪声等随机因素的基础上,通过PMSM的逆系统将被控对象补偿成为具有线性传递关系的系统,提出一种基于改进自适应逆控制的控制方案.采用矢量控制的双闭环控制结构,将多维泰勒网逆控制方法引入速度环.首先,对PMSM数学模型的可逆性进行证明以解决非线性系统逆建模的存在性问题;然后,建立新颖的动态网络化控制器-----多维泰勒网(MTN),其具有结构简单、计算复杂度低的优点;最后,为了实现高精度的速度控制,将3个MTN分别作为实现系统建模的自适应模型辨识器、逆建模的自适应逆控制器和噪声干扰消除的非线性自适应滤波器,并将PMSM的动态响应控制和消除干扰的控制分为相对独立的过程进行,同时实现最优控制.仿真结果表明,所提出控制方案能够实现PMSM伺服系统精确的速度控制,具有良好的跟踪性能和较强的抗干扰能力.     

基于LS-SVM逆模型的青霉素发酵软测量方法

针对青霉素发酵过程中的某些关键生物参数(如菌体浓度、基质浓度、产物浓度)难以实时在线测量的问题,提出一种基于最小二乘支持向量机(LS-SVM)逆模型的软测量方法。该方法用具有高斯核函数的LS-SVM,离线建立被测对象的静态非线性逆模型。由静态非线性逆模型外加若干表征非线性动态特征微分器,构成了非线性系统的逆系统,将此逆系统串联在原发酵系统之后,得到"线性化"的伪线性系统。仿真结果表明,该方法能够对青霉素发酵过程中不可在线测量的关键变量进行预测,且达到了较高的测量精度。     

基于GGAP-RBF神经网络逆的复杂多电机系统同步控制

针对以矢量控制工作方式的复杂多电机同步系统,以3台电机同步系统为研究对象,证明了该系统可逆,提出了基于增长和修剪的RBF(GGAP-RBF)神经网络逆的多电机同步控制方法.将RBF神经网络逆串接在三电机系统之前,组成由速度和张力子系统组成的伪线性复合系统,分别对速度和张力子系统设计闭环控制器,实现了对速度和张力的解耦控...     

基于LabVIEW的神经网络自适应逆控制系统

目前基于人工神经网络的非线性自适应逆控制研究主要集中在Matlab仿真研究方面,无法直接推广为实际应用。为此,采用基于LabVIEW的动态神经网络非线性自适应逆控制方法,首先在LabVIEW中建立动态神经网络结构及在线学习算法,并依此建立非线性对象的辨识器和逆控制器等模型;然后构建完整的非线性对象自适应逆控制系统,并在LabVIEW环境中通过仿真验证了系统性能。通过配置相应的数据采集设备,该系统可以直接推广为实际应用。     

Stable inversion of LPTV systems with application in position-dependent and non-equidistantly sampled systems

Many control applications, including feedforward and learning control, involve the inverse of a dynamical system. For nonminimum-phase systems, the response of the inverse system is unbounded. For linear time-invariant (LTI), nonminimum-phase systems, a bounded, noncausal inverse response can be obtained through an exponential dichotomy. For generic linear time-varying (LTV) systems, such a dichotomy does not exist in general. The aim of this paper is to develop an inversion approach for an important class of LTV systems, namely linear periodically time-varying (LPTV) systems, which occur in, e.g. position-dependent systems with periodic tasks and non-equidistantly sampled systems. The proposed methodology exploits the periodicity to determine a bounded inverse for general LPTV systems. Conditions for existence are provided. The method is successfully demonstrated in several application cases, including position-dependent and non-equidistantly sampled systems.     

Extended Jacobian inverse kinematics algorithm for nonholonomic mobile robots

By a generalization of the well-known extended Jacobian method for stationary manipulators, we derive the extended Jacobian inverse kinematics algorithm for nonholonomic mobile robots. Key points of the derivation consist in defining the kinematics of a mobile robot as the end-point map of a driftless control system, decomposing the space of control functions of this system into a finite and an infinite dimensional subspaces, and introducing an augmenting kinematics map subordinated to this decomposition. The original kinematics and the augmenting kinematics constitute the extended kinematics. The inverse Jacobian of the extended kinematics defines the extended Jacobian inverse kinematics algorithm. By design, the algorithm is repeatable. As an example, we derive a specific extended Jacobian inverse kinematics algorithm and illustrate its performance with the computer simulations.     

Adaptive actuator compensation control with feedback linearization

An adaptive inverse controller is developed for feedback linearizable nonlinear systems with nonsmooth actuator nonlinearities. The use of an actuator nonlinearity inverse and a feedback linearizing controller leads to an error equation suitable for deriving an adaptive update law for the inverse parameters. Closed-loop signal boundedness is proved analytically, and system performance improvement is shown by simulation results. Such adaptive control schemes are also developed for multivariable nonlinear systems with actuator nonlinearities. For nonlinear systems that do not possess a relative degree, dynamic extension is employed to realize adaptive inverse compensation designs for actuator nonlinearities. These adaptive designs ensure closed-loop stability in the presence of uncertain actuator nonlinearities     

基于ε滤波和反馈误差学习原理的自适应逆控制

如何调节逆控制器是自适应逆控制的一个关键.本文采用基于ε滤波和反馈误差学习原理的方法来解决这个问题.利用反馈误差学习原理调节逆控制器,利用ε滤波方法在线构造反馈控制器,从而构成一个非线性自适应逆控制系统,使得被控对象的输出跟随指令输入.仿真结果表明了这种方法的有效性.