Design of Nonlinear Decoupling Controller for Double-electromagnet Suspension System

Aiming at the coupling characteristic between the two groups of electromagnets embedded in the module of the maglev train, a nonlinear decoupling controller is designed. The module is modeled as a double-electromagnet system, and based on some reasonable assumptions its nonlinear mathematical model, a MIMO coupling system, is derived. To realize the linearization and decoupling from the input to the output, the model is linearized exactly by means of feedback linearization, and an equivalent linear decoupling model is obtained. Based on the linear model, a nonlinear suspension controller is designed using state feedback. Simulations and experiments show that the controller can effectually solve the coupling problem in double-electromagnet suspension system.     

Observer-Based Nonlinear Control of A Torque Motor with Perturbation Estimation

This paper presents an observer-based nonlinear control method that was developed and implemented to provide accurate tracking control of a limited angle torque motor following a 50Hz reference waveform. The method is based on a robust nonlinear observer, which is used to estimate system states and perturbations and then employ input-output feedback linearization to compensate for the system nonlinearities and uncertainties. The estimation of system states and perturbations allows input-output linearization of the nonlinear system without an accurate mathematical model of nominal plant. The simulation results show that the observer-based nonlinear control method is superior in comparison with the conventional model-based state feedback linearizing controller.     

Linearizing control of induction motor based on networked control systems

A new approach to speed control of induction motors is developed by introducing networked control systems （NCSs） into the induction motor driving system. The control strategy is to stabilize and track the rotor speed of the induction motor when the network time delay occurs in the transport medium of network data. First, a feedback linearization method is used to achieve input-output linearization and decoupling control of the induction motor driving system based on rotor flux model, and then the characteristic of network data is analyzed in terms of the inherent network time delay. A networked control model of an induction motor is established. The sufficient condition of asymptotic stability for the networked induction motor driving system is given, and the state feedback controller is obtained by solving the linear matrix inequalities （LMIs）. Simulation results verify the efficiency of the proposed scheme.     

Nonlinear excitation controller design for power systems： an I＆I approach

The Immersion and Invariance(I&I) methodology provides a novel approach for nonlinear system control, which is distinct from the traditional feedback linearization and backstepping method.In this paper,a new excitation controller is designed for single machine infinite bus system(SMIBS) based on the I&I approach.Firstly the dynamic model of SMIBS is homeomorphously transformed to a specific form for which a stable lower-order target system is selected.Then the I&I excitation controller is designed by immersing the transformed system into the target system.Simulation results from PSCAD/EMTDC demonstrate that the proposed controller guarantees transient stability of the system after large disturbances.     

Decoupling Control of 5 Degrees of Freedom Bearingless Induction Motors Using α-th Order Inverse System Method

A 5-degrees-of-freedom bearingless induction motor is a multi-variable,nonlinear and strong-coupled system．In order to achieve rotor suspension and operation steadily,it is necessary to realize dynamic decoupling control among torque and suspension forces．In the paper,a method based on α-th order inverse system theory is used to study dynamic decoupling control．Firstly,the working principles of a 3-degrees-of-freedom magnetic bearing and a 2-degrees-of-freedom bearinglees induction motor are analyzed, the radial-axial force equations of 3-degrees-of-freedom magnetic bearing,the electromagnetic torque equation and radial force equations of the 2-degrees-of-freedom bearingless induction motor are given,and then the state equations of the 5-degrees-of-freedom bearingless induction motor are set up．Secondly,the feasibility of decoupling control based on dynamic inverse theory is discussed in detail,and the state feedback linearization method is used to decouple and linearize the system．Finally,linear control system techniques are applied to these linearization subsystems to synthesize and simulate．The simulation results have shown that this kind of control strategy can realize dynamic decoupling control among torque and suspension forces of the 5-degrees-of-freedom bearingless induction motor,and that the control system has good dynamic and static performance．     

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.     

Sliding mode decoupling control of a generic hypersonic vehicle based on parametric commands

A sliding mode decoupling attitude controller based on parametric commands is proposed for a generic hypersonic vehicle(GHV). This vehicle model has fast time variability and strong coupling, is highly nonlinear, and includes uncertain parameters. The design of the controller takes these features into account.First, for the purpose of decoupling, the inner loop of the controller is designed using the dynamic inversion(DI) method. Input/output linearization is achieved using full-state feedback to globally linearize the nonlinear dynamics of selected controlled outputs. Second, to improve the robustness of the attitude control system, the sliding mode control(SMC) method is used to design the outer loop of the controller. Although the DI and SMC methods result in decoupling and robustness, there exists serious inconsistency between the commands of the attitude angles and the commands of the first-order differential of the attitude angles. To solve this problem and achieve a trade-off between dynamic response speed and attitude-tracking precision, we propose a parametric method for calculating the commands of the first-order differential of the attitude angles. Finally,simulation studies are conducted for the trimmed cruise conditions of 33.5 km altitude and Mach 15, and the responses of the vehicle to the step commands of pitch angle, yaw angle, and rolling angle are examined. The simulation studies demonstrate that the proposed controller is robust with respect to the uncertain parameters and atmospheric disturbance and meets the performance requirements of the GHV with acceptable control inputs.     

Nonlinear Decoupling PID Control Using Neural Networks and Multiple Models

For a class of complex industrial processes with strong nonlinearity, serious coupling and uncertainty, a nonlinear decoupling proportional-integral-differential （PID） controller is proposed, which consists of a traditional PID controller, a decoupling compensator and a feedforward compensator for the unmodeled dynamics. The parameters of such controller is selected based on the generalized minimum variance control law. The unmodeled dynamics is estimated and compensated by neural networks, a switching mechanism is introduced to improve tracking performance, then a nonlinear decoupling PID control algorithm is proposed. All signals in such switching system are globally bounded and the tracking error is convergent. Simulations show effectiveness of the algorithm.     

Adaptive guidance law design based on characteristic model for reentry vehicles

In this paper an adaptive guidance law based on the characteristic model is designed to track a reference drag acceleration for reentry vehicles like the Shuttle. The characteristic modeling method of linear constant systems is extended for single-input and single-output （SlSO） linear time-varying systems so that the characteristic model can be established for reentry vehicles. A new nonlinear differential golden-section adaptive control law is presented. When the coefficients belong to a bounded closed convex set and their rate of change meets some constraints, the uniformly asymptotic stability of the nonlinear differential golden-section adaptive control system is proved. The tracking control law, the nonlinear differential golden-section control law, and the revised logical integral control law are integrated to design an adaptive guidance law based on the characteristic model. This guidance law overcomes the disadvantage of the feedback linearization method which needs the precise model. Simulation results show that the proposed method has better performance of tracking the reference drag acceleration than the feedback linearizaUon one.     

基于二阶运动学模型的移动机器人主从跟踪系统的鲁棒控制

In this paper, we study the problem of modeling and controlling leader-follower formation of mobile robots. First, a novel kinematics model for leader-follower robot formation is formulated based on the relative motion states between the robots and the local motion of the follower robot. Using this model, the relative centripetal and Coriolis accelerations between robots are computed directly by measuring the relative and local motion sensors, and utilized to linearize the nonlinear system equations. A formation controller, consisting of a feedback linearization part and a sliding mode compensator, is designed to stabilize the overall system including the internal dynamics. The control gains are determined by solving a robustness inequality and assumed to satisfy a cooperative protocol that guarantees the stability of the zero dynamics of the formation system. The proposed controller generates the commanded acceleration for the follower robot and makes the formation control system robust to the effect of unmeasured acceleration of the leader robot. Furthermore, a robust adaptive controller is developed to deal with parametric uncertainty in the system. Simulation and experimental results have demonstrated the effectiveness of the proposed control method.     

EMS型磁悬浮列车模块悬浮系统的模型参考自适应控制

模块是EMS型磁悬浮列车的基本功能单元,常用的悬浮控制方法是将其两端等效为两个完全独立的对象分别设计控制器。实际上模块两端的运动状态是互相耦合的,独立设计的控制器很难解决彼此之间的状态耦合。本文针对这种耦合情况,将模块视为一个刚体对象,采用反馈线性化方法实现模块运动的电气解耦,并针对模型的不确定性设计了模块悬浮系统的模型参考自适应控制器。仿真结果表明,该自适应控制器有效地解决耦合问题,提高悬浮系统的性能。     

高超声速飞行器非线性鲁棒控制律设计

高超声速飞行器具有模型非线性程度高、耦合程度强、参数不确定性大、抗干扰能力弱等特点,其自主控制具有较大的挑战.论文提出了一种基于鲁棒补偿技术和反馈线性化方法的非线性鲁棒控制方法.文中首先采用反馈线性化的方法对纵向模型进行输入输出线性化,实现速度和高度通道的解耦和非线性模型的线性化.针对得到的线性模型,设计包括标称控制器和鲁棒补偿器的线性控制器.基于极点配置原理,设计标称控制器使标称线性系统具有期望的输入输出特性,利用鲁棒补偿器来抑制参数不确定性和外界扰动对于闭环控制系统的影响.基于小增益定理,证明了闭环控制系统的鲁棒稳定性和鲁棒跟踪性能.相比于非线性回路成形控制方法,仿真结果表明了所设计非线性鲁棒控制算法的有效性和优越性.     

基于滑模观测器的直线伺服系统反馈线性化速度跟踪控制

在许多高速、高精的直线伺服系统中,要求能实现对速度的快速精确跟踪,但其模型的非线性和变量间的耦合给控制带来难度.对高速、高精速度跟踪控制中,电流和速度的变化过程在时间尺度上相对接近,不能简单地采用磁场定向矢量控制方法实现静态解耦,否则电流和速度间的非线性耦合将破坏速度跟踪品质.采用状态反馈线性化方法来实现永磁直线同步电动机(PMLSM)模型的精确线性化和动态解耦.利用非线性坐标变换和非线性反馈将系统解耦成独立的线性电流子系统和速度子系统.通过扩展滑模观测器来实现对所需要的动子速度、加速度和负载扰动的鲁棒观测.并利用李雅普诺夫理论对由反馈线性化和滑模观测器构成的非线性闭环系统的稳定性进行了证明.仿真结果表明该方案使PMLSM伺服系统具有良好的鲁棒速度跟踪性能.     

电流滞环控制PWM逆变器异步电动机的非线性解耦控制系统

本文针对电流滞环控制PWM逆变器异步电动机系统这一非线性、多变量、强耦合的控 制对象,采用非线性变换和非线性反馈,实现了系统的动态解耦和全局线性化.其中关键问 题是对合性条件的满足,从而分解成线性化的转速子系统和转子磁链子系统,两个子系统的 调节器可按古典线性理论设计.最后,用8096十六位单片机实现非线性解耦控制算法,组成 了交流变频调速系统,并给出实验结果.     

基于LESO的MMC-RPC反馈线性化直接功率控制

模块化多电平铁路功率调节器作为一个耦合的多变量非线性系统, 传统PI控制的直接功率控制难以实现 对系统的精确解耦. 本文提出了一种基于线性扩张状态观测器的反馈线性化直接功率控制方法, 根据Lie导数构建 了模块化多电平铁路功率调节器(MMC-RPC)两输入/两输出功率仿射模型, 设计了精确反馈线性化功率解耦控制 器. 针对不确定因素等扰动对精确反馈线性化控制效果的影响, 设计了线性扩张状态观测器对扰动进行观测和补 偿, 实现了功率的精确跟踪控制. 最后, 通过MATLAB/Simulink平台搭建仿真模型对所提控制方法进行了验证.     

动物细胞悬浮培养过程神经网络逆解耦控制

动物细胞的悬浮培养以细胞增殖快、生产效率高等优势,成为动物细胞大规模培养的首选方式。而动物细胞悬浮培养过程是一个非线性、强耦合的多输入多输出系统,对一些生物参数(如细胞密度、基质浓度和产物浓度)的控制是提高整个生产水平的关键,应用神经网络逆系统方法对动物细胞悬浮培养过程进行线性化解耦控制,根据培养过程的特点,给出了相应的数学模型,并证明了系统的可逆性,利用神经网络的非线性逼近能力辨识出原系统的逆系统,然后串接在原系统前面构成伪线性复合系统,使动物细胞悬浮培养过程线性化解耦成三个子系统：一阶线性细胞密度子系统、一阶线性基质浓度子系统和一阶线性产物浓度子系统,最后设计模糊PID控制器对各解耦后的线性子系统进行控制,避免了传统PID控制器最优参数选取困难的问题。仿真结果表明,神经网络逆系统方法实现了对动物细胞悬浮培养过程的线性化解耦,系统对给定输入实现了高性能跟踪控制。     

可逆冷带轧机速度张力系统的动态滑模解耦控制

为削弱可逆冷带轧机速度张力系统中各变量间的非线性耦合影响,本文提出了一种基于幂指数趋近律的微分几何动态滑模解耦控制方法.首先,应用微分几何理论,通过非线性状态反馈和坐标变换,实现了可逆冷带轧机速度张力非线性耦合系统的输入/输出动态解耦和线性化.其次,针对解耦后得到的各独立线性子系统,综合考虑可逆冷带轧机速度张力系统的负载扰动、参数摄动和未建模动态等不确定部分的影响,基于幂指数趋近律设计了动态滑模控制器.理论分析表明,所提出的控制方法能够保证闭环系统渐近稳定,并能有效削弱系统抖振.最后,对某1422mm可逆冷带轧机速度张力非线性耦合系统进行仿真,并同其他解耦控制方法相比较,结果验证了所提出方法的有效性.     

Robust Control System Design for an Uncertain Nonlinear System Using Minimax LQG Design Method

A systematic approach to design a nonlinear controller using minimax linear quadratic Gaussian regulator (LQG) control is proposed for a class of multi‐input multi‐output nonlinear uncertain systems. In this approach, a robust feedback linearization method and a notion of uncertain diffeomorphism are used to obtain an uncertain linearized model for the corresponding uncertain nonlinear system. A robust minimax LQG controller is then proposed for reference command tracking and stabilization of the nonlinear system in the presence of uncertain parameters. The uncertainties are assumed to satisfy a certain integral quadratic constraint condition. In this method, conventional feedback linearization is used to cancel nominal nonlinear terms and the uncertain nonlinear terms are linearized in a robust way. To demonstrate the effectiveness of the proposed approach, a minimax LQG‐based robust controller is designed for a nonlinear uncertain model of an air‐breathing hypersonic flight vehicle (AHFV) with flexibility and input coupling. Here, the problem of constructing a guaranteed cost controller which minimizes a guaranteed cost bound has been considered and the tracking of velocity and altitude is achieved under inertial and aerodynamic uncertainties.     

矿用自卸车主动油气悬架非线性控制方法研究

为了对矿用自卸车主动油气悬架进行有效控制,分析了其油气弹簧的刚度力与阻尼力的非线性特性,进而建立了两自由度油气悬架的非线性力学模型。应用微分几何线性化理论,经过恰当的坐标变换和反馈控制,实现非线性系统的精确线性化,而后对其设计使用线性PID控制研究;根据选定的控制目标及输出变量,通过制定相应的模糊控制规则表,设计模糊PID控制器,对悬架系统使用模糊PID控制研究。仿真结果表明,与被动悬架相比,精确线性化后的PID控制悬架以及模糊PID控制悬架均能显著提高车辆的平顺性和操稳性,使悬架的动态性能趋于稳定;另外,与模糊PID控制相比,线性化PID控制对于改善车辆行驶平顺性优势更加明显。