含间隙非线性的Wiener-Hammerstein系统复合补偿控制

针对含非对称间隙环节的Wiener-Hammerstein系统提出了一种新的由输出反馈和间隙动态逆补偿构成的复合控制方案.首先应用参数化分段线性表达式设计了未知参数的整体估计模型,可同时估计线性参数和间隙的特征参数,然后提出了一种新的误差有界的间隙动态逆模型,该模型可使得驱动信号能在间隙的不同线性段之间快速切换,在此基础上设计了鲁棒补偿控制律,同时对输入线性环节采用输出反馈控制构建了复合控制器,通过李亚普诺夫方法证明了闭环系统的稳定性.带减速器的单电机伺服系统模型的仿真结果表明该方法在跟踪精度良好的同时可使系统动态响应满足要求.     

间隙三明治系统的改进卡尔曼状态估计

控制工程中许多实际系统都可以描述为带间隙的三明治系统,由于间隙具有非光滑、局部记忆性和多值映射等复杂非线性特性,使得整个三明治系统的内部状态估计工作具有很大挑战性.首先根据间隙三明治系统的特性引入了几个自动切换函数,采用关键项分离原理,建立了随机噪声干扰下间隙三明治系统的非光滑整体伪线性状态空间模型.针对该系统提出了一种非光滑的改进卡尔曼滤波算法以估计系统状态,其工作机制能够随系统当前工作区间的转变而自动切换模式.仿真和实验结果表明,针对含噪声的间隙三明治系统,非光滑的改进卡尔曼滤波算法对系统状态的估计准确度要高于传统卡尔曼滤波算法.     

Parameter identification of Hammerstein systems containing backlash operators with arbitrary-shape parametric borders

A two-stage parameter identification method is developed for Hammerstein systems containing backlash nonlinearities bordered by parametric arbitrary-shape lines. In the first stage, a persistently exciting input is designed so that the linear subsystem can be made decoupled from the nonlinear element. Therefore, linear subsystem identification is coped with using a least squares estimator enjoying consistency, due to input persistent excitation. Then, the backlash parameters are estimated using appropriate periodic exciting signals and consistent parameter estimators.     

Robust‐decentralized tracking control for a class of uncertain MIMO nonlinear systems with time‐varying delays

A new control design method based on signal compensation is proposed for a class of uncertain multi‐input multi‐output (MIMO) nonlinear systems in block‐triangular form with nonlinear uncertainties, unknown virtual control coefficients, strongly coupled interconnections, time‐varying delays, and external disturbances. By this method, the controller design is performed in a backstepping manner. At each step of backstepping procedure, a nominal virtual controller is first designed to get desired output tracking for the nominal disturbance‐free subsystem, and then a robust virtual compensator is designed to restrain the effect of the uncertainties, delays involved in the subsystem, and the couplings among the subsystems. The designed controller is linear and time‐invariant, so the explosion of complexity in the control law is avoid. It is proved that robust stability and robust practical tracking property of the closed‐loop system can be ensured, and the tracking errors can be made as small as desired. Copyright © 2013 John Wiley & Sons, Ltd.     

Fuzzy Mixed H2/H∞ Optimization-Based Decentralized Model Reference Control and Application to Piezo-Driven XY Table Systems

In this paper, a decentralized model reference control via fuzzy mixed H²/H^infin optimization design was developed. Each subsystem contained L linear pulse transfer function systems (LPTFSs). The reference model for every LPTFS was first designed to shape the response of the ith closed-loop subsystem. Then the H² -norm of the output error (i.e., the difference between the output of the reference model and the system) and weighted control input of the jth LPTFS was minimized to obtain a control such that smaller energy consumption with bounded tracking error of the jth LPTFS was achieved. However, an output disturbance of the jth LPTFS caused by the interactions among the LPTFSs, the interconnections among the subsystems, modeling errors, and external loads deteriorated system performance or even resulted in instability. In this situation, the H ^infin-norm of weighted sensitivity between output disturbance and output error of the jth LPTFS was minimized to attenuate its effect. A nonlinear control based on output error for every LPTFS was also established to improve robust performance. The stability of the overall system was then verified by Lyapunov stability criterion. The application to piezo-driven XY table system (PD-XY-TS) was carried out to confirm the usefulness of the proposed control     

Composite nonlinear feedback control for linear singular systems with input saturation

This paper investigates the composite nonlinear feedback (CNF) control technique for linear singular systems with input saturation. First, a linear feedback control law is designed for the step tracking control problem of linear singular systems subject to input saturation. Then, based on this linear feedback gain, a CNF control law is constructed to improve the transient performance of the closed-loop system. By introducing a generalized Lyapunov equation, this paper develops a design procedure for constructing the CNF control law for linear singular systems with input saturation. After decomposing the closed-loop system into fast subsystem and slow subsystem, it can be shown that the nonlinear part of the CNF control law only relies on slow subsystem. The improvement of transient performance by the proposed design method is demonstrated by an illustrative example.     

用于清洗绝缘子的四旋翼无人机抗回冲力控制

本文主要针对利用四旋翼无人机清洗绝缘子时受到的回冲力干扰及姿态控制问题,提出了一种用于清洗绝缘子的无人机抗回冲力控制方法.对于无人机系统,本文运用非线性控制方法中的反步法来设计姿态控制器,使其达到输入状态稳定,并对外部扰动具有鲁棒性.本文首先根据无人机运动模型建立了其动力学方程.之后,运用动量定理和流体力学中的伯努利方程对所受的回冲力进行建模.然后,运用反步法设计姿态控制器并证明其稳定性.最后,运用MATLAB对无人机系统进行仿真实验,其结果证明了文中所提出的控制方法的有效性和鲁棒性.本文所提出的控制方案可以避免目前已有的一些技术存在的缺陷,并且为无人机抗扰动控制和绝缘子冲洗都提供了发展空间.     

基于鲁棒观测器的带间隙三明治系统故障预报

工程实践中常见的带间隙的三明治系统的准确故障预报具有重要的现实意义,为此,本文构建了一种新的动态鲁棒观测器对其进行故障预报.首先,通过将非光滑项转化为干扰项的方法,将间隙非光滑三明治系统转化为可用动态鲁棒观测器设计方法设计的系统.其次,采用零点配置和最小化基准区间观测器的范数(H_∞,F/H_,F)指标的方法确定动态鲁棒观测器的增益矩阵.最后,通过仿真,分别比较了基于非光滑鲁棒观测器和基于传统观测器的故障预报效果,比较结果表明:鲁棒观测器能够及时地准确预报传统观测器无法预报的故障,且有效减少了故障的漏报和错报现象.     

Fuzzy neural-based control for nonlinear time-varying delay systems.

In this paper, a partially known nonlinear dynamic system with time-varying delays of the input and state is approximated by N fuzzy-based linear subsystems described by a state-space model with average delay. To shape the response of the closed-loop system, a set of fuzzy reference models is established. Similarly, the same fuzzy sets of the system rule are employed to design a fuzzy neural-based control. The proposed control contains a radial-basis function neural network to learn the uncertainties caused by the approximation error of the fuzzy model (e.g., time-varying delays and parameter variations) and the interactions resulting from the other subsystems. As the norm of the switching surface is inside of a defined set, the learning law starts; in this situation, the proposed method is an adaptive control possessing an extra compensation of uncertainties. As it is outside of the other set, which is smaller than the aforementioned set, the learning law stops; under this circumstance, the proposed method becomes a robust control without the compensation of uncertainties. A transition between robust control and adaptive control is also assigned to smooth the possible discontinuity of the control input. No assumption about the upper bound of the time-varying delays for the state and the input is required. However, two time-average delays are needed to simplify the controller design: 1) the stabilized conditions for every transformed delay-free subsystem must be satisfied; and 2) the learning uncertainties must be relatively bounded. The stability of the overall system is verified by Lyapunov stability theory. Simulations as compared with a linear transformed state feedback with integration control are also arranged to consolidate the usefulness of the proposed control.     

BACKLASH COMPENSATION WITH FILTERED PREDICTION IN DISCRETE TIME NONLINEAR SYSTEMS BY DYNAMIC INVERSION USING NEURAL NETWORKS

A dynamics inversion compensation scheme is designed for control of nonlinear discrete‐time systems with input backlash. This paper extends the dynamic inversion technique to discrete‐time systems by using a filtered prediction, and shows how to use a neural network (NN) for inverting the backlash nonlinearity in the feedforward path. The technique provides a general procedure for using NN to determine the dynamics preinverse of an invertible discrete time dynamical system. A discrete‐time tuning algorithm is given for the NN weights so that the backlash compensation scheme guarantees bounded tracking and backlash errors, and also bounded parameter estimates. A rigorous proof of stability and performance is given and a simulation example verifies performance. Unlike standard discrete‐time adaptive control techniques, no certainty equivalence (CE) or linear‐in‐the‐parameters (LIP) assumptions are needed.     

线性系统极点配置控制器的鲁棒稳定性分析

本文利用所建立的线性系统鲁棒稳定性的分析结果分析了离散和连续的极点配置控制系统关于时变参数摄动在输入输出有界意义下的鲁棒稳定性。指出该控制系统对于参数摄动总具有一定的鲁棒性,并给出了保持系统输入输出有界情况下参数摄动应满足的限度。     

Design of an RMPC with a time‐varying terminal constraint set for tracking problem

This paper presents a robust model predictive control algorithm with a time‐varying terminal constraint set for systems with model uncertainty and input constraints. In this algorithm, the nonlinear system is approximated by a linear model where the approximation error is considered as an unstructured uncertainty that can be represented by a Lipschitz nonlinear function. A continuum of terminal constraint sets is constructed off‐line, and robust stability is achieved on‐line by using a variable control horizon. This approach significantly reduces the computational complexity. The proposed robust model predictive controller with a terminal constraint set is used in tracking set‐points for nonlinear systems. The effectiveness of the proposed method is illustrated with a numerical example. Copyright © 2015 John Wiley & Sons, Ltd.     

Robust fault diagnosis scheme in a class of nonlinear system based on UIO and fuzzy residual

In this study, a novel robust fault diagnosis scheme is developed for a class of nonlinear systems when both fault and disturbance are considered. The proposed scheme includes both component and sensor fault with nonlinear system that transferred to nonlinear Takagi-Sugeno (T-S) model. It considers a larger category of nonlinear system when fuzzification is used for only nonlinear distribution matrices. In fact the proposed method covers nonlinear systems could not transform to linear T-S model. This paper studies the problem of robust fault diagnosis based on two fuzzy nonlinear observers, the first one is a fuzzy nonlinear unknown input observer (FNUIO) and the other is a fuzzy nonlinear Luenberger observer (FNLO). This approach decouples the faulty subsystem from the rest of the system through a series of transformations. Then, the objective is to design FNUIO to guarantee the asymptotic stability of the error dynamic using the Lyapunov method; meanwhile, FNLO is designed for faulty subsystem to generate fuzzy residual signal based on a quadratic Lyapunov function and some matrices inequality convexification techniques. FNUIO affects only the fault free subsystem and completely removes any unknown inputs such as disturbances when residual signal is generated by FNLO is affected by component or sensor fault. This novelty and using nonlinear system in T-S model make the proposed method extremely effective from last decade literature. Sufficient conditions are established in order to guarantee the convergence of the state estimation error. Thus, a residual generator is determined on the basis of LMI conditions such that the estimation error is completely sensitive to fault vector and insensitive to the unknown inputs. Finally, an numerical example is given to show the highly effectiveness of the proposed fault diagnosis scheme.     

Robust nonlinear internal model control of stable Wiener systems

Many process systems can be modeled as a stable Wiener system, which is a stable linear system followed by a static nonlinearity. A nonlinear control design procedure is presented that provides robustness to uncertainties while being applicable to systems with unstable zero dynamics, unmeasured states, disturbances, and measurement noise. The design procedure combines nonlinear internal model control with linear matrix inequality feasibility or optimization problems, such that all robust stability and performance criteria are computable in polynomial-time using readily available software. Application to a pH neutralization case study demonstrates the importance of taking uncertainty into account during the design of controllers for Wiener systems. The approach is generalizable to Hammerstein and sandwich systems, whether well- or poorly conditioned, and to systems with actuator constraints.     

Output feedback model predictive control for LPV systems based on quasi-min–max algorithm

This paper proposes a robust output feedback model predictive control (MPC) scheme for linear parameter varying (LPV) systems based on a quasi-min–max algorithm. This approach involves an off-line design of a robust state observer for LPV systems using linear matrix inequality (LMI) and an on-line robust output feedback MPC algorithm using the estimated state. The proposed MPC method for LPV systems is applicable for a variety of systems with constraints and guarantees the robust stability of the output feedback systems. A numerical example for an LPV system subject to input constraints is given to demonstrate its effectiveness.     

The Structural Robustness of the Induction Motor Stator Currents Subsystem

Stator‐currents control is essential for several high‐performance induction motor control schemes such as field oriented control. There are numerous reports dealing with sophisticated control schemes for this subsystem. However, classical linear controllers remain widely used due to their experimental success and simplicity. Considering that the induction motor stator currents subsystem is normally represented by a fifth order non‐linear multivariable model, it is remarkable that simple fixed linear controllers, such as typical proportional integral schemes, are able to provide adequate robustness and performance in practice. In fact, it is normally assumed that this subsystem is “easy” to control, and the difficulties are mostly technical. Moreover, it is common practice to consider a stable first order linear single input single output (SISO) system as a design model. On the other hand, it is widely known that stable and minimum phase uncertain SISO systems are also “easy” to control. In this article it is formally demonstrated that the stator currents subsystem of the induction motor is the multivariable equivalent of such SISO systems. That is, it is formally demonstrated that this process is “easy” to control. This result may assist with better induction motor control and may serve as an example of the evaluation of similar multivariable systems. Real time experimental results are included.     

Fuzzy linear pulse-transfer function-based sliding-mode control fornonlinear discrete-time systems

In this paper, a nonlinear discrete-time system in the presence of input disturbance and measurement noise is approximated by N subsystems described by the linear pulse-transfer functions. Although the input disturbance and the measurement noise are unknown, they are modeled as known pulse-transfer functions. The approximation error between the nonlinear discrete-time system and the fuzzy linear pulse-transfer function system is represented by the linear time-invariant dynamic system in every subsystem, whose degree can be larger than that of the corresponding subsystem. Besides the input disturbance and the measurement noise, uncertainties are caused by the approximation error of the fuzzy-model and the interconnected dynamics resulting from the other subsystems. Owing to the presence of input disturbance, measurement noise, or uncertainties, a disadvantageous response occurs. Based on Lyapunov redesign, the switching control in every subsystem is designed to reinforce the system performance. Due to the time-invariant feature for a constant reference input, the operating point can approach the sliding surface in the manner of finite-time steps. The stability of the overall system is verified by Lyapunov stability theory     

SECOND‐ORDER NONSINGULAR TERMINAL SLIDING MODE DECOMPOSED CONTROL OF UNCERTAIN MULTIVARIABLE SYSTEMS

This paper proposes a second‐order nonsingular terminal sliding mode decomposed control method for multivariable linear systems with internal parameter uncertainties and external disturbances. First, the systems are converted into the block controllable form, consisting of an input‐output subsystem and a stable internal dynamic subsystem. A special second‐order non‐singular terminal sliding mode is proposed for the input‐output subsystem. The control law is designed to drive the states of the input‐output subsystem to converge to the equilibrium point asymptotically. Then the states of the stable zero‐dynamics of the system converge to the equilibrium point asymptotically. The method proposed in the paper has advantages for higher‐dimensional multivariable systems, in the sense that it simplifies the design and makes it possible to realize a robust decomposed control. Meanwhile, because of the adoption of the second‐order sliding mode, the control signal is continuous. Simulation results are presented to validate the design.