Improved Controller Design with Two Gain-scaling Factors for Global Asymptotic Stabilization of Nonlinear Systems via Matrix Inequality Approach

In this paper, we consider a problem of global asymptotic stabilization for nonlinear systems with the perturbed nonlinearity. We provide a stabilizing controller with two gain-scaling factors and a new controller design method with matrix inequality approach. In particular, we provide a new procedure for selecting gain-scaling factors which are associated with stabilizing the closed-loop system. As a result, our proposed control method widens the class of considered nonlinear systems and yields better control performance over the existing methods. Via several comparison examples, we illustrate the improved features of the proposed control method over the existing ones.

     

Global regulation of a class of feedforward and non-feedforward nonlinear systems with a delay in the input

In this paper, we propose a new state feedback controller using dynamic gain for input-delayed systems with high-order nonlinearity terms in both feedforward and non-feedforward forms. The controller design is based on a reduction method to remove the input delay and a gain scaling technique involving appropriate powers of high-order nonlinearity. As a result, more generalized systems containing feedforward and nonfeedforward terms with an input delay are regulated when the proposed power order condition of the nonlinear function is satisfied. An example is given to show the generality of our result over existing results.     

On asymptotic stabilisation of a chain of integrators with nonlinearity and an uncertain input delay by output feedback

In this paper, we provide an output feedback solution over one given by Choi and Lim [Systems & Control Letters, 59(6), 374–379 (2010)] under more generalised system set-up. More specifically, we consider a stabilisation problem of a chain of integrators that has nonlinearity and an uncertain delay in the input by output feedback. The nonlinearity is classified into four types. Then, we propose a memoryless output feedback controller which contains a gain-scaling factor to adjust controller gains depending on the given nonlinearity type. Our stability analysis shows that the controlled system has unique stabilisation result associated with each type of nonlinearity. Our result provides a new aspect to the stabilisation problem of nonlinear time-delay systems and broadens the existing control results of time-delay systems. Two examples are given for illustration.     

Stable Switching PD Control Using Hunt-Crossley Model for a Press Platform

This study proposes a switching proportional-derivative (PD) controller for a press platform for inspecting the surfaces of a wind turbine blade. We use the Hunt-Crossley model to represent the probe shape or the nonlinearity of the inspection platform. This model consists of a nonlinear spring and a damper; therefore, it is more accurate than linear spring-damper models. We prove the global asymptotic stability of a PD force feedback controller and a PD position feedback (force) controller. However, both the controllers suffer from implementation-related problems. Specifically, the PD force feedback controller makes the impact force large, and the PD position feedback controller cannot easily measure small position changes when the platform contacts the surface. These problems of each controller are solved by switching the two controllers. The PD force feedback control and PD position feedback control are used when the platform is in the contact and noncontact states, respectively. We prove that the proposed switching PD force/position feedback controller is globally asymptotically stable. Further, simulations show satisfactory performance resulting from stable switching between the two control schemes.

     

一种非线性反馈反步递推算法的线性弱化

为了解决反步递推算法应用于船舶航向控制中输出能量大且非线性部分难以对消的实际问题, 首先对原有的反步递推算法进行线性弱化, 然后利用正弦函数驱动的非线性反馈控制技术代替原有的线性误差反馈. 理论分析表明, 该算法能够保证控制效果相当或更优的前提下, 降低控制能量. 以“育龙” 轮为载体开展仿真实验, 结果表明所提出的新型非线性反馈控制律在鲁棒性、控制能量需求方面具有明显的优势.

     

Adaptive non-predictor control of lower triangular uncertain nonlinear systems with an unknown time-varying delay in the input

In this paper, we consider a control problem for a class of uncertain nonlinear systems in which there exists an unknown time-varying delay in the input and lower triangular nonlinearities. Usually, in the existing results, input delays have been coupled with feedforward (or upper triangular) nonlinearities; in other words, the combination of lower triangular nonlinearities and input delay has been rare. Motivated by the existing controller for input-delayed chain of integrators with nonlinearity, we show that the control of input-delayed nonlinear systems with two particular types of lower triangular nonlinearities can be done. As a control solution, we propose a newly designed feedback controller whose main features are its dynamic gain and non-predictor approach. Three examples are given for illustration.     

Tracking and robustness analysis for controlled microelectromechanical relays

The feedback control problem for microelectromechanical (MEM) relays is complicated by a quadratic nonlinearity in the dynamic model. We show that this nonlinearity imposes constraints on the reference trajectories that can be tracked and on the global convergence rate of the tracking error. Using a dynamic model that is applicable to both electrostatic and electromagnetic MEM relays, we introduce a new class of nonlinear tracking controls. In particular, we use Lyapunov theory to construct a state feedback that yields uniform global asymptotic stability and arbitrarily fast local exponential convergence of the tracking error. We then show how our control can be redesigned with partial‐state feedback under the assumption that only the movable electrode position and the electrical state (i.e. charge or flux) are fed back. Finally, we utilize input‐to‐state stability theory to quantify the robustness of our state feedback controller to parametric uncertainties. Our simulation results illustrate the good stability and tracking performance of the proposed control. They also illustrate how to craft a reference trajectory that satisfies the aforementioned constraints while being compatible with a typical relay operation. Copyright © 2007 John Wiley & Sons, Ltd.     

基于控制系统直接方法的非线性系统预见控制器设计

针对一类非线性离散时间系统给出最优预见控制器设计方法. 首先运用非线性控制系统直接控制方法的思想, 将非线性反馈部分作为形式输入, 使得系统成为“形式上”的线性系统; 然后, 针对该线性系统, 利用最优预见控制的基本方法设计最优预见控制器; 最后, 利用形式输入与实际输入的关系得到非线性离散时间系统的最优预见控制器. 证明了如果形式线性系统满足一定的可镇定和可检测条件, 则闭环系统是渐近稳定的. 数值仿真结果表明了控制器的有效性.

     

基于分段线性化Hammerstein模型的自校正控制器设计

许多实际系统可以表示为不连续非线性块状结构模型,其不连续非线性部分常采用符号函数参数化,该处理方法适用于递推参数辨识,但自适应控制器的设计较为困难。鉴于此,针对一类含有不连续非线性环节的Hammerstein模型,采用一系列线性分段函数参数化不连续非线性环节,提出自校正控制方法。根据线性分段函数的逆函数特性,求解自适应控制律。理论分析证明了闭环系统的稳定性,仿真结果验证了所提出方法的有效性。     

一类MIMO非线性大系统基于H∞ 跟踪的分散自适应输出反馈模糊控制

针对一类状态不可测的MIMO不确定非线性大系统,提出一种基于H∞跟踪的分散自适应输出反馈模糊控制器.主要工作有:1)通过对观测误差向量进行滤波来确保严格正实条件成立,使得提出的反馈与自适应机制可以执行;2)利用模糊系统提出一种适用于一般非线性大系统基于H∞跟踪的分散自适应模糊控制方案;3)在统一的框架下处理了控制器奇异性.闭环大系统被证明足稳定的,且输出误差具有H∞跟踪性能.仿真结果验证了控制器设计的有效性.     

带执行器饱和的N 连杆机械臂输出反馈动态面控制

针对带执行器饱和的多关节刚性机械臂系统,提出一种基于RBF神经网络补偿的输出反馈动态面控制.通过观测器实现角速度的观测,采用RBF网络实现执行器饱和的补偿;通过Lyapunov方法证明闭环系统的稳定性,实现高精度的角度和角速度跟踪.仿真结果表明,所提出的方法能够有效补偿系统存在的执行器饱和,显著减小跟踪误差,并且对于外界干扰具有一定的鲁棒性.     

不确定非线性系统的鲁棒自适应输出反馈控制

基于自适应非线性阻尼,提出一种鲁棒自适应输出反馈控制方法。该方法适用于带有未建模动态、未知非线性、有界扰动、未知非线性参数和不确定控制系数的多输入多输出非线性系统。理论证明,在一定的假设条件下,该方法能保证闭环系统所有动态信号有界;不论有多少不确定非线性参数、多高阶的非线性系统,只需要一个自适应控制参数和观察参数;而且通过选择适当的控制器和观测器参数,能使控制误差和估计误差达到任意小。仿真结果表明了所提出方法的有效性。     

Output feedback regulation of a class of high‐order feedforward nonlinear systems with unknown time‐varying delay in the input under measurement sensitivity

An output feedback regulation problem is considered for a class of high‐order feedforward nonlinear systems with delay in the input under measurement sensitivity. The key features are that the considered systems have uncertain high‐order feedforward nonlinearity and unknown time‐varying delay in the input. Then, the controller is supposed to be engaged where the output feedback information is distorted by measurement sensitivity. Our proposed controller has two gains—fixed and adaptive gains. The fixed gain is first designed to compensate for measurement sensitivity, and the adaptive gain is next utilized to dominate both unknown input delay and uncertain high‐order feedforward nonlinearity. Simulation examples are given to highlight the advantage of our control scheme.     

基于动态面的高超声速飞行器模糊自适应非线性控制

针对高超声速飞行器模型非线性、多变量和参数不确定特性, 并考虑控制增益变化可能导致控制奇异值问 题, 提出一种基于动态面的模糊自适应非线性控制方法. 根据动态面和动态逆策略, 分别设计了高度和速度跟踪控制 器. 利用模糊自适应方法在线逼近不确定函数项, 并采用Nussbaum 增益技术抑制虚拟控制增益不确定影响, 以减少 在线学习量, 保证系统的半全局一致最终有界性. 仿真结果表明, 所提出的方法可实现飞行器对高度和速度的准确跟 踪控制.

     

Stable auto-tuning of adaptive fuzzy/neural controllers for nonlinear discrete-time systems

In direct adaptive control, the adaptation mechanism attempts to adjust a parameterized nonlinear controller to approximate an ideal controller. In the indirect case, however, we approximate parts of the plant dynamics that are used by a feedback controller to cancel the system nonlinearities. In both cases, "approximators" such as linear mappings, polynomials, fuzzy systems, or neural networks can be used as either the parameterized nonlinear controller or identifier model. In this paper, we present algorithms to tune some of the parameters (e.g., the adaptation gain and the direction of descent) for a gradient-based approximator parameter update law used for a class of nonlinear discrete-time systems in both direct and indirect cases. In our proposed algorithms, the adaptation gain and the direction of descent are obtained by minimizing the instantaneous control energy. We will show that updating the adaptation gain can be viewed as a special case of updating the direction of descent. We will also compare the direct and indirect adaptive control schemes and illustrate their performance via a simple surge tank example.     

Output feedback regulation of upper triangular nonlinear systems with uncertain time‐varying delays in states and input

In this paper, an output feedback controller is studied to regulate a class of upper triangular nonlinear systems with uncertain time‐varying delays. The key features of our considered system are that there are uncertain time‐varying delays in both states and input and the high‐order nonlinearity is in a more relaxed form over the previous results. Theoretical analysis and numerical example are presented to show the benefits of our controller. Copyright © 2017 John Wiley & Sons, Ltd.     

Two‐stage stability control for strict‐feedback systems with input delays and input nonlinear uncertainties

In this paper, a two‐stage control procedure is proposed for stabilization of a class of strict‐feedback systems with unknown constant time delays and nonlinear uncertainties in the input. A nominal controller is first designed to compensate input time delays without considering input nonlinear uncertainties. Extended from backstepping algorithm, input delay compensation is realized by means of predicted states that are computed through integration of cascaded system dynamics, making the nominal closed‐loop system asymptotically stable. Based on the nominal controller presented for the input delay system, a multi‐timescale system is subsequently developed to estimate the unknown input nonlinearity and make the estimate approach the nominal control input as fast as possible. It is proved that the proposed control scheme can make states of the strict‐feedback systems converge to zero and all the signals of the closed‐loop systems are guaranteed to be bounded in the presence of input time delays and nonlinear uncertainties. Simulation verification is carried out to illuminate the effectiveness of the proposed control approach.     

Adaptive finite-time control of a class of non-triangular nonlinear systems with input saturation

This paper studies the problem of adaptive neural network finite-time control for a class of non-triangular nonlinear systems with input saturation. Under the assumption that the nonlinearities have strict increasing smooth bounding functions, the backstepping technique can be used to design the state feedback controller and adaptive laws. Neural networks are adopted to approximate some unknown nonlinear functions. With the help of the finite-time Lyapunov stability theorem, it can be proved that the state of the closed-loop system can converge to an arbitrarily small neighborhood of the origin in a finite time. Finally, a numerical simulation example is given to show the effectiveness of the proposed design method.

     

用虚拟目标值和支持向量机直接设计非线性控制器

对于复杂被控对象, 通常采用间接方法设计控制系统. 由于必须首先辨识对象模型, 因此这类方法往往耗时较多. 本文基于虚拟目标值反馈调整(VRFT)方法的思想, 利用支持向量机(SVM), 给出一种非线性控制器直接设计方法. 论文首先分析了VRFT方法与内模控制的关系, 接着给出了基于虚拟目标值(VR)和SVM的非线性控制器的结构和设计步骤. 仿真结果表明, 该方法具有良好的处理非线性和噪声的能力, 并且能消除稳态误差. 与经典基于神经网络(NN)的间接模型参考控制方法相比, 计算量大大降低.     

Global output‐feedback stabilization for a class of stochastic nonlinear systems via sampled‐data control

In this paper, the global sampled‐data output‐feedback stabilization problem is considered for a class of stochastic nonlinear systems. First, based on output‐feedback domination technique and emulation approach, a systematic design procedure for sampled‐data output‐feedback controller is proposed for a class of stochastic lower‐triangular nonlinear systems. It is proved that the proposed sampled‐data output‐feedback controller will stabilize the given stochastic nonlinear system in the sense of mean square exponential stability. Because of the domination nature of the proposed control approach, it is shown that the proposed control approach can also be used to handle the global sampled‐data output‐feedback stabilization problems for a more general class of stochastic non‐triangular nonlinear systems. Finally, simulation examples are given to demonstrate the effectiveness of the proposed method. Copyright © 2017 John Wiley & Sons, Ltd.