不确定时滞系统的基于观察器的稳定化控制器设计

本文研究不确定时滞系统的鲁棒镇定问题,在系统状态不能直接测量的情况下,提出了基于观察器的稳定化控制器设计方法.     

双馈感应发电机的微分几何变结构控制方法研究

为了实现变速恒频控制的双馈感应发电机风电系统的良好控制性能,从电机学基本原理出发,建立双馈感应发电机的动态数学模型.根据其数学模型的非线性特性,提出了微分几何变结构控制方法,运用微分几何精确线性化理论,把非线性系统转化成了一个线性系统.在此基础上应用非线性变结构控制理论进行设计控制器.用Matlab对系统进行仿真,结果表明:该控制方法能实现系统的有功功率和无功功率的解耦,进一步验证该控制策略的合理性及控制器设计的有效性和可行性.     

模糊自适应PID控制器的设计及应用研究

为实现多输入多输出、高度非线性、不稳定的倒立摆系统平衡稳定控制,将倒立摆系统的非线性模型进行近似线性化处理,获得系统在平衡点附近的线性化模型,利用牛顿一欧拉方法建立直线型一级倒立摆系统的数学模型.利用模糊控制技术对直线型一级倒立摆系统设计模糊自适应PID控制器,此整定方法有效地把专家经验应用于PID参致调节中,控制器集模糊控制器和PID控制器的优点于一身.仿真表明设计的控制器是有效的,该整定策略是实现自动工业控制器的一种简单、实用的方法.     

全电式炮控非线性系统模型预测控制器设计

全电式炮控系统是一个强非线性的复杂控制对象,由于存在摩擦力矩、参数摄动等不确定内部因素,常规控制算法难于对其实现精确控制。针对提高全电式炮控非线性系统控制的性能,本文提出了一种基于模型预测控制的全电式炮控系统控制方法,并把菌群优化算法应用到非线性系统模型预测控制器设计。通过对控制目标的分析,将输入受限的非线性预测控制器设计问题转化为控制器参数寻优问题,并利用菌群优化算法来对参数进行寻优,提高了系统控制性能。文中对算法的稳定性进行了分析,并通过全电式炮控非线性系统实例对算法进行了验证。结果证明了算法的有效性和可行性,为全电式炮控非线性系统模型预测控制器的设计提供了一种有效的途径。     

倒立摆的PID神经元网络的间接控制系统的研究

摆系统是一个典型的强耦合、非线性、高阶次的不稳定系统.由于摆系统的数学模型是在忽略了次要因素的基础上得出来的,而实际上是一个非线性的系统,当系统受到外部的干扰时,这些次要因素的影响比较突出.实验采用PID神经元,设计一个神经网络间接自适应控制系统,首先用一个神经网络对摆系统模型进行辨识,辨识完成后,辨识模型的权值与隐层积分元的数值传递给具有同样结构的PID神经元的神经网络控制器,对倒立摆进行自适应控制.最后根据以上算法,采用6.0编写控制程序,实现对平面一级摆系统的实时控制.     

一类机器人操作手的鲁棒最优控制

在这篇文章中我们扩展了[1]中的理论框架,描述了非线性最优鲁棒控制问题;特别地,我们把非线性鲁棒控制问题转化为最优控制问题.具体是根据系统的不确定性,通过适当的选择目标函数使最优控制的解满足鲁棒控制问题的解.这篇文章的主要优点在于为实现非线性控制器的设计提供了很好的方法它不仅保证了在不确定性存在时的鲁棒稳定性,而且使问题的求解变得简单.     

液压位置系统P.I最优控制器设计

本文提出了用单纯形法设计液压位置系统P.I最优控制器参数。采用此法,作者分别对线性和非线性液压位置系统进行了P.I控制器参数设计。仿真结果表明：这种方法设计的液压线性和非线性位置系统控制器参数是有效的。该法可用干其它液压系统控制器参数设计。     

基于自适应PID控制器的平面一级倒立摆的研究

倒立摆是一个强耦合、严重不稳定的系统,其背景来源于火箭发射等课题.在该系统中,PID控制器常常被采用.由于该系统在建立数学模型时次要的因素被忽略了,实际上是一个非线性系统;为了提高系统的控制性能,根据计算智能逼近非线性系统的功能,设计一个RBF神经网络控制系统,实现对常规PID控制器的参数进行自适应整定.最后使用BC++编写系统的控制程序,通过实物控制验证基于RBF神经网络的PID控制器参数的自适应整定的系统具有较好的瞬态性和鲁棒性.     

基于遗传算法的风/光互补发电系统的优化设计

风能和太阳能是丰富清洁的可再生能源,风力发电和太阳能光伏发电是重要的后续能源,并将为能源结构的调整和环境的保护做出巨大贡献.本文设计了一种风/光互补发电系统,由于风/光互补发电系统具有强非线性,很难建立精确的数学模型,因此用传统的方法对其进行优化设计很难达到理想的效果.本文设计了一种遗传算法,并用该算法对一个已经运行的风/光互补发电系统进行了优化设计,结果证明了遗传算法在风/光互补发电系统优化设计中的有效性.最后,提出风/光互补发电系统的运行也具有非线性特点,用遗传算法对其进行动态管理是今后值得探讨的一个问题.     

基于PID参数自整定的双容系统抗扰控制

针对双容液面调节系统的非线性、参数时变的特点,基于现代控制理论设计了带扰动观测器的模糊自整定控制器.把这种控制器应用在双容液面调节系统中,既实现了PID控制器参数的在线自整定,又增强了系统的抗扰能力.仿真试验表明,这种方案极大地提高了被控对象的静态和动态性能,对参数时变的适应能力强,鲁棒性好.该方案同样可以用于其他非线性、时变系统.     

基于干扰观测器的时滞非线性切换系统滑模控制方法

为实现更加精准的时滞非线性切换系统滑模控制,应用干扰观测器设计一种新的系统滑模控制方法。构建时滞非线性切换系统模型,针对系统在发生结构变化时会产生复合干扰变化的情况,设计了一种非线性切换干扰观测器,实施系统不连续干扰的估计。通过 Backstepping 方法结合干扰观测器,设计一种切换滑模控制器,依据标量非线性特性打造一个滑模面,通过滑模控制器算法使时滞非线性切换系统能够满足滑模面的实际可达性条件,完成切换滑模控制器设计,实现系统的滑模控制。对设计的滑模控制方法进行测试,实验中选择的时滞非线性切换系统为一种变后掠翼 NSV 。实验结果表明,该设计方法能够实现较为准确地切入信号跟踪,表现出了很好的切换复合干扰估计性能。     

基于极小化条件数的电液伺服控制系统观测器设计

设计良好的状态观测器是电液伺服控制系统故障诊断研究的关键。本文提出一种电液伺服控制系统观测器设计的新方法。针对所构建的位置伺服系统,建立系统的离散非线性模型,绘制系统的离散观测器结构,推导极小化条件数梯度下降法的算法流程并运用该算法优化计算观测器的增益矩阵和Lipschitz常数,为系统设计一个Lipschitz非线性离散观测器。将该观测器与现有的电液伺服控制系统观测器进行比较,发现本文所采用的方法具有计算简单、设计优良等特点。     

A novel extended state observer

A novel extended state observer, which feeds back the output estimation error via both nonlinear and switching terms, is put forward for the first time in this paper. No longer neglecting the lumped uncertainty׳s first time derivative, the problem of disturbance observer design is transformed into the problem of state observer design in the presence of external disturbance. The switching term of the output estimation error is employed to counteract the adverse effect of external disturbance. The newly developed extended state observer provides an attractive solution to the issue of high precision motion control system. Both numerical simulation and experimentation on a speed turntable with temperature box are implemented to verify the performance of the proposed newly developed extended state observer.     

Observer-based adaptive fuzzy-neural control for a class of uncertain nonlinear systems with unknown dead-zone input

Based on the universal approximation property of the fuzzy-neural networks, an adaptive fuzzy-neural observer design algorithm is studied for a class of nonlinear SISO systems with both a completely unknown function and an unknown dead-zone input. The fuzzy-neural networks are used to approximate the unknown nonlinear function. Because it is assumed that the system states are unmeasured, an observer needs to be designed to estimate those unmeasured states. In the previous works with the observer design based on the universal approximator, when the dead-zone input appears it is ignored and the stability of the closed-loop system will be affected. In this paper, the proposed algorithm overcomes the affections of dead-zone input for the stability of the systems. Moreover, the dead-zone parameters are assumed to be unknown and will be adjusted adaptively as well as the sign function being introduced to compensate the dead-zone. With the aid of the Lyapunov analysis method, the stability of the closed-loop system is proven. A simulation example is provided to illustrate the feasibility of the control algorithm presented in this paper.     

Adaptive observer design for nonlinear systems using generalized nonlinear observer canonical form

In this paper, we present an adaptive observer for nonlinear systems that include unknown constant parameters and are not necessarily observable. Sufficient conditions are given for a nonlinear system to be transformed by state-space change of coordinates into an adaptive observer canonical form. Once a nonlinear system is transformed into the proposed adaptive observer canonical form, an adaptive observer can be designed under the assumption that a certain system is strictly positive real. An illustrative example is included to show the effectiveness of the proposed method.     

Quasi-minimal active disturbance rejection control of MIMO perturbed linear systems based on differential neural networks and the attractive ellipsoid method

This study addresses the problem of designing an output-based controller to stabilize multi-input multi-output (MIMO) systems in the presence of parametric disturbances as well as uncertainties in the state model and output noise measurements. The controller design includes a linear state transformation which separates uncertainties matched to the control input and the unmatched ones. A differential neural network (DNN) observer produces a nonlinear approximation of the matched perturbation and the unknown states simultaneously in the transformed coordinates. This study proposes the use of the Attractive Ellipsoid Method (AEM) to optimize the gains of the controller and the gain observer in the DNN structure. As a consequence, the obtained control input minimizes the convergence zone for the estimation error. Moreover, the control design uses the estimated disturbance provided by the DNN to obtain a better performance in the stabilization task in comparison with a quasi-minimal output feedback controller based on a Luenberger observer and a sliding mode controller. Numerical results pointed out the advantages obtained by the nonlinear control based on the DNN observer. The first example deals with the stabilization of an academic linear MIMO perturbed system and the second example stabilizes the trajectories of a DC-motor into a predefined operation point.     

Robust fault diagnosis algorithm for a class of Lipschitz system with unknown exogenous disturbances

A robust fault diagnosis scheme for nonlinear system is designed and a novel algorithm for a robust fault diagnosis observer is proposed in this paper. The robustness performance index is defined to ensure the robustness of the observer designed. The norm of most unknown input disturbances are assumed bounded at present. However, some systems are proved unstable under traditional assumptions. In the proposed algorithm, the external disturbances constraint condition that satisfies the system stability is derived based on Gronwall Lemma. The design procedure of the observer proposed is implemented by pole assignment. Adaptive threshold is generated using the designed observer. Simulations are performed on continuous stirred tank reactor (CSTR) and the results show the effectiveness and superiority of the proposed algorithm.     

Sliding mode controller with sliding perturbation observer based on gain optimization using genetic algorithm

The Stewart platform manipulator is a closed-kinematics chain robot manipulator that is capable of providing high structural rigidity and positional accuracy. However, this is a complex and nonlinear system, so the control performance of the system is not so good. In this paper, a new robust motion control algorithm is proposed. The algorithm uses partial state feedback for a class of nonlinear systems with modeling uncertainties and external disturbances. The major contribution is the design of a robust observer for the state and the perturbation of the Stewart platform, which is combined with a variable structure controller (VSC). The combination of controller and observer provides the robust routine called sliding mode control with sliding perturbation observer (SMCSPO). The optimal gains of SMCSPO, which is determined by nominal eigenvalues, are easily obtained by genetic algorithm. The proposed fitness function that evaluates the gain optimization is to put sliding function. The control performance of the proposed algorithm is evaluated by the simulation and experiment to apply to the Stewart platform. The results showed high accuracy and good performance.     

Disturbance observer-based backstepping sliding mode fault-tolerant control for the hydro-turbine governing system with dead-zone input

This paper investigates a backstepping sliding mode fault-tolerant tracking control problem for a hydro-turbine governing system with consideration of external disturbances, actuator faults and dead-zone input. To reduce the effects of the unknown random disturbances, the nonlinear disturbance observer is designed to identify and estimate the disturbance term. To drastically decrease the complexity of stability functions selection and controller design, the recursive processes of the backstepping technique are employed. Additionally, based on the nonlinear disturbance observer and the backstepping technique, the sliding mode fault-tolerant tracking control approach is developed for the hydro-turbine governing system (HTGS). The stability of HTGS is rigorously demonstrated through Lyapunov analysis which is capable to satisfy a tracking control performance. Finally, comprehensive simulation results are presented to illustrate the effectiveness and superiority of the proposed control scheme.