一类非线性系统的自适应观测器设计

在故障诊断应用中, 状态方程中的未知参数和输出方程中的未知参数分别表征执行机构故障和传感器故障, 所以研究状态方程和输出方程同时含有未知参数的自适应观测器有着实际的应用意义. 本文基于高增益观测器和自适应估计理论, 针对状态方程和输出方程同时含有未知参数的一类一致可观的非线性系统, 用构造性方法设计了一种联合估计状态和未知参数的自适应观测器. 该自适应观测器的参数估计采用时变增益矩阵, 结构形式及参数设置简单. 给出了使该自适应观测器满足全局指数收敛性的持续激励条件, 并在理论上简洁地证明了该自适应观测器的全局指数收敛性. 数值仿真结果表明该自适应观测器具有良好的快速收敛性、跟踪性等期望性能.     

Adaptive observer and nonlinear control strategy for chemical reactors via neural networks

An adaptive observer and nonlinear feedback control strategy with constraints on control action are developed by using a supervised learning rule of a neural network and the theory of functional-link networks. The convergence of the adaptive observer and the stability of the control system are proven. They are applied to the control of an exothermic stirred-tank reactor. It is shown that an adaptive observer for concentration can be constructed for a reaction system when only temperature measurements are available on line. An adaptive observer is used to identify the pre-exponential Arrhenius constant and to provide on line estimation of the unmeasured reactant concentration for a nonlinear state-feedback controller. Simulations show that the combined observer/controller provides satisfactory closed-loop behaviour, fast responses and strong robustness. Estimated and actual concentration are in good agreement. A nonlinear feedback controller can provide effective feedback control over a wide range of operating conditions.     

Algebraic separation in realizing a linear state feedback control law by means of an adaptive observer

Based on a novel adaptive observer, which does not require signal boundedness in its stability proof, an algebraic separation property of linear state feedback control and adaptive state observation is established. This means, whenever a linear, stabilizing state feedback control law is realized with the state replaced by the state estimate of the given stable adaptive observer, then the resulting nonlinear control system is also globally asymptotically Lyapunov stable with respect to the initial state and parameter observation error of the adaptive observer. In particular, no assumptions on the system dynamics nor on the speed of the adaptation are made.     

A performance comparison between two design techniques for non-linear output feedback control

For a system possessing a non-linear output feedback normal form, an observer backstepping design is compared to a high gain observer design with respect to non-singular performance cost functional. If the initial error between the initial condition of the state and the initial condition of the observer is large, the high gain observer design is shown to have better performance than the observer backstepping design. An output feedback system with parametric uncertainty is then considered. It is shown that if an a priori estimate for the bound of the uncertain parameter is conservative, then an adaptive observer backstepping design has better performance than the adaptive high gain observer design.     

控压钻井系统井下不确定参数与未知状态估计

针对控压钻井过程中井下信息获取难度大的问题, 本文设计了一种可以同时估计井下不确定参数与未知 状态的自适应观测器. 首先, 针对未知状态中含有不确定参数的控压钻井系统, 提出了一种可解耦不确定参数的观 测器匹配条件. 其次, 根据此观测器匹配条件设计自适应观测器估计井下参数与状态, 并通过李雅普诺夫稳定性理 论证明了估计误差的渐近稳定性. 最后, 基于线性矩阵不等式方法求解观测器增益, 提高观测器的鲁棒性. 仿真分析 表明: 与已有针对控压钻井系统设计的自适应观测器相比, 本文所设计的观测器对井下环空摩擦积分和井底流量 的估计具有更好的收敛性以及鲁棒性.     

Adaptive output feedback control of a class of non-linear systems using neural networks

This paper presents tools for the design of a neural network based adaptive output feedback controller for a class of partially or completely unknown non-linear multi-input multi-output systems without zero dynamics. Each of the outputs is assumed to have relative degree less or equal to 2. A neural network based adaptive observer is designed to estimate the derivatives of the outputs. Subsequently, the adaptive observer is integrated into a neural network based adaptive controller architecture. Conditions are derived which guarantee the ultimate boundedness of all the errors in the closed loop system. Stability analysis reveals simultaneous learning rules for both the adaptive neural network observer and adaptive neural network controller. The design approach is illustrated using a fourth order two-input two-output example, in which each output has relative degree two.     

Neural network-based sliding mode adaptive control for robot manipulators

This paper addresses the robust trajectory tracking problem for a robot manipulator in the presence of uncertainties and disturbances. First, a neural network-based sliding mode adaptive control (NNSMAC), which is a combination of sliding mode technique, neural network (NN) approximation and adaptive technique, is designed to ensure trajectory tracking by the robot manipulator. It is shown using the Lyapunov theory that the tracking error asymptotically converge to zero. However, the assumption on the availability of the robot manipulator dynamics is not always practical. So, an NN-based adaptive observer is designed to estimate the velocities of the links. Next, based on the observer, a neural network-based sliding mode adaptive output feedback control (NNSMAOFC) is designed. Then it is shown by the Lyapunov theory that the trajectory tracking errors, the observer estimation errors asymptotically converge to zero. The effectiveness of the designed NNSMAC, the NN-based adaptive observer and the NNSMAOFC is illustrated by simulations.     

Neural network-based adaptive controller design of roboticmanipulators with an observer

A neural network (NN)-based adaptive controller with an observer is proposed for the trajectory tracking of robotic manipulators with unknown dynamics nonlinearities. It is assumed that the robotic manipulator has only joint angle position measurements. A linear observer is used to estimate the robot joint angle velocity, while NNs are employed to further improve the control performance of the controlled system through approximating the modified robot dynamics function. The adaptive controller for robots with an observer can guarantee the uniform ultimate bounds of the tracking errors and the observer errors as well as the bounds of the NN weights. For performance comparisons, the conventional adaptive algorithm with an observer using linearity in parameters of the robot dynamics is also developed in the same control framework as the NN approach for online approximating unknown nonlinearities of the robot dynamics. Main theoretical results for designing such an observer-based adaptive controller with the NN approach using multilayer NNs with sigmoidal activation functions, as well as with the conventional adaptive approach using linearity in parameters of the robot dynamics are given. The performance comparisons between the NN approach and the conventional adaptation approach with an observer is carried out to show the advantages of the proposed control approaches through simulation studies     

不确定随机非线性系统自适应观测器设计

考虑带非参数不确定项的随机非线性系统自适应观测器设计问题.不同于已有结果,系统的不确定项无需满足Lipschitz连续性条件,也不必要仅仅是系统输出的函数.通过设计一个带参数自适应律的非线性观测器来重构系统状态,该观测器结构简单目易于实现.应用Lyapunov稳定性理论和随机微分理论证明该观测器是最终有界的,并且它的界可以通过选取适当的参数进行调节.最后,数值仿真结果表明了该观测器的有效性.     

基于观测器的一类非线性系统的自适应模糊控制

针对一类有界的不确定非线性系统设计了模糊观测器和自适应控制器.该方法不需要系统状态完全可测的条件,而是通过模糊观测器估计系统的状态变量并且能保证观测误差是一致最终有界的.该自适应控制器取得了良好的控制效果并且保证了跟踪误差的一致最终有界性.仿真结果表明了本文所提出的方法有效性.     

基于H∞观测器原理的模糊自适应控制器设计

针对一类非线性系统提出一种利用观测器原理来求解干扰抑制项和控制器参数的新 型模糊自适应控制方法.通过寻求线性矩阵不等式(LMI)的可行解来得到具有H∞跟踪性能的 模糊自适应观测器.并证明了如果该观测器具有H∞跟踪性能,则由该观测器参数构成的控制 器,可以保证闭环系统稳定.     

基于扩张状态观测器的发动机转速双闭环自适应控制系统设计

为较好控制发动机设备的实时转速水平,使其在不同压力水平下均能呈现理想化工作状态,设计基于扩张状态观测器的发动机转速双闭环自适应控制系统。根据双闭环电路的集成形式,连接发动机控制器与自适应传感器,利用隔离驱动芯片,更改观测器驱动管的作用频率周期,完成自适应控制系统的硬件执行环境搭建。在此基础上,深入分析扩张状态观测器的内部结构,以微分跟踪器作为切入点,选取关键的ESO参数,再借助扰动补偿向量,完成对扩张状态观测器的频域参数配置,联合相关硬件应用设备,实现基于扩张状态观测器的发动机转速双闭环自适应控制系统设计。实验结果表明,在扩张状态观测器作用下,发动机元件在不同压力水平下的实时转速水平均能得到有效控制,可使发动机设备保持较长时间的稳定工作状态。     

非线性不确定系统的自适应观测器设计

非线性状态观测器可改善过程控制性能和故障诊断,针对一类参数不确定非线性系统提出了自适应观测器设计方法。通过微分同胚变换,将非线性系统转换为仅依赖原系统输入、输出的自适应观测器规范形式。利用自适应调节器估计未知参数,用构造的观测器实现状态的重构。Lyapunov稳定性理论分析了状态观测误差动态方程的稳定性,用来证明所设计的自适应观测器为全局渐近收敛的,既实现了系统状态的渐近重构又确保了在持续激励条件下未知参数估计以指数快速收敛到真值,并通过仿真试验。仿真结果表明提出方法的有效性。     

Adaptive Dynamic Surface control of An electrohydraulic Actuator with Friction Compensation

In this paper, an adaptive nonlinear control scheme with a friction observer for position control of an electrohydraulic actuator is proposed. The observer based on the LuGre friction model is employed to compensate for the friction. Adaptation laws are used to handle parameter uncertainties in the actuator and friction model. The control law including dynamics of the observer is developed through a backstepping‐like dynamic surface control (DSC) technique. Experimental results have illustrated the success of the control scheme. The results also show that the adaptive DSC controller has better tracking performance than an adaptive backstepping and conventional PI controllers.     

感应电机转矩跟踪无源控制及自适应观测器设计

针对感应电机高性能转矩跟踪控制和磁链难以直接测量问题,提出了基于无源性的转矩跟踪和自适应磁链观测器控制方案.首先基于感应电机的无源性特性设计了渐近稳定转矩跟踪控制器,重新配置了系统的平衡点,通过注入阻尼提高系统的收敛速度.然后通过将定子电流和转子磁链作为状态变量构建了自适应磁链观测器,简化了观测器结构,根据Lyapunov稳定性理论设计了自适应控制律,实现转子磁链、转速和定子电阻的在线估计.为减小转速估计误差对观测器的影响,给出了观测器增益矩阵的选择方法.仿真结果表明本文所提出的基于自适应观测器的无源控制方案能够有效提高感应电机的动静态性能.     

Optimal reset adaptive observer design

A reset adaptive observer (ReAO) is an adaptive observer consisting of an integrator and a reset law that resets the output of the integrator depending on a predefined reset condition. The inclusion of reset elements can improve the observer performance but it can also destroy the stability of the estimation process if the ReAO is not properly tuned. As contribution, a method to optimally tune the parameters and gains of the ReAO is presented. They are optimally chosen by solving the L₂ gain minimization problem, which can be rewritten as an equivalent LMI problem. The effectiveness of the proposed method is checked by simulations comparing the results of an optimal ReAO with an optimal traditional adaptive observer.     

Robust adaptive backstepping control for a class of uncertain nonlinear systems based on disturbance observers

In this paper, a novel robust adaptive control scheme for a class of uncertain nonlinear systems is proposed using disturbance observer and backstepping method.Firstly, a disturbance observer is developed using radial basis function(RBF) neural network.The parameter updated law of the RBF neural network is given for monitoring subsystem disturbance well.The robust adaptive control scheme is then presented with backstepping method based on the designed disturbance observer.Semiglobal uniform ultimate bounded...     

欠驱动手动态抓取控制的研究

基于手指动力学模型建立了速度观测器.该观测器用于欠驱动手的自适应轨迹跟踪,弥补了欠驱动手没有速度传感器的缺点,补偿了手指模型中欠驱动元件(如扭簧)造成的不确定因素.与PID和计算力矩法相比,其跟踪误差更小,动态控制效果更加理想.通过动力学模型建立了基于力的阻抗控制策略,在阻抗控制中使用速度观测器,实现了基关节的动态抓取力跟踪.动态轨迹跟踪与力跟踪相结合,使欠驱动手在抓握时具有良好的动态抓取性能.     

基于降维观测器的非最小相位系统的快速故障估计

针对传统的基于自适应观测器的故障诊断方法不适用于非最小相位系统的问题,本文基于降维 观测器提出了一种新的自适应故障估计方法．首先引入特殊坐标基（special coordinate basis, SCB）变换,可以 方便地求出误差系统的不变零点．其次对于变换之后的系统,基于降维观测器提出了一种新的快速故障估计 方法,目的在于提高故障估计的性能,即故障估计的快速性和准确性．最后仿真验证了此方法的有效性．