一种基于 DTFEL的非线性自适应逆控制

提出一种基于离散时间反馈误差学习(DTFEL)的两自由度非线性自适应逆控制(AIC)方法,其控制器由动态RBF神经网络(DRBFNN)前馈控制器和参数固定的PD 反馈控制器构成.PD 控制器用来保证闭环系统稳定,动态 RBF神经网络以 PD控制器输出和反馈误差的线性组合为学习信号,通过一种改进的 NLMS(VS MNLMS)算法在线学习和逼近对象的动态逆,提高反馈控制器的性能. 稳定性分析证明了该AIC 系统稳定. 数字仿真结果表明,该 AIC具有良好的自适应能力和鲁棒性,是一种有效的非线性控制方法.

     

超磁致伸缩微致动器车削加工系统模糊自适应精密位移控制

考虑温度及弹簧非线性的影响,基于非线性压磁理论、扩展的J-A模型及动力学分析,建立超磁致伸缩微致动器车削加工系统非线性动力学模型;研究了前馈径向基函数(RBF)逆控制与多模自适应反馈控制方法.提出了以系统运行时间为开关的多模切换控制方案,在系统初始运行时刻采用PD反馈控制器,当时间达到规定的切换时刻采用自适应模糊滑模反馈控制器,使系统全局具有较高的跟踪精度.     

一种基于模糊径向基函数神经网络的自学习控制器

提出了一种新型的基于模糊径向基函数 (RBF)的神经网络学习控制器 ,并应用于电液伺服系统 .由于RBF网络和模糊推理系统具有函数等价性 ,采用模糊经验值方法选取网络中心值和基函数数目 .与一般的神经网络自学习控制器不同 ,以系统动态误差作为网络输入量 ,RBF神经网络控制器学习的是整个系统的动态逆过程 ,因而控制性能明显提高 .对电液位置伺服系统的仿真和实验结果表明 ,该控制方案可以有效提高系统的控制精度和自适应能力     

机器人轨迹跟踪的一种自适应神经鲁棒控制

针对不稳定机器人轨迹跟踪问题,提出了一种基于神经网络的自适应鲁棒控制。该控制方案由一个PD反馈和一个神经动态补偿器组成,其特点是不需要系统不确定性上界的先验知识,而且避免了求解惯性矩阵逆,通过利用一个RBF神经网络自适应学习系统不稳定性的未知上界,从而可以有效克服系统不确定性的影响,保证机器人系统的输出跟踪误差渐近收敛于0。     

基于非线性L1自适应动态逆的飞行器姿态角控制

钊对常规动态逆控制器不能有效抵消系统中的不确定性这一缺点,提出了一种非线性L_1自适应动态逆控制方法.该方法能够克服常规动态逆的不足,在保证系统鲁棒性的前提下,提升飞行器姿态角控制效果.首先,采用时标分离原理,将姿态角控制系统分为内外两个回路:外回路采用常规动态逆控制器,用于姿态角的跟踪控制;内回路采用非线性L_1自适应控制器,用于角速率的控制.其中,L_1自适应控制器由静态反馈控制器和自适应控制器组成:静态反馈控制器通过状态反馈实现,用于保证内回路的稳定和具有期望的闭环特性;自适应控制器由状态观测器、自适应律和控制律组成,用于抵消系统中的不确定性.其次,对所提控制方法的稳定性进行了分析,结果证明了该控制方法能够保证内回路的稳定和外回路的误差有界.最后,在综合考虑多种不确定性的情况下,将本文提出的非线性L_1自适应动态逆控制方法用于某无人飞行器姿态角控制,仿真结果验证了该控制方法的有效性和鲁棒性.     

基于神经网络的注塑机注射速度的迭代学习控制

对具有不确定性和干扰项的重复非线性注塑机控制系统,尤其是注射速度的控制,提出基于神经网络的迭代学习控制器,其中迭代学习控制器设计为神经网络控制器,它以前馈方式作用于对象。PD反馈控制器用于使系统达到稳定,同时和前馈的神经网络学习控制器一起使系统达到理想的控制效果。仿真结果表明,该控制器可以随着迭代次数的增加有效减小跟踪误差。     

未知控制方向的迟滞非线性系统预设自适应控制

针对一类含有迟滞特性的未知控制方向严反馈非线性系统,设计了基于误差变换的反步自适应控制器.首先提出动态迟滞算子来扩展输入空间建立神经网络迟滞模型.然后利用径向基函数(RBF)神经网络逼近未知函数,并引入Nussbaum型函数来解决系统未知控制方向问题.最后采用误差变换将误差限定在预设的范围内,并利用反步法设计自适应控制器.该控制方案不仅能够保证跟踪精度,还可以提高系统暂态和稳态性能.仿真结果表明了控制方案的可行性.     

一种改进的NLMS算法及其在自适应逆控制中的应用

提出一种对FIR滤波器和基于反馈RBF网络的非线性滤波器都适用的改进型NLMS( VS MNLMS)算法,并将其应用于线性和非线性自适应逆控制(AIC)系统的逆建模.该算法计算简单,容易实现,具有全局收敛性.数字仿真结果表明VS MNLMS能获得比其它四种变步长LMS算法更快的收敛速度、更小的稳态MSE,更好的鲁棒性,并使AIC系统具有良好的动静态性能,从而验证了本文提出的算法和非线性滤波器在AIC中的有效性.     

汽车操纵稳定性的自适应控制策略研究

针对汽车系统的非线性和参数不确定性,设计了一种“前馈+反馈”自适应神经模糊控制器,通过ESP和AFS的协调控制来提高汽车操纵稳定性.ESP反馈控制器采用模糊控制策略,以横摆角速度和质心侧偏角为控制目标;AFS前馈控制器采用径向基神经网络控制,以反馈控制器的输出作为误差进行学习,从而实现自适应控制.仿真结果表明,上述控制策略是可行和有效的,能显著改善汽车在高速或湿滑路面上的操纵稳定性.     

基于RBF神经网络的一类不确定非线性系统自适应H∞控制

基于RBF神经网络提出了一种H∞自适应控制方法.控制器由等效控制器和H∞控制器两部分组成.用RBF神经网络逼近非线性函数,并把逼近误差引入到网络权值的自适应律中用以改善系统的动态性能.H∞控制器用于减弱外部干扰及神经网络的逼近误差对跟踪的影响.所设计的控制器不仅保证了闭环系统的稳定性,而且使外部干扰及神经网络的逼近误差对跟踪的影响减小到给定的性能指标.最后给出的算例验证了该方法的有效性.     

Robot learning control based on recurrent neural network inverse model

A non-linear model-based feedforward, feedback, and learning controller is presented. This controller can control a non-linear plant such as a robot whose dynamics are initially unknown. In the feedforward part, a recurrent neural network (RNN) is used to model the inverse dynamics of the plant. In the feedback part, a PD controller is added to handle unmodeled dynamics and disturbances. Furthermore, an add-on learning controller is established to reduce tracking errors for repetitive tasks. The controller is validated with the control of a simulated two-joint manipulator. Simulation results show that the controller can successfully learn the inverse dynamics of a robot, perform accurate tracking for a general trajectory, and improve its own performance over the repetitions of a trajectory, with and without a payload change. © 1997 John Wiley & Sons, Inc.     

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.

     

超磁致伸缩智能构件的位移控制系统设计与仿真

由于超磁致伸缩材料（GMM）内在的迟滞特性会引起智能构件的定位误差,并且其迟滞现象具有输入和输出一对多,输出随输入频率变化的特点,提出一种基于神经网络实现GMM智能构件动态迟滞建模方法。通过所建立神经网络实现GMM 智能构件逆迟滞模型,结合PD反馈控制器,实现智能构件的实时精密位移控制。在Matlab平台上进行仿真,结果表明所建立控制策略能消除GMM智能构件迟滞非线性的影响,实现了GMM智能构件的精密位移控制目的。     

非线性系统近似最优PD动态补偿控制

本文研究了一类基于动态补偿的非线性系统的近似最优PD控制的问题.用微分方程的逐次逼近理论将非线性系统的最优控制问题转化为求解线性非齐次两点边值序列问题,并提供了从时域最优状态反馈到频域最优PD控制器参数的优化方法,从而获取系统最优的动态补偿网络,设计出最优PD整定参数,给出其实现算法.最后仿真示例将所提出的方法与传统的线性二次型调节器（LQR）逐次逼近方法相比较,表明该方法具有良好的动态性能和鲁棒性.     

Linearizing feedforward–feedback control of pH processes based on the Wiener model

This paper examines the control of pH processes based on the Wiener model construct (a dynamic linear element representing the mixing dynamics of the process in series with a static nonlinearity representing the titration curve). Conditions under which the pH process behaves like an exact Wiener system are examined. Linearization by output transformation using both the true inverse of the titration curve and an estimate of the inverse is employed to make the pH process appear linear enabling the application of a linear feedback (PI) controller. Although many others have utilized an identified nonlinearity for linearizing feedback control of pH processes, much less work has been done on using the nonlinearity for linearizing feedforward control. Here, a simple linearizing feedforward controller is proposed based on a current estimate of the inverse titration curve. Simulated closed-loop results demonstrate the superiority of the linearizing feedforward–feedback strategy versus linearizing feedback only, when the inverse titration curve is accurately estimated.     

Robust near-optimal output feedback control of non-linear systems

This work proposes a robust near-optimal non-linear output feedback controller design for a broad class of non-linear systems with time-varying bounded uncertain variables. Both vanishing and non-vanishing uncertainties are considered. Under the assumptions of input-to-state stable (ISS) inverse dynamics and vanishing uncertainty, a robust dynamic output feedback controller is constructed through combination of a high-gain observer with a robust optimal state feedback controller synthesized via Lyapunov's direct method and the inverse optimal approach. The controller enforces exponential stability and robust asymptotic output tracking with arbitrary degree of attenuation of the effect of the uncertain variables on the output of the closed-loop system, for initial conditions and uncertainty in arbitrarily large compact sets, provided that the observer gain is sufficiently large. Utilizing the inverse optimal control approach and singular perturbation techniques, the controller is shown to be near-optimal in the sense that its performance can be made arbitrarily close to the optimal performance of the robust optimal state feedback controller on the infinite time-interval by selecting the observer gain to be sufficiently large. For systems with non-vanishing uncertainties, the same controller is shown to ensure boundedness of the states, uncertainty attenuation and near-optimality on a finite time-interval. The developed controller is successfully applied to a chemical reactor example.     

共轴式无人直升机建模与鲁棒跟踪控制

针对共轴式无人直升机非线性、强耦合的动力学特性,本文提出了一种基于动态反馈线性化方法的鲁棒跟踪控制策略.首先根据叶素理论、Pitt-Peters动态入流模型、上下旋翼气动干扰分析建立了共轴式无人直升机的数学模型.然后对于高度-姿态子系统,通过扩展状态变量对其进行了动态反馈线性化,分析了零动态特性.根据内环期望跟踪特性对解耦后的子系统进行极点配置.通过设计鲁棒补偿器实现了对高度与姿态指令的鲁棒跟踪.在此基础上,针对水平面内的位置子系统设计了外环比例微分(proportional-derivative,PD)控制器以实现位置跟踪.最后,通过内环跟踪仿真验证了反馈线性化方法良好的解耦特性,通过干扰条件下的轨迹跟踪仿真验证了所设计控制器具有较好的控制性能与鲁棒性.     

Autonomous mobile robots navigation using RBF neural compensator

This paper presents an approach to adaptive trajectory tracking of mobile robots which combines a feedback linearization based on a nominal model and a RBF-NN adaptive dynamic compensation. For a robot with uncertain dynamic parameters, two controllers are implemented separately: a kinematics controller and an inverse dynamics controller. The uncertainty in the nominal dynamics model is compensated by a neural adaptive feedback controller. The resulting adaptive controller is efficient and robust in the sense that it succeeds to achieve a good tracking performance with a small computational effort. The analysis of the RBF-NN approximation error on the control errors is included. Finally, the performance of the control system is verified through experiments.     

欠约束多机器人并联协调吊运系统运动控制

针对吊运过程中如何协调控制各机器人以实现负载高精度快速运动问题,采用了一种基于动力学模型前馈补偿+PD反馈的欠约束多机并联协调吊运系统轨迹跟踪控制方法。利用位置几何关系进行了逆运动学分析,并采用拉格朗日方程建立了系统的逆动力学模型。考虑到模型不确定性及外界扰动,采用前馈补偿+PD反馈控制方法进行轨迹跟踪控制。为了使被吊运物的轨迹跟踪控制更加快速准确,采用遗传算法对PD参数进行优化。理论分析和数值仿真结果表明,控制方法实现简单,能够快速有效地跟踪被吊运物的运动轨迹,满足被吊运物轨迹跟踪精度的要求。     

Adaptive actuator compensation control with feedback linearization

An adaptive inverse controller is developed for feedback linearizable nonlinear systems with nonsmooth actuator nonlinearities. The use of an actuator nonlinearity inverse and a feedback linearizing controller leads to an error equation suitable for deriving an adaptive update law for the inverse parameters. Closed-loop signal boundedness is proved analytically, and system performance improvement is shown by simulation results. Such adaptive control schemes are also developed for multivariable nonlinear systems with actuator nonlinearities. For nonlinear systems that do not possess a relative degree, dynamic extension is employed to realize adaptive inverse compensation designs for actuator nonlinearities. These adaptive designs ensure closed-loop stability in the presence of uncertain actuator nonlinearities