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.     

非线性旋转倒立摆智能控制的实验研究

应用欧拉一拉格朗日原理建立旋转倒立摆系统非线性、多变量耦合的动力学方程,采用梯度下降和最小二乘混合学习算法将神经网络和模糊控制相结合,得到一种具有自适应、自学习能力的模糊神经网络智能控制器,该控制器在SRV02-Series旋转倒立摆实验平台上取得成功应用.     

基于微分平坦的旋转倒立摆双闭环抗扰 PID 控制

针对一类不稳定、欠驱动二自由度旋转倒立摆系统, 提出一种基于微分平坦的双闭环抗扰 PID 控制方法。 首先, 建立倒 立摆的非线性动力学模型, 利用近似线性化分析系统的不稳定零动态与非最小相位特性; 然后, 结合微分平坦理论, 设计倒立摆 平坦输出,重构系统平坦状态, 并建立平坦状态与角度输出之间的转化关系,克服倒立摆的非最小相位影响; 进一步, 针对微分 平坦系统, 设计抗扰 PID 双闭环控制结构和带宽化调节方法, 在主动抗扰机制下实现对摆杆角度与悬臂角度的精准控制; 最后, 通过仿真与实验, 验证所提方法的有效性和实用性。 所提方法为欠驱动系统提供了一种结构简单、抗扰能力强的控制方案。     

抑制倒立摆测量噪声的反演控制器设计

针对存在随机测量噪声的单级倒立摆系统,提出了一种带有低通滤波的反演控制方法。该方法将倒立摆系统输出的摆角作为低通滤波器的输入信号,低通滤波器的输出作为滤除测量噪声之后系统输出的摆角。即将倒立摆系统和低通滤波器看作一个整体的被控系统,并根据该系统逐步推导出反演控制律。设计的控制器能够有效滤除测量噪声,增强了系统的鲁棒性,提高了倒立摆系统的跟踪精度,并且该控制器结构简单,便于工程实现。最后,通过仿真实验进一步验证了该控制器的有效性。     

自适应神经模糊推理系统在倒立摆控制中的应用

针对单级倒立摆系统具有多变量、非线性、绝对不稳定的特点,应用Matlab/Simulink设计了用于倒立摆系统的、基于自适应神经模糊推理系统的ANFIS控制器,采用反向传播算法和最小二乘算法的混合算法对倒立摆控制样本数据进行学习,调整各变量的隶属度函数,自动产生模糊规则.仿真结果表明,ANFIS控制器对倒立摆系统的摆杆角度和小车位置的控制过程具有良好的动态性能和稳态性能.     

直线二级倒立摆系统的H_∞鲁棒最优控制

针对存在模型参数不确定性和外部扰动的直线型二级倒立摆系统的稳摆控制问题,对如何实现倒立摆系统鲁棒稳定的同时,还保证其达到期望的最优控制性能和H_∞性能指标的稳定性控制问题进行了研究。基于状态反馈、最优控制和H_∞鲁棒控制方法设计了一个H∞鲁棒最优控制器,用来实现对该倒立摆系统的稳摆控制。利用设计的控制器,基于倒立摆实验平台和Matlab数值仿真平台,使用该控制器来实现系统的控制并对稳定倒立摆的过程进行了验证。研究结果表明,该控制器能较好的实现对直线型二级倒立摆的稳摆控制,并对受控系统的模型参数摄动有较好的鲁棒性,同时还能利用最优的控制作用达到期望的H_∞性能指标。     

基于Matlab和Automation Studio的倒立摆控制仿真

针对一级倒立摆的控制问题使用状态反馈极点配置、PID方法控制、线性二次型最优调节器三种方法,使用Matlab和Automation Studio(AS)软件进行了建模和仿真,实现了一阶倒立摆的稳定控制。在建立完善的模型中,使用阶跃信号、正弦信号、脉冲信号、噪声信号等进行了测试,测试结果表明,倒立摆系统可实现稳定控制。     

旋转型倒立摆应用T-S模型模糊控制的研究

针对不稳定旋转倒立摆系统 ,在对其进行动力学分析的基础上 ,借助于状态反馈极点配置方法 ,提出了一种基于T -S模型的模糊控制器设计方法 ,并进行了计算机仿真。仿真结果表明 ,该方法具有响应快、稳定性好 ,是解决倒立摆及高阶不稳定系统等问题的理想方案     

基于切换方法的倒立摆起振和平衡控制

采用切换控制的方法实现环型倒立摆的起振和平衡控制。通过设计非线性前馈控制器实现倒立摆的起振控制,当环型倒立摆达到非稳定平衡点附近的时候,切换到线性状态反馈控制器实现倒立摆的镇定控制。改变线性状态反馈控制器的输入还可以实现环型倒立摆的旋转平衡控制。仿真验证了控制策略的有效性。     

一类非线性系统的变结构控制器设计

将非线性系统用HM模型进行描述，并将全局HM模型表示成不确定系统形式，采用不确定系统的变结构控制器设计方法，设计出使全局HM模型渐近稳定的变结构控制器。并用一级倒立摆进行仿真验证。成功的将变结构控制应用于非线性控制系统中。     

Robust adaptive precision motion control of hydraulic actuators with valve dead-zone compensation

This paper addresses the high performance motion control of hydraulic actuators with parametric uncertainties, unmodeled disturbances and unknown valve dead-zone. By constructing a smooth dead-zone inverse, a robust adaptive controller is proposed via backstepping method, in which adaptive law is synthesized to deal with parametric uncertainties and a continuous nonlinear robust control law to suppress unmodeled disturbances. Since the unknown dead-zone parameters can be estimated by adaptive law and then the effect of dead-zone can be compensated effectively via inverse operation, improved tracking performance can be expected. In addition, the disturbance upper bounds can also be updated online by adaptive laws, which increases the controller operability in practice. The Lyapunov based stability analysis shows that excellent asymptotic output tracking with zero steady-state error can be achieved by the developed controller even in the presence of unmodeled disturbance and unknown valve dead-zone. Finally, the proposed control strategy is experimentally tested on a servovalve controlled hydraulic actuation system subjected to an artificial valve dead-zone. Comparative experimental results are obtained to illustrate the effectiveness of the proposed control scheme.     

Robust tracking and distributed synchronization control of a multi-motor servomechanism with H-infinity performance

The multi-motor servomechanism (MMS) is a multi-variable, high coupling and nonlinear system, which makes the controller design challenging. In this paper, an adaptive robust H-infinity control scheme is proposed to achieve both the load tracking and multi-motor synchronization of MMS. This control scheme consists of two parts: a robust tracking controller and a distributed synchronization controller. The robust tracking controller is constructed by incorporating a neural network (NN) K-filter observer into the dynamic surface control, while the distributed synchronization controller is designed by combining the mean deviation coupling control strategy with the distributed technique. The proposed control scheme has several merits: 1) by using the mean deviation coupling synchronization control strategy, the tracking controller and the synchronization controller can be designed individually without any coupling problem; 2) the immeasurable states and unknown nonlinearities are handled by a NN K-filter observer, where the number of NN weights is largely reduced by using the minimal learning parameter technique; 3) the H-infinity performances of tracking error and synchronization error are guaranteed by introducing a robust term into the tracking controller and the synchronization controller, respectively. The stabilities of the tracking and synchronization control systems are analyzed by the Lyapunov theory. Simulation and experimental results based on a four-motor servomechanism are conducted to demonstrate the effectiveness of the proposed method.     

基于干扰观测器的电液伺服系统反馈线性化滑模控制

为抑制电液伺服系统中各种非线性因素及不确定干扰,提出了基于输入输出反馈线性化的滑模控制与非线性干扰观测器相结合的控制策略以提高其位置控制跟踪精度。以电液振动台为试验对象,建立其非线性控制模型,利用李雅普诺夫稳定性理论保证了位置闭环系统的全局稳定性。利用MATLAB/Simulink对设计的控制器进行了仿真验证,结果验证了提出的控制器的可行性。为了模拟实际环境下存在不确定干扰,在位置电液系统基础上增加了电液加载系统,开展了试验研究。结果表明,该控制器能有效的提高干扰下电液伺服系统的位置跟踪性能。     

基于误差反馈学习的永磁同步电机变负载有限时间速度控制

针对传统永磁同步电机调速系统面对变负载和大范围调速时,P、I参数需要频繁调整且速度跟踪不理想的问题,提出了一种基于误差反馈学习结构的永磁同步电机有限时间速度控制方法。在对永磁同步电机运动方程分析的基础上,使用非线性PI和径向基神经网络建立了速度环控制器模型。前者保证控制系统收敛和稳定,其输出作为神经网络的误差学习参数;后者基于终端滑模理论设计参数调整律,加快神经网络的参数收敛速度,使得神经网络的输出逐渐取代非线性PI成为控制系统的主要控制器。利用李雅普诺夫稳定判据分析了控制器的收敛性,并在永磁同步电机调速系统上进行了试验。研究结果表明,基于误差反馈学习结构的有限时间控制策略能够减小系统静态误差和抖振,具有一定的抗干扰能力。     

Finite-time decentralized adaptive neural constrained control for interconnected nonlinear time-delay systems with dynamics couplings among subsystems

The problem of finite-time decentralized neural adaptive constrained control is studied for large-scale nonlinear time-delay systems in the non-affine form. The main features of the considered system are that 1) unknown unmatched time-delay interactions are considered, 2) the couplings among the nested subsystems are involved in uncertain nonlinear systems, 3) based on finite-time stability approach, asymmetric saturation actuators and output constraints are studied in large-scale systems. First, the smooth asymmetric saturation nonlinearity and barrier Lyapunov functions are used to achieve the input and output constraints. Second, the appropriately designed Lyapunov-Krasovskii functional and the property of hyperbolic tangent functions are used to deal with the unknown unmatched time-delay interactions, and the neural networks are employed to approximate the unknown nonlinearities. Note that, due to unknown time-delay interactions and the couplings among subsystems, the controller design is more meaningful and challenging. At last, based on finite-time stability theory and Lyapunov stability theory, a decentralized adaptive controller is proposed, which decreases the number of learning parameters. It is shown that the designed controller can ensure that all closed-loop signals are bounded and the tracking error converges to a small neighborhood of the origin. The simulation studies are presented to show the effectiveness of the proposed method.     

基于NDO的ROV滤波反步轨迹跟踪控制

针对作业型遥控水下机器人(ROV)在轨迹跟踪过程中存在模型非线性、强耦合、模型参数不确定和外界干扰不确定等问题,提出一种基于非线性干扰观测器(NDO)的滤波自适应反步控制策略。使用NDO观测模型的不确定性和外界干扰,通过指令滤波器避免了直接对虚拟控制量解析求导的过程,利用自适应律补偿观测器观测残量。通过Lyapunov稳定性理论证明了跟踪误差系统的渐进稳定。仿真实验表明,设计的控制器能够实现精确的轨迹跟踪,具有较好的鲁棒特性。     

Adaptive backstepping-based tracking control design for nonlinear active suspension system with parameter uncertainties and safety constraints

This paper proposes a novel constraint adaptive backstepping based tracking controller for nonlinear active suspension system with parameter uncertainties and safety constraints. By introducing the virtual control input and reference trajectories, the adaptive control law is developed to stabilize both of the vertical and pitch motions of vehicle body using backstepping technique and Lyapunov stability theory, and further to track the predefined reference trajectories within a finite time, which not only ensure the safety performance requirements, but also achieve improvements in riding comfort and handling stability of vehicle active suspension system. Next, the stability analysis on zero dynamics error system is conducted to ensure that all the safety performance indicators are all bounded and the corresponding upper bounds are estimable. Finally, a numerical simulation is provided to verify the effectiveness of the proposed controller and to address the comparability between the classical Barrier–Lyapunov Function based adaptive tracking controller and the proposed controller.     

Robust adaptive integral backstepping control for opto-electronic tracking system based on modified LuGre friction model

This paper presents a robust adaptive integral backstepping control strategy with friction compensation for realizing accurate and stable control of opto-electronic tracking system in the presence of nonlinear friction and external disturbance. With the help of integral control term to decrease the steady-state error of the system and combining robust adaptive control approach with the backstepping design method, a novel control method is constructed. Nonlinear modified LuGre observer is designed to estimate friction behavior. Robust adaptive integral backstepping control strategy is developed to compensate the changes in friction behavior and external disturbance of the servo system. The stability of the opto-electronic tracking system is proved by Lyapunov criterion. The performance of robust adaptive integral backstepping controller is verified by the opto-electronic tracking system with modified LuGre model in simulation and practical experiments. Compared to the adaptive integral backstepping sliding mode control method, the root mean square of angle error is reduced by 26.6% when the proposed control method is used. The experiment results demonstrate the effectiveness and robustness of the proposed strategy.     

Neural networks for tracking of unknown SISO discrete-time nonlinear dynamic systems

This article presents a Lyapunov function based neural network tracking (LNT) strategy for single-input, single-output (SISO) discrete-time nonlinear dynamic systems. The proposed LNT architecture is composed of two feedforward neural networks operating as controller and estimator. A Lyapunov function based back propagation learning algorithm is used for online adjustment of the controller and estimator parameters. The controller and estimator error convergence and closed-loop system stability analysis is performed by Lyapunov stability theory. Moreover, two simulation examples and one real-time experiment are investigated as case studies. The achieved results successfully validate the controller performance.     

三阶线性自抗扰控制器的液压伺服流量控制

针对阀控液压马达系统受非线性复杂扰动导致流量输出不稳定的问题,提出一种基于三阶线性自抗扰控制器(LADRC)的液压伺服流量控制方法。基于高阶LADRC理论,提出将ADRC应用于非线性的液压伺服系统控制,分析并验证了跟踪微分器的跟踪误差前馈增益具有抑制系统超调的作用。采用跟踪误差前馈与扩张状态观测器扰动反馈相分离的办法,提出一种针对复杂非线性三阶被控系统的改进的三阶LADRC算法。最后验证了该算法对一类大范围复杂不确定性液压伺服系统具有较PID更强的扰动抑制能力。