Controller Design Based On Wavelet Neural Adaptive Proportional Plus Conventional Integral-Derivative For Bilateral Teleoperation Systems With Time-Varying Parameters

International Journal of Control, Automation and Systems - In this study, a new controller method based on wavelet neural adaptive proportional plus conventional integral-derivative (WNAP+ID)...     

强制循环蒸发系统的非线性自适应解耦PID控制

本文结合现场的实际过程数据,首先应用能量平衡建立了强制循环蒸发过程的动态模型.针对该过程的多变量、非线性以及强耦合特性,在常规增量式PID控制器的基础上提出基于神经网络与多模型切换的非线性自适应解耦PID控制策略.该控制器是由线性自适应解耦PID控制器和基于神经网络的非线性自适应解耦PID控制器以及切换机构组成.其中线性自适应解耦PID控制器可以保证系统的稳定,而基于神经网络的非线性自适应解耦PID控制器则可以有效地提高系统的性能.上述过程的PID参数是通过广义预测的方法得到,最后通过仿真表明,上述控制方法不仅消除了回路间的耦合,在稳定生产的同时提高了蒸发的效率.     

基于Adaptive Interaction理论的PID控制器的设计及应用

本文在Adaptive Interaction理论的基础上,提出了一种新的自调整 PID 控制器。这种新的控制器根据输入及其误差信号进行在线训练,通过误差评价函数的最小化,在模型未知的情况下能很好地调整比例、积分、微分三个参数。对于被控对象的变化具有鲁棒性,很大程度上解决了传统的 PID 控制器对于非线性、不稳定系统控制效果不佳及在线调整困难的问题。通过仿真实例,验证了应用 Adaptive Interaction 理论的 PID 控制器的有效性和实用性。     

一种新型的二维PID模糊控制器

通过对常规PID控制器的结构分析,设计出一种新型的二维PID模糊控制器,其结构形式简称为fuzzy PD+ fuzzy ID型.根据模糊规则的图解分析,提出fuzzy ID控制嚣的输入变量(偏差和偏差变化加速率)与输出变量之间的控制结构,并确定两控制器的模糊控制规则的相似性.通过对该PID模糊控制器的结构分析,给出与常...     

DC／DC变换单神经元自适应PID控制器仿真研究

为解决传统PI控制对具有时变、非线性特性的DC/DC变换器动态控制性能不佳的问题,将人工神经网络与PID控制理论相结合,为DC/DC变换器设计了一种单神经元自适应PID控制器。该控制器算法简单,通过对加权系数的在线调整来实现自适应、自学习功能,从而满足DC/DC变换器的时变及非线性特性。以BUCK型变换器为例,建立了DC/DC变换器智能控制系统的仿真模型,在不同负载及参数变化的情况下与常规PI控制效果进行对比分析,结果表明,单神经元自适应PID控制器对DC/DC变换器具有很好的控制效果和鲁棒性。     

Structural analysis of fuzzy controllers with nonlinear input fuzzy sets in relation to nonlinear PID control with variable gains

The popular linear PID controller is mostly effective for linear or nearly linear control problems. Nonlinear PID controllers, however, are needed in order to satisfactorily control (highly) nonlinear plants, time-varying plants, or plants with significant time delay. This paper extends our previous papers in which we show rigorously that some fuzzy controllers are actually nonlinear PI, PD, and PID controllers with variable gains that can outperform their linear counterparts. In the present paper, we study the analytical structure of an important class of two- and three-dimensional fuzzy controllers. We link the entire class, as opposed to one controller at a time, to nonlinear PI, PD, and PID controllers with variable gains by establishing the conditions for the former to structurally become the latter. Unlike the results in the literature, which are exclusively for the fuzzy controllers using linear fuzzy sets for the input variables, this class of fuzzy controllers employs nonlinear input fuzzy sets of arbitrary types. Our structural results are thus more general and contain the existing ones as special cases. Two concrete examples are provided to illustrate the usefulness of the new results.     

拟人智能控制在温控系统中的应用

针对实际多变量、强耦合非线性温控系统，设计了自适应拟人智能控制器。采用了分区控制思想，根据不同的误差和误差变化趋势，采用了不同的PID控制策略。为了更好地克服各种外界因素的影响，构造了误差观测器，在线实时调整控制器参数。同时，为了有效克服系统时滞特性的影响，进一步引入了提前补偿的控制思想。实际系统运行结果表明了该控制策略的有效性。     

Wavenet fuzzy PID controller for nonlinear MIMO systems: Experimental validation on a high-end haptic robotic interface

A novel global PID control scheme for nonlinear MIMO systems is proposed and implemented for a robot as study case, this scheme is called AWFPID from its adaptive wavelet fuzzy PID control structure. Basically, it identifies inverse error dynamics using a radial basis neural network with daughter RASP1 wavelets activation function; its output is in cascaded with an infinite impulse response (IIR) filter to prune irrelevant signals and nodes as well as to recover a canonical form. Then, online adaptive fuzzy tuning of a discrete PID regulator is proposed, whose closed-loop guarantees global regulation for nonlinear dynamical plants. The wavelet network includes a fuzzy inference system for online tuning of learning rates. A real-time experimental study on a three degrees of freedom haptic interface, the PHANToM Premium 1.0A, highlights the regulation with smooth control effort without using the mathematical model of the robot.     

一种PID模糊控制器(fuzzy PI+fuzzy ID型)

提出一种二维PID模糊控制器,其结构形式筒称为fuzzy PI+fuzzy ID型.根据fuzzy PI和fuzzy ID控制器的图解说明,确定该fuzzy PI和fuzzy ID控制器的模糊控制规则的相似性.理论分析表明,该PID模糊控制器除具有常规PID性能外,还具有非线性等特点.仿真结果表明,与常规PID和fuzzy PI控制相比性能更优.     

基于执行器–评价器学习的自适应PID控制

针对传统PID控制器无法在线自整定参数的不足,提出了一种基于执行器一评估器（Actor-Critic,AC）学习的自适应PID控制器结构与学习算法．该控制器利用AC学习实现PID参数的自适应整定,采用一个径向基函数网络同时对Actor的策略函数和Critic的值函数进行逼近．径向基函数网络的输入为系统误差、误差的一次差分和二次差分,Actor实现系统状态到PID参数的映射,Critic则对Actor的输出进行评判并且生成时序差分（temporaldifference,TD）误差信号．基于AC学习的体系结构和TD误差性能指标,给出了控制器设计的步骤流程图．两个仿真实验表明：与传统的PID控制器相比,基于AC学习的PID控制器在响应速度和自适应能力方面要优于传统PID控制器．     

挠性航天器的非线性PID和PI姿态控制器设计

针对存在模型不确性和常值干扰的挠性航天器, 提出一种不依赖于模型参数的非线性PID姿态控制器. 该控制器在小姿态偏差的情况下近似经典的线性PID控制器. 另外, 考虑到航天器上陀螺失效情况, 设计了一种仅需姿态测量信息的非线性PI控制器. 这两种控制器在局部均对常值干扰有抑制作用, 并能使无干扰作用的姿态控制系统半全局渐近稳定. 闭环系统的稳定性证明采用了奇异扰动理论, 以解决积分项的存在带来的稳定性分析问题. 文章最后用数学仿真验证了控制器的性能.     

自适应模糊PID控制器在跟踪器瞄准线稳定系统中的应用

针对陀螺惯性平台上的跟踪器瞄准线稳定系统中非线性不确定因素对稳定精度的影响, 设计了一种自适应模糊PID复合控制策略. 提出了改进的自适应调整因子和学习算法进行控制参数和规则的在线修正; 采用PID控制克服模糊控制固有的精度盲区. 实验结果表明该方法在一定测量噪声和速度敏感范围内, 能有效地隔离载体扰动,保证跟踪器对目标的准确瞄准, 具有动态响应快、稳定精度高、自适应抗干扰鲁棒性强等特点.     

无刷直流电机的模糊神经网络控制设计

在无刷直流电机(BLDCM)的控制上,传统PID等控制方法存在或多或少的不足.在模糊PID控制的基础上提出了一种模糊神经网络PI控制器的设计方法.该方法结合了模糊逻辑与神经网络,使得模糊控制器模拟了人的控制功能,不仅对环境变化有较强的适应能力,还拥有自学习能力.相比模糊PID控制,其具有计算量小、稳定性强等特点.对BLDCM进行建模与分析;在BLDCM数学模型的基础上,分别设计模糊PID控制器和模糊神经网络PI控制器;对设计的控制器进行仿真验证并分析.实验结果表明,模糊神经网络PI控制具有跟踪性能好、超调小、响应快、脉动小等优点,其动静态特性均优于模糊PID控制.     

基于神经网络的模糊自适应PID控制方法

提出一种基于BP神经网络的模糊自适应PID控制器。该控制器综合模糊控制、神经网络与PID调节各自的优点，既具有模糊控制的简单和有效的非线性控制作用，又具有神经网络的学习和适应能力，同时具备PID控制的广泛适应性，仿真实验表明该控制器对模型、环境具有较好的适应能力和较强的鲁棒性。     

Adaptive Fourier Series Neural Network PID Controller

In this work, an Adaptive Neural Networks PID controller structure, called Adaptive Fourier Series Neural Networks PID controller (AFSNNPID), is developed. The main objective is to obtain a simple controller for nonlinear systems that can be tuned online to reject perturbations effect and compensate the system parameters variation. Due to its simple architecture and very attractive proprieties, the Fourier Series Neural Network (FSNN) is used to online adjust the parameters of the PID controller. Furthermore, using the delta-rule algorithm, the adaptation dynamics of the FSNN is globally stable. The design procedure of the proposed controller and the stability analysis of the closed loop system using the small gain theorem are given. To assess the effectiveness of the proposed control scheme, the control of a 3-DOF robot arm manipulator is considered and a comparative study, using the adaptive neural network PID controller and the particle swarm optimization based PID controller, is carried out. The obtained results, through the experimental study, indicate that the AFSNNPID controller presents better control performance than the other controllers.

     

具有H_∞跟踪性能的机器人自适应PID控制

针对非线性不确定机器人系统的轨迹跟踪控制问题,提出一种鲁棒自适应PID控制算法.该控制器由主控制器和监督控制器组成.主控制器以常规PID控制为基础,基于滑模控制思想设计PID参数的自适应律,根据误差实时修正PID参数.基于Lyapunov函数设计的监督控制器补偿自适应PID控制器与理想控制器之间的差异,使系统具有设定的H_∞的跟踪性能.最后,两关节机器人的仿真实验结果表明了算法的有效性.

Abstract：

A robust adaptive PID control algorithm is proposed for trajectory tracking of robot manipulators with nonlinear uncertainties.The controller is composed of a main controller and a supervisory controller.The main controller is designed based on the traditional PID controller.The parameters of the PID controller are updated online according to the system running errors with the adaptation law based on the sliding mode control.The supervisory controller is proposed to compensate the error between the adaptive PID controller and the ideal controller in the sense of the Lyapunov function with the specified H_∞ tracking performance.Finally, the simulation results based on a two-joint robot manipulator show the effectiveness of the presented controller.     

Design of an enhanced hybrid fuzzy P+ID controller for a mechanicalmanipulator

We propose in this paper an enhanced fuzzy P+ID controller to improve control performance in both dynamic transient and steady-state periods for mechanical manipulators under uncertainty. The fuzzy P+ID controller adds only two additional parameters to be tuned relative to the original PID controller. One of these parameters is mainly used to reduce a steady-state error. The other is used to speed up the dynamic response. A simulation study and experimental results for a two-link manipulator with uncertainty demonstrate the superior control performance of the proposed fuzzy P+ID controllers.     

制冷站双目标权重自适应非线性预测控制

针对传统制冷站控制系统易产生振荡, 且无法实现系统性能整体优化的问题, 本文提出一种制冷站非线性 预测控制策略, 优化目标函数设计为满足建筑冷量需求的同时, 尽可能提高系统整体能效. 为解决上述两个优化目 标之间的矛盾关系, 本文采用模糊逻辑设计了优化目标权重自适应模块, 实时求取权重因子最优解; 针对非线性系 统在线优化求解困难问题, 本文提出了基于神经网络的非线性滚动优化算法, 采用神经网络作为反馈优化控制器, 并将系统优化目标函数作为在线寻优性能指标, 结合Euler-Lagrange方法和随机梯度下降法对控制器权值和阈值进 行在线寻优, 算法计算量小, 占用存储空间适中, 便于采用低成本的现场控制器实现制冷站预测控制. 仿真实验结果 表明, 本文所提出的预测控制策略与PID控制相比, 在未加入优化目标函数权重自适应模块情况下, 系统平均能效 比提高约32.5%; 进行优化目标函数权重自适应寻优后, 系统平均能效提高约39.43%.     

基于小波变换的非线性广义预测控制

提出了基于小波变换的非线性广义预测控制算法。预测模型采用Hammerstein模型,对于其静态非线性部分采用小波网络来辨识,动态线性部分用最小二乘法来辨识。这种辨识方法比传统的多项式拟合的模型误差要小得多。基于这种预测模型广义预测控制器弥补了传统广义预测控制的模型失配问题。以CSTR为例对所设计的控制器进行仿真研究,结果表明控制器能够取得良好的控制效果。     

非伪控制解决惯性稳定平台的非线性时变干扰

非伪控制是一种基于在线数据的驱动控制方式.本文通过进化策略实现候选控制器的多样性操作,结合滞后算法切换控制器,从而实现系统自适应的PID稳定控制.在惯性稳定平台中,由于非线性时变干扰的存在令被控对象不能提供精确的控制模型,今将非伪控制算法用于惯性稳定平台中,能使惯性稳定平台准确地稳定框架,其方位轴稳定误差小于3.4mrad,俯仰轴稳定误差小于4.36mrad.