规则自适应模糊控制器*

本文研究了模糊控制器的控制规则自适应问题，提出了一种新的模糊控制规则自生成与自校正方法及相应的算法。仿真研究结果表明本文提出的控制规则自适应算法是有效的。     

一种新的自适应模糊滑模控制器设计方法

对一类非线性系统提出一种新的自适应模糊滑模控制器设计方法。将自适应模糊控制与滑模控制有效地结合在一起,先用滑模控制使跟踪误差进入边界层内,然后启动自适应模糊控制器。该控制器可消除滑模控制器中出现的抖振,并可在存在模糊逻辑系统逼近误差情况下使系统跟踪误差小于预先给定的任意常数。仿真算例验证了所提出方法的有效性。     

一种多变量自适应模糊控制器的设计方法

把采用梯度学习算法的自适应模糊控制器设计方法推以多为情形。针对梯度学习算法收敛速度慢的缺点，把神经元学习中的赫布（Ｈｅｂｂ）规则与梯度算法相结合，构成梯度一赫布学习算法，从而使收敛速度明显加快，满足了过程控制中实时性的要求。仿真研究表明，本文的设计方法是可行的。     

应用单层神经网络设计多变量自适应模糊控制器

提出一种应用单层神经网络设计多变量自适应模糊控制器的方法。应用单层神经网络可以学习多变量模糊控制规则中的未知参数，还可由它来实现多变量模糊推理过程。该方法能解决多变量模糊控制中普遍存在的规则获取困难和难于实现实时自适应等问题。仿真试验表明，所设计的多变量模糊控制器不仅实时性好，而且可得到满意的控制效果。     

基于规则插入的自适应模糊控制器的设计

提出一种为模糊控制器系统开发有效规则的自适应模糊控制器的设计方法.该方法首先基于两个基本的If-then模糊规则,然后插入多个新If_then模糊规则,其合成的模糊规则的输入_输出映射保持不变,且其隶属函数的参数在线约束为最小化成本函数,最后以最典型的非线性和延迟系统模型为例进行仿真实验,结果证明了该设计方法的有效性.     

用模糊RBF神经网络简化模型设计多变量自适应模糊控制器

针对多变量系统实时性要求,提出模糊径向基神经网络结构的简化模型及相应算法,并对由此简化模型设计的多变量模糊控制器模糊规则的在线自学习算法进行分析,提出一种系统动态增益的处理方法和基于过程最优的改进方案,仿真实验结果表明该控制器可实时自自应控制,改进算法是有效的。     

自适应模糊控制器在液压电梯中的应用

采用一种建立在连续输入输出论 的模糊控制器,避免了普通模糊控制器由外论域的离散化而存在量化误差。通过采用分级模糊控制的方法一步提高了系统的控制精度和收敛品质,同时还给出了一种自适应控制算法,利用它可以调整模糊规则以适应系统的时变性。利用该文的模糊控制器对典型非线性时变系统一－ＶＶＶ液压电梯的实时控制结果表明,这种模糊控制器鲁棒性好,控制精度高,自适应能力强,具有很高的实用价值。     

一种自适应模糊控制器

对文 [1]提出的模糊自适应控制算法提出改进方案 ,根据改进方案 ,控制算法可以去掉监督项 ,同时可加入一辅助控制项以提高动态特性 .经证明 ,算法仍然满足全局稳定 .为保证闭环渐近稳定性条件 ,文中提出一种模糊控制器结构自组织学习方法 .仿真结果显示 ,与文 [1]算法相比 ,本文算法更能保证闭环渐近稳定性 ,具有更好的动态性能 .     

利用模糊系统的自适应模糊控制器

针对非线性系统控制,设计了利用TSK(Takagi-Sugeno-Kang)模糊系统的自适应模糊控制器。所设计的自适应控制方法是参考模型自适应控制方法,而且利用Lyapunov函数保证了闭环系统的稳定性,同时推导了最优的自适应控制规律。首先,根据控制对象的输入输出数据建立TSK模糊模型,然后,由TSK模糊模型设计初期的TSK模糊控制器,并根据自适应规律随时调整模糊控制器参数。倒立摆系统的仿真实验验证了所设计的自适应模糊控制器的有效性。     

永磁无刷直流电机模糊自适应控制器设计

关于直流电机优化问题,针对永磁无刷商流电机工作过程中电气参数的非线性、时变特性,影响系统的品质特性.在分析电机模型的基础上,提出采用积分系数、比例系数模糊自整定技术,按照电机理想的起动、稳态和制动过程要求,设计了速度偏差、速度偏差微分、控制器输出和比例因子隶属度函数及相应的模糊控制规则,构成了模糊自适应控制器.经仿真结果表明,设计的控制器对电机负载、电机电气参数大范围扰动具有较强的抑制能力,系统动态响应快、超调小,达到了对电机参数变化的自适应控制目的,并为控制器设计提供参考.     

机械手的模糊逆模型鲁棒控制

提出一种基于模糊聚类和滑动模控制的模糊逆模型控制方法,并将其应用于动力学方程未知的机械手轨迹控制.首先,采用C均值聚类算法构造两关节机械手的高木-关野(T-S)模糊模型,并由此构造模糊系统的逆模型.然后,在提出的模糊逆模型控制结构中,离散时间滑动模控制和时延控制(TDC)用于补偿模糊建模误差和外扰动,保证系统的全局稳定性并改进其动态和稳态性能.系统的稳定性和轨迹误差的收敛性可以通过稳定性定理来证明.最后,以两关节机械手的轨迹跟随控制为例,揭示了该设计方法的控制性能.     

基于一阶不确定项估计律的机械臂鲁棒控制研究

针对多自由度机械臂控制系统的模型参数误差、关节摩擦力以及外部输入扰动等不确定项,设计了一类一阶误差估计律;结合基于机构动力学名义模型的输入输出反馈线性化控制算法,对六自由度刚性机械臂的时变轨迹跟踪控制进行了研究,理论上证明了设计的鲁棒控制器是全局渐进稳定的.仿真结果表明该控制策略对系统的各类不确定项具有很好的鲁棒性,能够实现高精度的轨迹跟踪控制.     

Application of Fuzzy Logic for the Solution of Inverse Kinematics and Hierarchical Controls of Robotic Manipulators

In this paper, hierarchical control techniques is used for controlling a robotic manipulator. The proposed method is based on the establishment of a non-linear mapping between Cartesian and joint coordinates using fuzzy logic in order to direct each individual joint. The hierarchical control will be implemented with fuzzy logic to improve the robustness and reduce the run time computational requirements. Hierarchical control consists of solving the inverse kinematic equations using fuzzy logic to direct each individual joint. A commercial Microbot with three degrees of freedom is utilized to evaluate this methodology. A decentralized fuzzy controller is used for each joint, with a Fuzzy Associative Memories (FAM) performing the inverse kinematic mapping in a supervisory mode. The FAM determines the inverse kinematic mapping which maps the desired Cartesian coordinates to the individual joint angles. The individual fuzzy controller for each joint generates the required control signal to a DC motor to move the associated link to the new position. The proposed hierarchical fuzzy controller is compared to a conventional controller. The simulation experiments indeed demonstate the effectiveness of the proposed method.     

Position and Force Control of Two Constrained Robotic Manipulators

In this paper, the problem of controlling two robotic manipulators handling a constrained object is addressed. First, a reduced order dynamic model of the system is obtained. Using this model, a controller that guarantees the asymptotic convergence of the position, the internal force, and the constraint force to their desired values is proposed. Simulation results for two three-link planar manipulators moving a constrained object demonstrate the effectiveness of the proposed controller.     

Control of Underactuated Manipulators using Fuzzy Logic Based Switching Controller

This paper introduces a new concept for designing a fuzzy logic based switching controller in order to control underactuated manipulators. The proposed controller employs elemental controllers, which are designed in advance. Parameters of both antecedent and consequent parts of a fuzzy indexer are optimized by using evolutionary computation, which is performed off-line. Design parameters of the fuzzy indexer are encoded into chromosomes, i.e., the shapes of the Gaussian membership functions and corresponding switching indices of the consequent part are evolved to minimize the angular position errors. Such parameters are trained for different initial configurations of the manipulator and the common rule base is extracted. Then, these trained fuzzy rules can be brought into the online operations of underactuated manipulators. 2-DOF underactuated manipulator is taken into consideration so as to illustrate the design procedure. Computer simulation results show that the new methodology is effective in designing controllers for underactuated robot manipulators.     

A Robust Adaptive Terminal Sliding Mode Control for Rigid Robotic Manipulators

In this paper, a robust adaptive terminal sliding mode controller is developed for n-link rigid robotic manipulators with uncertain dynamics. An MIMO terminal sliding mode is defined for the error dynamics of a closed loop robot control system, and an adaptive mechanism is introduced to estimate the unknown parameters of the upper bounds of system uncertainties in the Lyapunov sense. The estimates are then used as controller parameters so that the effects of uncertain dynamics can be eliminated and a finite time error convergence in the terminal sliding mode can be guaranteed. Also, a useful bounded property of the derivative of the inertial matrix is explored, the convergence rate of the terminal sliding variable vector is investigated, and an experiment using a five bar robotic manipulator is carried out in support of the proposed control scheme.     

柔性机器人的动力学建模及其控制*

本文首先综述了近年来在柔性机器人动力学建模方面所取得的进展，着重介绍了目前较常见的几种建模方法和模型，同时对柔性机器人的控制问题进行了评述，指出了今后研究的方向。     

Modeling of Flexible Beams for Robotic Manipulators

This work treats the problem of modeling robotic manipulators withstructural flexibility. A mathematical model of a planarmanipulator with a single flexible link is developed. This modelis capable of reproducing nonlinear dynamic effects, such as thebeam stiffening due to the centrifugal forces induced by therotation of the joints, giving it the capability to predictreliable dynamic behaviors for a wide range of applications. Onthe other hand, the model complexity is reduced, in order to keepit amenable for analysis and controller design. The models foundin current literature for control design of flexible manipulatorarms present dynamic limitations for the sake of real timeimplementation in a control scheme. These limitations are theresult of premature linearizations in the formulation of thedynamics equations. In this paper, these common linearizations arepresented and their dynamic limitations uncovered. An alternativereliable model is then presented. The model is founded on twobasic assumptions: inextensibility of the neutral fiber, andmoderate rotations of the cross sections in order to account forthe foreshortening of the beam due to bending. Simulation andexperimental results show that the proposed model has the closestdynamic behavior to the real beam.     

一种自调整模糊控制器的设计与仿真研究

提出了一种基于规则，参数自整定的模糊控制器的设计方法，它通过引入误差加权因子和误差变化率加权因子，以系统阶跃响应的上升时间和超调量为性能评价指标，并根据评价结果建立自适应算法，对模糊控制器的模糊推理规则及参数进行自调整。仿真结果进行表明，误差变化率加权因子以明显改善模糊控制器的稳定性，性能评价指标函数，模糊推理规则和参数调整算法简单实用，便于实现，对被控对象的参数变化具有较强的适应能力，控制效果良好。     

增益模糊自整定PID控制器设计

本文提出了一种模糊自整定ＰＩＤ控制器及设计方法，仿真结果表明与ＰＩＤ系统比较，模糊自整定ＰＩＤ超调量减小，抗环境变化能力强。较好地解决了对象结构时变系统控制适应能力