自适应神经模糊推理结合PID控制的并联机器人控制方法

针对6自由度液压驱动并联机器人的精确控制问题,提出一种结合自适应神经模糊推理系统(ANFIS)和比例积分微分(PID)控制的机器人控制方法。首先,利用浮动坐标系描述法(FFRF)来模拟机器人柔性组件,并构建并联机器人的拉格朗日动力学模型。然后,根据模糊推理中的模糊规则来自适应调整PID控制器参数。最后,利用神经自适应学习算法使模糊逻辑能计算隶属度函数参数,从而使模糊推理系统能追踪给定的输入和输出数据。将该控制器与传统PID控制器、模糊PID控制器进行比较,结果表明,ANFIS自整定PID控制器大大减小了末端器位移误差,能很好的控制并联机器人末端机械手的运动。     

Hybrid fuzzy control of robotics systems

This paper presents a new approach towards optimal design of a hybrid fuzzy controller for robotics systems. The salient feature of the proposed approach is that it combines the fuzzy gain scheduling method and a fuzzy proportional-integral-derivative (PID) controller to solve the nonlinear control problem. The resultant fuzzy rule base of the proposed controller can be decomposed into two layers. In the upper layer, the gain scheduling method is incorporated with a Takagi-Sugeno (TS) fuzzy logic controller to linearize the robotics system for a given reference trajectory. In the lower layer, a fuzzy PID controller is derived for all the locally linearized systems by replacing the conventional PI controller by a linear fuzzy logic controller, which has different gains for different linearization conditions. Within the guaranteed stability region, the controller gains can be optimally tuned by genetic algorithms. Simulation studies on a pole balancing robot and a multilink robot manipulator demonstrate the effectiveness and robustness of the proposed approach.     

Fuzzy PID control of space manipulator for both ground alignment and space applications

Considering gravity change from ground alignment to space applications, a fuzzy proportional-integral-differential (PID) control strategy is proposed to make the space manipulator track the desired trajectories in different gravity environments. The fuzzy PID controller is developed by combining the fuzzy approach with the PID control method, and the parameters of the PID controller can be adjusted on line based on the ability of the fuzzy controller. Simulations using the dynamic model of the space manipulator have shown the effectiveness of the algorithm in the trajectory tracking problem. Compared with the results of conventional PID control, the control performance of the fuzzy PID is more effective for manipulator trajectory control.     

不确定性操作臂的混合模糊P+ID控制的实验研究

提出了一种机械臂的混合模糊控制策略,它由一企增量模糊逻辑控制器和传统的积分－微分管制器构成,简称ＰｕｚｚｙＰ＋ＩＤ控制器,与传统的ＰＩＤ控制器相比,设计ＦｕｚｚｙＰ＋ＩＤ控制器时,只需多调节一个附加参数,我们应用ＦｕｚｚｙＰ＋ＩＤ控制器控制几何参数变化的两连杆机构臂ＥＤＤＡ,ＥＤＤＡ的阶跃和跟踪控制的实验结果表明：ＦｕｚｚｙＰ＋ＩＤ控制器更有效和更鲁棒。     

Adaptive fuzzy force control of manipulators with unknown environment parameters

This article proposes an adaptive fuzzy control scheme for explicit force control of a robot manipulator in contact with an environment whose parameters are unknown and vary considerably. The scheme consists of three main components: a reference force model describing the desired behavior of the force control system, a fuzzy force controller that determines the adjustment to the position control loop, and a fuzzy learning and adaptation mechanism that modifies the fuzzy force controller according to the difference between the actual and desired force responses. The modification is performed by shifting and contracting/expanding the membership functions of the fuzzy sets associated with the consequent rules of the fuzzy force controller. It is demonstrated, through simulations of a two-link manipulator and a 6-DOF industrial robot, that the scheme is capable force tracking despite wide parameter variations, such as when the environment stiffness changes by several orders of magnitude. © 1997 John Wiley & Sons, Inc.     

模糊预补偿PID控制

利用模糊控制理论的非线性特性，在工业过程控制中将其与PID控制结合，能改善常规PID控制器的控制性能。给出了一种基于模糊逻辑规则作为预补偿机制的PID控制。采用模糊推理和改进Z—N方法结合的方法。通过使用模糊逻辑规则作为预补偿器调整控制信号。校正PID控制器产生的较大超调，以达到改善PID控制器控制性能的目的。经仿真证明。该方法可克服传统Z—N方法产生的过大超调和较长调整时间的不足，同时又保持较好的抗负荷扰动能力和鲁棒性。     

PCR温度控制技术的研究进展

首先简述了PCR的基本原理,接着综述了几种常用的用于PCR的温度控制器,包括常规PID控制器、改进的PID控制器和模糊逻辑控制器,最后,将模糊逻辑应用于PID控制,提出了简练的模糊逻辑规则,设计了一套用于PCR的参数自整定模糊逻辑PID温控系统,得到了令人满意的温度控制效果.     

自适应模糊PID控制器的设计

目前的工业过程控制中被控对象往往都是非线性、时变的系统,常规PID控制对于这样系统的控制效果不是很理想。为此,提出将模糊技术与PID控制相结合的控制方式,设计出自适应模糊PID控制器。利用Matlab中模糊逻辑控制工具箱设计模糊控制器,在Simulink环境中实现了该自适应模糊PID控制器的仿真。仿真结果表明,该模糊PID控制具有控制灵活、响应快和适应性能强的优点。     

模糊免疫PID算法在温度控制系统中的应用研究

该文针对温度控制系统非线性、大滞后、参数时变的特征,设计了模糊免疫PID控制器。该控制器结合了模糊逻辑、免疫机理以及PID调节的各种优点,既具有模糊控制的非线性作用,又具有免疫控制的自适应能力,同时还具有PID控制的广泛适用性。文章介绍了模糊免疫PID控制器的控制原理和设计方法,在Matlab中编写函数仿真,结果表明该控制器能够实现持续干扰情况下的闭环鲁棒稳定,并使系统呈现良好的动态和静态性能。     

A Two-Layer Robot Controller Design Using Evolutionary Algorithms

The results obtained by a rule-based proportional, integral, derivative (PID) precompensator controller applied to a two-joint manipulator are discussed. The end effector is made to follow a specified trajectory obtained from the inverse kinematics by an appropriate design of a fuzzy control law. The desired trajectory is determined by the values of the joint variables and the structural kinematics parameters of the manipulator. The performance of the PID controller is exploited here to build a fuzzy precompensator that will enhance the conventional PID and to obtain better performances and results. The fuzzy rule base of the precompensator designed is found by associating two evolutionary algorithms that search for the optimal solution.     

模糊自适应控制器的设计及其仿真

提出了一种参数自适应模糊PID控制器,将模糊控制器和PID控制器结合在一起,利用模糊逻辑控制,并把MATLAB中的Fuzzy Toolbox和SIMULINK有机结合起来,实现了PID控制器参数在线自调整.进一步完善了PID控制器的性能,提高了系统的控制精度.仿真结果表明:该控制器明显改善了控制系统的动态性能,参数自适应模糊PID控制器能使系统达到满意的控制效果,对进一步应用研究具有很好的参考价值.     

基于半导体制冷片的超速离心机温度控制模块设计

设计了超速离心机的温度控制模块,采用了模糊PID算法,通过PID参数的自动整定,对半导体制冷片的电压进行调整,从而控制半导体制冷片的制冷或制热效率,能对超速离心机离心室内的温度进行精确并且快速的控制.在MATLAB的simulink环境下进行仿真后分析可知,模糊PID算法兼具常规PID算法和模糊控制的优点,能够有效提高...     

Design of a hybrid fuzzy logic proportional plus conventionalintegral-derivative controller

Presents approaches to the design of a hybrid fuzzy logic proportional plus conventional integral-derivative (fuzzy P+ID) controller in an incremental form. This controller is constructed by using an incremental fuzzy logic controller in place of the proportional term in a conventional PID controller, By using the bounded-input/bounded-output “small gain theorem”, the sufficient condition for stability of this controller is derived. Based on the condition, we modify the Ziegler and Nichols' approach to design the fuzzy P+ID controller. In this case, the stability of a system remains unchanged after the PID controller is replaced by the fuzzy P+ID controller without modifying the original controller parameters. When a plant can be described by any modeling method, the fuzzy P+ID controller can be determined by an optimization technique. Finally, this controller is used to control a nonlinear system. Numerical simulation results demonstrate the effectiveness of the fuzzy P+ID controller in comparison with the conventional PID controller, especially when the controlled object operates under uncertainty or in the presence of a disturbance     

薄煤层采煤机调高系统PID控制的研究与仿真

为了使采煤机滚筒调高系统在复杂工况下具有比较强的抗干扰能力,设计了模糊PID控制器.简单介绍采煤机的控制原理,建立系统数学模型,设计模糊控制器,对系统的稳定性进行判断.在稳定的控制系统下,建立仿真模型,给系统输入实际开采情况的三向力和三向力矩,比较无控制时、PID控制下、模糊PID控制下的输出曲线.结果表明:模糊PID控制系统的力波动范围最小,其次是PID控制,最差的是无PID控制.结论是模糊PID的控制效果最好,可以更好地满足系统的要求.     

基于模糊PID的铀水冶吸附原液流量控制研究

在铀水冶原液吸附的过程中,原液流入吸附塔的流量控制,关系到铀水吸附的效率与质量。本文通过对铀水冶吸附原液流量控制系统的研究,建立了系统的数学模型。将模糊控制与PID控制结合在一起,设计了模糊PID控制器,通过模糊控制器输出对PID参数进行在线调整。经过实验研究,模糊PID控制系统比常规PID控制系统相应快,调整能力强,鲁棒性好,有效的改善了控制效果。     

电动车转向系统的模糊控制仿真

本仿真的背景基于全国大学生智能车竞赛,研究电动车在随机轨迹上的跟踪转向控制。由于电动车转向控制系统的不确定性,使用传统PID控制无法满足控制需求并可能产生调节振荡。模糊控制技术基于模糊数学理论,通过模拟人的近似推理和综合决策过程,使控制算法的可控性、适应性和合理性得到提高。根据电动车转向控制的特点设计了一种简化了的模糊控制器,并与传统PID控制器进行比较。通过在MATLAB下搭建传统PID控制器模型和简化了的模糊控制器模型,最终分析和比较了该模糊控制器和传统PID控制器的不同控制效果。     

Fuzzy bang–bang relay control of a single-axis active magnetic bearing system

Active magnetic bearings (AMB) are presently being utilized in various classes of rotating machinery. Although the rotor-AMB systems are open loop unstable, they are easily stabilized using feedback control schemes of which the PID controller is the most commonly used. The PID controller is however only effective at the vicinity of the rotor’s equilibrium position where the dynamics of the rotor-AMB system is linearized. Significant deviation of the rotor’s motion from this equilibrium position may occur due to large imbalance forces. In this situation, the nonlinearity in AMBs, which arises from the relationship between the electromagnetic force, coil current and air gap, may render the PID controller ineffective. For the control of nonlinear systems, artificial intelligence techniques such as fuzzy and hybrid techniques are effective. In this paper, a new fuzzy controller is proposed for the control of a single-axis AMB system. This controller is based on the bang–bang scheme, which is an old but effective technique to control nonlinear systems in optimal time. The performance of the proposed integrated fuzzy bang–bang relay controller (FBBRC) was found to be superior to that of the optimized PD controller and the conventional fuzzy logic controller. Comparison of the FBBRC with the fuzzy logic controller cascaded with a hard limiter (FBBC) relay revealed almost equal performance. High frequency chattering was however observed in the steady-state response of the FBBC. Such chattering is known to cause instability and distortion in the amplifiers that are used to supply current to the magnetic bearing actuators.     

Developments of fuzzy PID controllers

Abstract: This paper describes the development and tuning methods for a novel self-organizing fuzzy proportional integral derivative (PID) controller. Before applying fuzzy logic, the PID gains are tuned using a conventional tuning method. At supervisory level, fuzzy logic readjusts the PID gains online. In the first tuning method, fuzzy logic at the supervisory level readjusts the three PID gains during the system operation. In the second tuning method, fuzzy logic only readjusts the proportional PID gain, and the corresponding integral and derivative gains are readjusted using the Ziegler–Nichols tuning method while the system is in operation. For the compositional rule of inferences in the fuzzy PID and the self-organizing fuzzy PID schemes two new approaches are introduced: the min implication function with the mean of maxima defuzzification method, and the max-product implication function with the centre of gravity defuzzification method. The fuzzy PID controller, the self-organizing fuzzy PID controller and the PID controller are all applied to a non-linear revolute-joint robot arm for step input and path tracking experiments using computer simulation. For the step input and path tracking experiments, the novel self-organizing fuzzy PID controller produces a better output response than the fuzzy PID controller; and in turn both controllers exhibit better process output than the PID controller.     

Laboratory evaluation of fuzzy controllers

The paper discuss various possibilities of applying fuzzy set theory in Real-Time control. Three types of controllers are used for comparing their behaviour in control experiments on scale laboratory model of a real dynamic system. The objective of the first experiment is to compare the performance of a fuzzy logic, a PID and an LQ controller in terms of stability, response time and steady-state error in various control tasks. The second experiment evaluates fuzzy logic adaptation of a PID controller, utilizing some heuristic knowledge.