New modifying self-organizing fuzzy controller for robotic motion control

The precise motion control of robotic manipulators is important in improving productivity and quality. However, robotic manipulators are multivariable nonlinear dynamic systems. Designing a model-based controller for robotic system control is difficult because its mathematical model is hard to accurately establish. This study proposed a self-organizing fuzzy controller (SOFC) to control a robotic system and evaluate its control performance. The SOFC continually updates the learning strategy in the form of fuzzy rules during the control process. The learning rate and the weighting distribution value of the controller are hard to regulate, so its fuzzy control rules may be modified to such an extent that the system response generally causes oscillatory phenomena. Two fuzzy logic controllers were designed according to the system output error and the error change, and introduced to the SOFC to determine the appropriate parameters of the learning rate and the weighting distribution, in order to eliminate this oscillation. This new modifying self-organizing fuzzy controller (NMSOFC) can effectively improve the control performance of the system, reduce the time consumed to establish a suitable fuzzy rule table, and support practical and convenient fuzzy controller applications. To confirm the applicability of the proposed intelligent controllers, this work retrofitted an old robot for a control system to evaluate the feasibility of motion control. Experiment results indicate the NMSOFC has better control performance in reducing the tracking errors of the joint-space trajectories and the positions, and requires less computational time than does the traditional fuzzy controller.     

基于模糊推理的热轧带钢宽度控制

带钢热连轧宽度控制过程是一个复杂的非线性过程、难以建立精确的数学模型，对于传统的数学模型的轧机计算很难实现精度要求，本文设计一种用于宽度控制的模糊控制器，模糊控制器根据推理规则获得控制系统的查询表，用Matlab语言的Simulink仿真工具，进行了常规控制与模糊控制的动态性能的仿真比较，结果表明模糊控制可明显提高宽度控制系统的动态性能。     

基于模糊推理的热轧带钢宽度控制

带钢热连轧宽度控制过程是一个复杂的非线性过程、难以建立精确的数学模型,对于传统的数学模型的轧机计算很难实现精度要求.设计一种用于宽度控制的模糊控制器.模糊控制器根据推理规则获得控制系统的查询表.并用Matlab语言的Simulink仿真工具,进行了常规控制与模糊控制的动态性能的仿真比较,结果表明模糊控制可明显提高宽度控制系统的动态性能.     

Discrete-time direct adaptive control for robotic systems based on model-free and if–then rules operation

An adaptive controller for a class of nonlinear discrete-time systems is proposed for robotic systems under the assumption that the parameters and structure of system dynamics are all unknown. This controller is designed with the concept of model-free adaptive control requiring only the input–output of the unknown plant. The robotic system has been generalized to be a nonaffine discrete-time system under reasonable assumptions. The adaptive scheme called fuzzy rules emulated network (FREN) is implemented as a direct controller. The IF–THEN rules for FREN have been defined by the knowledge according to the relation between input and output of the robotic system without any compensator for the unknown mathematical model or nonlinearities. The underlying physical specifications of robotic system such as the operating range, maximum joint velocity, and so on have been considered to initialize the membership functions and adjustable parameters of FREN. The adaptation scheme is developed according to convergence analysis established for both adjustable parameters and the tracking error. The performance of the proposed controller is validated by the experimental system with a 7-degrees-of-freedom robotic arm operated in velocity-mode control.     

A Self-Organising Fuzzy Logic Controller for a Coordinate Machine

For a 3D coordinate measurement system, the dynamic accuracy of the moving table will influence the measuring accuracy directly. If a classical PID controller were designed for this measuring table without an accurate mathematical model, the gain parameters may need to be regulated frequently by trial-and-error to obtain the precise motion control objective, good adaptability, and robustness. In this paper, a model-free fuzzy controller and a self-organising fuzzy controller (SOFC) were employed to eliminate the above controller design problems and improve the tracking control accuracy. The control performances of these intelligent control strategies were compared, based on the experimental results. The SOFC has the best tracking accuracy and its learning ability significantly reduces the trial-and-error design effort of a traditional fuzzy controller.     

Self-organizing fuzzy control of constant cutting force in turning

Constant force control is gradually becoming an important technique in the modern manufacturing process. Especially, constant cutting force control is a useful approach in increasing the metal removal rate and the tool life for turning systems. However, turning systems generally have nonlinear with uncertainty dynamic characteristics. Designing a model-based controller for constant cutting force control is difficult because an accurate mathematical model in the turning system is hard to establish. Hence, this study employed a model-free fuzzy controller to control the turning system in order to achieve constant cutting force control. Nevertheless, the design of the traditional fuzzy controller (TFC) presents difficulties in finding control rules and selecting an appropriate membership function. Moreover, the database and fuzzy rules of a TFC are fixed after the design step and then cannot appropriately regulate ones real time according to the system output response and the desired control performance. To solve the above problem, this work develops a self-organizing fuzzy controller (SOFC) for constant cutting force control to evaluate control performance of the turning system. The SOFC continually updates the learning strategy in the form of fuzzy rules, during the turning process. The fuzzy rule table of this SOFC can be begun with zero initial fuzzy rules which not only overcome the difficulty in the TFC design, but also establish a suitable fuzzy rules table, and support practically convenient fuzzy controller applications in turning systems control. To confirm the applicability of the proposed intelligent controllers, this work retrofitted an old lathe for a turning system to evaluate the feasibility of constant cutting force control. The SOFC has a better control performance in constant cutting force control than does the TFC, as verified in experimental results.     

Self-organizing fuzzy control of constant cutting force in turning

Constant force control is gradually becoming an important technique in the modern manufacturing process. Especially, constant cutting force control is a useful approach in increasing the metal removal rate and the tool life for turning systems. However, turning systems generally have nonlinear with uncertainty dynamic characteristics. Designing a model-based controller for constant cutting force control is difficult because an accurate mathematical model in the turning system is hard to establish. Hence, this study employed a model-free fuzzy controller to control the turning system in order to achieve constant cutting force control. Nevertheless, the design of the traditional fuzzy controller (TFC) presents difficulties in finding control rules and selecting an appropriate membership function. Moreover, the database and fuzzy rules of a TFC are fixed after the design step and then cannot appropriately regulate ones real time according to the system output response and the desired control performance. To solve the above problem, this work develops a self-organizing fuzzy controller (SOFC) for constant cutting force control to evaluate control performance of the turning system. The SOFC continually updates the learning strategy in the form of fuzzy rules, during the turning process. The fuzzy rule table of this SOFC can be begun with zero initial fuzzy rules which not only overcome the difficulty in the TFC design, but also establish a suitable fuzzy rules table, and support practically convenient fuzzy controller applications in turning systems control. To confirm the applicability of the proposed intelligent controllers, this work retrofitted an old lathe for a turning system to evaluate the feasibility of constant cutting force control. The SOFC has a better control performance in constant cutting force control than does the TFC, as verified in experimental results.     

基于改进型BP神经网络PID控制器在转台系统中的应用研究

为改善转台系统性能,针对传统的PID控制参数难以获得较理想的控制效果,设计了一种基于改进型BP神经网络的PID控制器。介绍了PID控制器的结构和BP神经网络算法描述,利用最小二乘法和神经网络建立被控对象的预测数学模型,并用该模型所计算的预测输出取代预测输出的实测值,对基于BP网络的PID控制器的权值调整算法进行改进。以某转台模型为对象,建立了转台控制系统的数学模型并对其进行仿真。仿真结果表明,改进型BP神经网络PID控制器具有良好的控制效果,跟踪精度高、性能稳定及鲁棒性强,能更为有效地应用到转台系统中。     

电子束焊熔深神经网络模糊控制系统建模与仿真

研究钛合金电子束焊熔深控制系统建模问题。在分析电子束焊接特点的基础上,设计三因素五水平正交试验,通过试验得到不同焊接参数下熔宽和熔深的值,将熔宽和熔深的值作为训练样本对神经网络进行训练,建立以熔宽为输入,以熔深为输出的误差反向传播(Error back propagation,BP)神经网络模型,该模型由一个S型函数隐含层加上一个线性输出层组成。针对熔深数学模型难以获得的情况,设计以熔深的偏差和偏差变化率为输入变量,焊接电流的变化量为输出变量的模糊控制器,该控制器有9条模糊控制规则。将BP神经网络模型和模糊控制器结合起来建立钛合金电子束焊熔深控制系统模型,并且采用单位阶跃信号对该模型进行仿真试验,试验结果表明所设计的控制系统动态性能和稳态性能良好。     

基于小波神经网络的电动负载模拟器的复合控制

为了提高电动负载模拟器的信号跟踪精度和多余力矩抑制能力,在分析系统结构和工作原理的基础上建立了电动负载模拟器系统的完整数学模型。针对电动负载模拟器中存在的力矩跟踪精度问题,提出了一种前馈补偿和基于小波网络的PID控制相结合的复合控制方法。利用改进的前馈补偿法抑制多余力矩,基于小波网络的PID控制器可以在线调整PID参数补偿系统的非线性环节,提高系统动态性能。仿真结果表明,复合控制器对多余力矩有良好的抑制效果,跟踪精度满足要求,和传统PID控制相比,系统鲁棒性得到显著提高。     

一种改进的模糊CMAC神经网络车辆自主避障算法

考虑到模糊控制算法在自主避障上的缺陷,设计了一种改进的模糊CMAC神经网络车辆自主避障算法。采用模糊CMAC神经网络(FCMAC)的各层节点来实现模糊控制器变量的输入、模糊化、模糊逻辑的前提条件匹配运算、模糊量的归一化、控制量的输出和控制规则的调整,借助神经网络的自学习能力来完成模糊控制。通过仿真实验,验证了改进的模糊CMAC神经网络算法的可行性和有效性。     

连铸结晶器非正弦振动系统模糊神经网络跟踪控制

针对结晶器非正弦振动液压伺服系统参数易变、模型不确定这一特点，对模型未知的系统用神经网络模型逼近，并采用改进的递阶遗传算法对神经网络的权值和结构同时进行训练，实现系统模型的精确辨识；将模糊控制与神经网络相结合，提出一种模糊神经网络控制方法，实现连铸结晶器非正弦振动系统的跟踪控制。仿真验证了该方案能提高非正弦振动系统跟踪控制性能和鲁棒性，且易于工程实现。     

电液伺服系统的模糊神经网络并行自学习鲁棒控制

基于模糊神经网络结构，提出了一种复合式控制方案，解决了传统自适应控制中模型的在线辨识和控制器的在线设计问题。达到了对不确定非线性系统的高精度输出跟踪；通过引入运行监控器，解决了模糊神经网络实时性差的问题；同时，利用一个鲁棒反馈控制器，来保证模糊神经网络学习初期闭环系统的稳定性。应用到电液力伺服加载系统中，获得满意控制效果。     

电液伺服系统RBF神经网络滑模控制

针对阀控液压缸位置伺服系统非线性导致模型参数确定困难及干扰问题，在分析三阶位置控制的电液控制系统原理及模型的基础上，引入神经网络的RBF 径向基控制模型和自适应滑模算法，同时考虑了非1负反馈参数，建立了基于RBF 神经网络滑模控制的电液伺服控制系统数学模型。通过选取合适的Lyapunov 函数，分析了系统稳定性，解决了参数未定及挠动情况下的电液伺服系统控制器设计问题。仿真结果证明，所设计的控制器使系统的输出对给定信号的跟踪精度高，响应快，具有较强的鲁棒性。     

机器人焊缝跟踪神经网络控制的研究

研究神经网络技术在弧焊机器人焊缝跟踪过程中的应用,通过神经网络在笛卡尔空间轨迹的补偿作用,确定出基于笛卡尔空间参考轨迹控制的机器人焊缝跟踪神经网络控制器。与传统的关节计算力矩法相比,所设计的神经网络控制器具有良好的控制特性及较强的鲁棒性,焊缝跟踪精度得到了显著的提高。     

伺服电机驱动的液压动力系统及其神经网络自适应优化控制

针对传统液压系统存在的高能耗、低响应特点,采用节能型液压动力源-永磁伺服电机直接驱动定量泵,以取代原有的异步电机驱动液压动力源,从而形成一种新型的节能、响应快速、易实现闭环控制的液压动力系统。由于实际液压系统随机干扰严重,具有多变量、非线性、强耦合的特征,难以建立较准确的数学模型,常规的PID控制算法很难满足液压系统高精度控制的要求,因此提出基于PSO与BP混合优化前向神经网络 PID自适应控制方法,实现液压系统在典型工况下流量的精确控制。PID控制器的参数采用神经网络进行自适应整定,神经网络的权值采用混合优化算法进行调整,通过神经网络的自学习能力寻找最佳的P、I、D非线性组合控制律,以增强液压系统对工况变化的适应能力。仿真和实验结果表明,该控制方法跟踪速度快、超调小、鲁棒性强,从而为液压系统流量高精度控制提供了一种新方法。     

基于小波神经网络辨识器的模糊神经自适应控制在VAV空调系统中的应用

针对温度控制的大惯性、大滞后、非线性特点，提出采用基于小波神经网络辨识器的模糊神经自适应控制的中央空调房间温度控制器的设计方案。由于小波神经网络的非线性映射能力比一般神经网络要强，所以基于小波神经网络的辨识器可以获得很高的辨识精度。而且，模糊神经自适应控制器随着系统动态特性的改变可以在线改变其控制规则，从而进行客观准确的控制。与普通模糊控制方法相比较，仿真试验说明了系统设计的有效性。     

某越野汽车磁流变半主动悬架变论域模糊控制

为了解决半主动悬架传统变论域模糊控制器过度依赖经验规则的问题,提出了一种基于模糊神经网络的变论域T-S模糊控制策略。首先,根据磁流变减振器阻尼特性的实验结果,建立基于自适应模糊神经网络的减振器阻尼力模型及1/2车辆半主动悬架动力学模型;其次,建立悬架系统T-S模糊控制器,同时为了实时调节T-S模糊控制器变量的论域,采用模糊神经网络结构描述伸缩因子的变化。仿真结果表明,笔者提出的变论域模糊控制策略能够有效提高车辆行驶平顺性和操作稳定性。     

自适应神经模糊推理的四轮转向车辆转向控制研究

模糊规则的建立和隶属度函数的确定是设计模糊系统的难题。基于神经网络和模糊逻辑的自适应神经模糊推理系统,能够从仿真数据中自动提取出If-Then规则。并在Matlab/Simulink软件中,建立包含侧向运动、横摆运动、侧倾运动三个自由度的四轮转向车辆三自由度动力学模型。将得到的If-Then规则读取到模糊控制器中和三自由度车辆模型进行联合仿真。其中模糊控制器以方向盘转角、方向盘转角速度和车速作为输入,后轮转角作为输出。最后与前轮转向的车辆进行转向盘角阶跃仿真对比。仿真分析结果表明:基于自适应神经模糊推理系统建立的后轮转角模糊控制器能够实现理想的质心侧偏角和车辆横摆角速度响应,提高了车辆的操纵稳定性。     

Neuro-fuzzy control strategy for an offshore steel jacket platform subjected to wave-induced forces using magnetorheological dampers

Magnetorheological (MR) damper is a prominent semi-active control device to vibrate mitigation of structures. Due to the inherent non-linear nature of MR damper, an intelligent non-linear neuro-fuzzy control strategy is designed to control wave-induced vibration of an offshore steel jacket platform equipped with MR dampers. In the proposed control system, a dynamic-feedback neural network is adapted to model non-linear dynamic system, and the fuzzy logic controller is used to determine the control forces of MR dampers. By use of two feedforward neural networks required voltages and actual MR damper forces are obtained, in which the first neural network and the second one acts as the inverse dynamics model, and the forward dynamics model of the MR dampers, respectively. The most important characteristic of the proposed intelligent control strategy is its inherent robustness and its ability to handle the non-linear behavior of the system. Besides, no mathematical model needed to calculate forces produced by MR dampers. According to linearized Morison equation, wave-induced forces are determined. The performance of the proposed neuro-fuzzy control system is compared with that of a traditional semi-active control strategy, i.e., clipped optimal control system with LQG-target controller, through computer simulations, while the uncontrolled system response is used as the baseline. It is demonstrated that the design of proposed control system framework is more effective than that of the clipped optimal control scheme with LQG-target controller to reduce the vibration of offshore structure. Furthermore, the control strategy is very important for semi-active control.