堆垛机英文资料翻译

翻 译 英文原文 RealizationRealization ofof NeuralNeural NetworkNetwork InverseInverse SystemSystem withwith PLCPLC inin VariableVariable FrequencyFrequency Speed RegulatingSpeed Regulating SystemSystem Abstract The variable frequency speed regulating system which consists of an induction motor and a general inverter and controlled by PLC is widely used in industrial field However for the multivariable nonlinear and strongly coupled induction motor the control performance is not good enough to meet the needs of speed regulating The mathematic model of the variable frequency speed regulating system in vector control mode is presented and its reversibility has been proved By constructing a neural network inverse system and combining it with the variable frequency speed regulating system a pseudo linear system is completed and then a linear close loop adjustor is designed to get high performance Using PLC a neural network inverse system can be realized in actural system The results of experiments have shown that the performances of variable frequency speed regulating system can be improved greatly and the practicability of neural network inverse control was testified 1 Introduction1 Introduction In recent years with power electronic technology microelectronic technology and modern control theory infiltrating into AC electric driving system inverters have been widely used in speed regulating of AC motor The variable frequency speed regulating system which consists of an induction motor and a general inverter is used to take the place of DC speed regulating system Because of terrible environment and severe disturbance in industrial field the choice of controller is an important problem In reference 1 2 3 Neural network inverse control was realized by using industrial control computer and several data acquisition cards The advantages of industrial control computer are high computation speed great memory capacity and good compatibility with other software etc But industrial control computer also has some disadvantages in industrial application such as instability and fallibility and worse communication ability PLC control system is special designed for industrial environment application and its stability and reliability are good PLC control system can be easily integrated into field bus control system with the high ability of communication configuration so it is wildly used in recent years and deeply welcomed Since the system composed of normal inverter and induction motor is a complicated nonlinear system traditional PID control strategy could not meet the requirement for further control Therefore how to enhance control performance of this system is very urgent The neural network inverse system 4 5 is a novel control method in recent years The basic idea is that for a given system an inverse system of the original system is created by a dynamic neural network and the combination system of inverse and object is transformed into a kind of decoupling standardized system with linear relationship Subsequently a linear close loop regulator can be designed to achieve high control performance The advantage of this method is easily to be realized in engineering The linearization and decoupling control of normal nonlinear system can realize using this method Combining the neural network inverse into PLC can easily make up the insufficiency of solving the problems of nonlinear and coupling in PLC control system This combination can promote the application of neural network inverse into practice to achieve its full economic and social benefits In this paper firstly the neural network inverse system method is introduced and mathematic model of the variable frequency speed regulating system in vector control mode is presented Then a reversible analysis of the system is performed and the methods and steps are given in constructing NN inverse system with PLC control system Finally the method is verified in experiments and compared with traditional PI control and NN inverse control 2 Neural2 Neural NetworkNetwork InverseInverse SystemSystem ControlControl MethodMethod The basic idea of inverse control method 6 is that for a given system an th integral inverse system of the original system is created by feedback method and combining the inverse system with original system a kind of decoupling standardized system with linear relationship is obtained which is named as a pseudo linear system as shown in Fig 1 Subsequently a linear close loop regulator will be designed to achieve high control performance Inverse system control method with the features of direct simple and easy to understand does not like differential geometry method 7 which is discusses the problems in geometry domain The main problem is the acquisition of the inverse model in the applications Since non linear system is a complex system and desired strict analytical inverse is very difficult to obtain even impossible The engineering application of inverse system control doesn t meet the expectations As neural network has non linear approximate ability especially for nonlinear complexity system it becomes the powerful tool to solve the problem a th NN inverse system integrated inverse system with non linear ability of the neural network can avoid the troubles of inverse system method Then it is possible to apply inverse control method to a complicated non linear system a th NN inverse system method needs less system information such as the relative order of system and it is easy to obtain the inverse model by neural network training Cascading the NN inverse system with the original system a pseudo linear system is completed Subsequently a linear close loop regulator will be designed 3 3 MathematicMathematic ModelModel ofof InductionInduction MotorMotor VariableVariable FrequencyFrequency Speed Speed RegulatingRegulating SystemSystem andand ItsIts ReversibilityReversibility Induction motor variable frequency speed regulating system supplied by the inverter of tracking current SPWM can be expressed by 5 th order nonlinear model in d q two phase rotating coordinate The model was simplified as a 3 order nonlinear model If the delay of inverter is neglected the model is expressed as follows 1 where denotes synchronous angle frequency and is rotate speed are stator s current and are rotor s flux linkage in d q axis is number of poles is mutual inductance and is rotor s inductance J is moment of inertia is rotor s time constant and is load torque In vector mode then Substituted it into formula 1 then 2 Taking reversibility analyses of forum 2 then The state variables are chosen as follows Input variables are Taking the derivative on output in formula 4 then 5 6 Then the Jacobi matrix is Realization of Neural Network Inverse System with PLC 7 8 As so and system is reversible Relative order of system is When the inverter is running in vector mode the variability of flux linkage can be neglected considering the flux linkage to be invariableness and equal to the rating The original system was simplified as an input and an output system concluded by forum 2 According to implicit function ontology theorem inverse system of formula 3 can be expressed as 9 When the inverse system is connected to the original system in series the pseudo linear compound system can be built as the type of 4 4 RealizationRealization StepsSteps ofof NeuralNeural NetworkNetwork InverseInverse SystemSystem 4 14 1 AcquisitionAcquisition ofof thethe InputInput andand OutputOutput TrainingTraining SamplesSamples Training samples are extremely important in the reconstruction of neural network inverse system It is not only need to obtain the dynamic data of the original system but also need to obtain the static date Reference signal should include all the work region of original system which can be ensure the approximate ability Firstly the step of actuating signal is given corresponding every 10 HZ form 0HZ to 50HZ and the responses of open loop are obtain Secondly a random tangle signal is input which is a random signal cascading on the step of actuating signal every 10 seconds and the close loop responses is obtained Based on these inputs 1600 groups training samples are gotten 4 24 2 TheThe ConstructionConstruction ofof NeuralNeural NetworkNetwork A static neural network and a dynamic neural network composed of integral is used to construct the inverse system The structure of static neural network is 2 neurons in input layer 3 neurons in output layer and 12 neurons in hidden layer The excitation function of hidden neuron is monotonic smooth hyperbolic tangent function The output layer is composed of neuron with linear threshold excitation function The training datum are the corresponding speed of open loop close loop first order derivative of these speed and setting reference speed After 50 times training the training error of neural network achieves to 0 001 The weight and threshold of the neural network are saved The inverse model of original system is obtained 5 5 Experiments Experiments andand ResultsResults 5 15 1 HardwareHardware ofof thethe SystemSystem The hardware of the experiment system is shown in Fig 5 The hardware system includes upper computer installed with Supervisory Control configuration software WinCC6 0 8 and S7 300 PLC of SIEMENS inverter induction motor and photoelectric coder PLC controller chooses S7 315 2DP which has a PROFIBUS DP interface and a MPI interface Speed acquisition module is FM350 1 WinCC is connected with S7 300 by CP5611 using MPI protocol The type of inverter is MMV of SIEMENS It can communicate with SIEMENS PLC by USS protocol ACB15 module is added on the inverter in this system 5 25 2 SoftwareSoftware ProgramProgram 5 2 1 Communication Introduction MPI MultiPoint Interface is a simple and inexpensive communication strategy using in slowly and non large data transforming field The data transforming between WinCC and PLC is not large so the MPI protocol is chosen The MMV inverter is connected to the PROFIBUS network as a slave station which is mounted with CB15 PROFIBUS module PPO1 or PPO3 data type can be chosen It permits to send the control data directly to the inverter addresses or to use the system function blocks of STEP7V5 2 SFC14 15 OPC can efficiently provide data integral and intercommunication Different type servers and clients can access data sources of each other Comparing with the traditional mode of software and hardware development equipment manufacturers only need to develop one driver This can short the development cycle save manpower resources and simplify the structure of the entire control system Variety data of the system is needed in the neural network training of Matlab which can not obtain by reading from PLC or WinCC directly So OPC technology can be used l to obtain the needed data between WinCC and Exce Setting WinCC as OPC DA server an OPC client is constructed in Excel by VBA System real time data is readed and writen to Excel by WinCC and then the data in Excel is transform to Matlab for offline training to get the inverse system of original system 5 2 2 Control Program Used STL to program the communication and data acquisition and control algorithm subroutine in STEP7 V5 2 velocity sample subroutine and storage subroutine are programmed in regularly interrupt A and the interrupt cycle chooses 100ms In order to minimum the cycle time of A to prevent the run time of A exceeding 100ms and system error the control procedure and neural network algorithm are programmed in main procedure B In neural network algorithm normalized the training samples is need to speed up the rate of convergence by multiplying a magnification factor in input and output data before the final training 5 35 3 ExperimentExperiment ResultsResults When speed reference is square wave signal with 100 seconds cycle where the inverter is running in vector mode The results show that the tracking performance of neural network control is better than traditional PI control When speed reference keeps in constant and the load is reduced to no load at 80 seconds and increased to full load at 120 seconds the response curves of speed with traditional PI control and neural network inverse control are shown in Fig 11 and 12 respectively It is clearly that the performance of resisting the load disturbing with neural network inverse control is better than the traditional PI control Speed response in PI control Speed response in neural network inverse control 6 6 ConclusionConclusion In order to improve the control performance of PLC Variable Frequency Speed regulating System neural network inverse system is used A mathematic model of variable frequency speed regulating system was given and its reversibility was testified The inverse system and original system is compound to construct the pseudo linear system and linear control method is design to control With experiment neural network inverse system with PLC has its effectiveness and its feasibility in industry application 中文译文 PLCPLC 变频调速的网络反馈系统的实现变频调速的网络反馈系统的实现 摘要 变频调速系统 包括一个异步电动机和通用逆变器 且 PLC 控制被广 泛地应用于工业领域 然而 对多变量 非线性和强耦合的异步电机的控制性能 却不足 不能很好地满足客户的调速要求 该数学模型的变频调速系统提出了矢 量控制方式 其可逆转性得到证实 通过构建一种基于神经网络的逆系统 并结 合变频调速系统 pseudo linear 系统被完成了 并且为了得到性能优良的系统 采用了一个线性闭环调节器 采用 PLC 神经网络逆系统在实际系统可以实现 实验结果表明变频调速系统的性能得到了很大的提高 并且神经网络反馈控制 的可行性得到了验证 1 导论 近年来 随着电力电子技术 微电子技术和现代控制理论 逐渐涉及到交流 电机系统 这些技术已经广泛应用于变频器调速的 AC 马达 变频调速系统 包括 一个异步电动机和通用逆变器 用来代替直流调速系统 由于在工业领域中的 糟糕的环境和严重的干扰 选择控制器是一个十分重要的问题 在文献 1 2 3 介绍了利用工业控制计算机和数据采集卡实现了神经网络反馈控制 工业控制 计算机的优势有较高的计算速度 庞大的记忆能力以及与其他软件良好的兼容 性等 但是工业控制计算机在工业应用上也有一些不足 比如运行不稳定 不可 靠及更恶劣的通信能力 可编程序控制器 PLC 控制系统是专为工业环境中的应 用而设计的 它的稳定性和可靠性好 PLC 控制系统 可以很容易地集成到现场 总线控制系统并得到高性能的通信结构 所以它在近年来被广泛地使用 并且深 受欢迎 该系统由普通的逆变器和异步电机组成 是一种复杂的非线性系统 传 统的 PID 控制策略 并不能满足要求和进一步控制 因此 如何加强系统的控制 性能是非常迫切的事情 神经网络逆系统 4 5 在未来几年里将是一种新型的控制方法 其基本 的想法是 对于一个给定的系统 原系统的逆系统是由一个动态神经网络引起的 对象信号和反馈信号的组合系统被转化成一种线性关系的解耦标准系统 随后 一个线性闭环调节器设计可以达到较高的控制性能 该方法的优点是在工程上 很容易实现 在线性化及其解耦控制正常的非线性系统能实现采用这种方法 把神经网络反馈结合到可编程序控制器 PLC 上就可以很容易地弥补不足的 问题 解决在 PLC 控制系统上的非线性耦合 这个组合可以促进神经网络反馈 付诸实践 来实现其全部的经济效益和社会效益 在这篇文章中 首先对神经网络反馈方法进行了介绍 并且描述了采用矢量 控制的变频调速系统的数学模型 然后是对反馈系统进行分析的的介绍 并给出 了关于 PLC 控制系统中构造 NN 反馈系统的方法和步骤 最后 该方法在实验中 被验证 并将传统的 PI 控制和 NN 反馈控制进行了对比 2 神经反馈网络控制方法 基本的反馈控制方法 6 就是 对于一个给定的系统 一种 th 由反馈方 法建立的完整的反馈系统 并结合反馈系统与原系统的特点 提出了一种解耦的 线性关系 以标准化体系 并命名为伪线性系统 随后 一个线性闭环调节器运行 并将达到较高的控制性能 当在 几何领域 讨论这些问题时 反馈系统控制方法并不像微分几何方 法 其特点是直接 简单 易于理解 主要的问题是怎样在应用软件中获得反 馈模型 由于非线性系统是一个复杂的系统 所以很难要求严格解析反馈信号 这甚至是不可能的 反馈系统控制在工程应用中不能达到期望值 作为神经网 络非线性逼近能力 尤其是对于非线性的复杂系统 它会是来解决问题的强大工 具 反馈系统集成了具有非线性逼近能力的反馈系统 其中具有非线性逼近能 力的反馈系统能够避免使用反馈方法带来的麻烦 这样就可能 运用反馈控制 方法去控制一个复杂的非线性系统 a th NN 反馈系统的控制方法只需要较 少的系统信息 比如与系统相关的命令 并且容易获得运行网络的反馈模型 原系统的层叠式的 NN 反馈系统 会形成一个伪线性系统 然后 一个线性闭环 调节校准器将工作 3 异步电机变频调速系统的数学模型和它的反馈性能 异步电机变频调速系统提供的跟踪电流正弦脉宽调制逆变器可以表示为非 线性模型在两相循环的协调 该模型简化为一个3 order非线性模型 如果忽略 逆变器的延迟 该模型表述如下 1 表示同步角频率 表示转速 表示定子的电流 表示转子在 qd 轴线上的不稳定部分 表示点的数量 表示互感系数 表示惯性转矩 表示转子的时间常数 表示负载转矩 用矢量模式 得 代进公式 1 得 2 可逆转性分析 2 得 3 4 可供选择的状态变量如下 输入变量 由公式 4 得出结果 得 5 6 然后雅可比矩阵 7 8 作为 所以并且系统是可逆的 相关的系统是 当变频器运行模式的变化 在矢量磁链的可以忽略的磁链 考虑到是恒定 等 于等级 原系统简化为一个输入和输出系统订立的 2 根据隐函数定理 公式 3 的反馈系统可以表达为 9 当反馈系统连续连接到原系统时 伪线性复合系统形成类型 4 网络反馈系统的实现步骤 4 1 输入与输出的运行样