自动化立体仓库堆垛起重机机械设计开题报告VIP

欢迎下载本文档参考使用，如果有疑问或者需要CAD图纸的请联系q1484406321编号无锡太湖学院毕业设计（论文）相关资料题目： FMS自动化立体仓库 堆垛起重机机械结构设计 信机 系 机械工程及自动化专业学 号： 09230102 学生姓名： 赵 磊 指导教师： 尤丽华 （职称：副教授 ） 2013年5月25日目 录一、毕业设计（论文）开题报告二、毕业设计（论文）外文资料翻译及原文三、学生“毕业论文（论文）计划、进度、检查及落实表”四、实习鉴定表无锡太湖学院毕业设计（论文）开题报告题目： FMS自动化立体仓库 堆垛起重机机械结构设计 信机 系 数 控 专业学 号： 00923102 学生姓名： 赵 磊 指导教师： 尤丽华 （职称：教授 ） 2012年11月28日 课题来源本课题来源于工程生产实际，应教学要求，使我们在学习期间接触到一些先进的技术装备和控制技术等重要知识，配合有关课程开设“柔性制造系统综合实验”的需求，培养独立进行科学研究、综合分析思考以及实际动手的能力，此毕业设计由此设立。科学依据目前自动化仓库在发达国家已相当普遍，日本是自动化仓库发展最快，建造数量最多的国家，此外美国、德国、瑞士、意大利、英国、和法国等国家也建造了许多自动化仓库，发展至今，自动化仓库在设计、制造自动化控制盒计算机管理方面的技术也日趋成熟。50年代初，美国美国出现了采用桥式堆垛起重机的立体仓库。60年代初，出现了司机操作的香道式堆垛起重机立体仓库。1963年美国率先在立体仓库中采用计算机控制技术，建立了世界上第一座计算机控制的立体仓库。进入80年代，我国对老式仓库进行技术改造，开始采用自动化立体仓库。1980年由北京机械工业自动化研究所等单位研制建成我国第一座自动化立体仓库，在北京汽车制造厂投产。据不完全统计，目前我国已建成立体仓库已有300座左右，其中全自动的立体仓库有50多座，其中高度在12米以上的大型立体仓库有8座，主要集中在传统优势行业。在此基础上我国对仓库的研究也向着智能化的方向发展，但是目前还处于自动化仓储的推广和应用阶段。研究内容 1.完成FMS自动化立体仓库堆垛起重机机械结构设计的总体设计； 2.完成机架、行走机构、提升机构和载货台、货叉等关键部件设计； 3.绘制相应的二维总装图及主要零部件图纸； 4.设计工作量要求：至少完成A0图纸2张和一份40页以上的毕业论文； 5.查阅相关外文资料并完成不少于8000字符的外文资料翻译； 6.完成一份毕业设计实习报告。拟采取的研究方法、技术路线、实验方案及可行性分析本文从实际问题出发，以现有设备为依托，先确定出堆垛机的总体结构及各部分的结构草图，然后运用理论力学、材料力学、机械设计、制造技术等专业知识，并查阅相关设计手册，对其机械部分进行了详细的设计计算，包括：机架、行走机构、提升机构、载货台和货叉伸缩机构等。设计过程中，以实现堆垛机的机械性能为目的，在满足其强度、刚度、运行稳定性等要求的前提下，综合考虑结构的合理性和所选材料的经济性，力求达到高质量、低成本。可行性分析：由此可见，该设计方案切实可行。研究计划及预期成果研究计划：大致分为如下几个阶段。第一阶段，熟悉老师提供的设计资料，设计参数等，仔细阅读任务书，理清设计思路，搜集资料了解研究目标，构思设计方案。第二阶段，进行方案实施，根据设计参数进行计算，按设计要求进行设计使之满足生产实际，完成设计。第三阶段，制定检测方案及检测方法从而进行校核，改善不满足要求的设计,最后完成图纸。预期成果： 巷道堆垛起重机满足所给自动化立体仓库中各项要求,如:在规定货架上自由提取，存放货物以及完成与输送系统的配合，速度控制严格满足稳定性的要求等。实现最优化设计。特色或创新之处国内外其它行业采用自动化仓库的情况已经充分证明，使用自动化立体仓库能够产生巨大的社会效益和经济效益。这些效益主要表现在以下几个方面： 1.搞层货架存储 由于使用高层货架，存储区可以大幅度地向空间发展，充分利用仓库地面和空间，因此节省了库存占地面积，提高了空间利用率。 2.自动存取 自动化立体仓库使用机械和自动化设备，运行和处理速度快，提高了作业效率。 3.计算机控制与管理 计算机能够准确无误地对仓库的各种信息进行存储和管理，降低了操作人员的劳动强度，从而提高仓库的管理水平。 4.作业效率明显提高 能充分保证“先进先出”的合理作业原则。由于计算机管理、自动作业，可以方便地实施货位和账目的科学管理，改善库存结构，避免盲目压货，并改善劳动环境。 5.节约费用 随着经济的高速的发展，我国有关行业开始重视立体库的研究，对于促进传统观念的转变、提高现代化物流意识，形成新型的商品流通产业等方面均产生了强劲的推动作用。已具备的条件和尚需解决的问题已具备的条件：设计过程中所需要的几种软件、相关搜集资料的网站。尚需解决的问题：相关文献资料的缺乏，对一些结构设计部分的具体设计指导，以及三维软件的高级运用技巧。指导教师意见指导教师（签名）： 年 月 日系主任（签名）： 年 月 日英文原文：Realization of Neural Network Inverse System with PLC in Variable Frequency Speed-Regulating System 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 is designed to get high performance. Using PLC, a neural network inverse system can be realized in 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.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 123, 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 45 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 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 .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 traditional PI control and NN-inverse control.2.Neural Network Inverse System Control MethodThe 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 inverse is very difficult toobtain, even impossible. The engineering application of inverse system control dont meet the expectations. As neural network has non-linear approximate ability, especially for nonlinear 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. Mathematic Model of Induction Motor Variable FrequencySpeed-Regulating System and Its ReversibilityInduction motor variable frequency speed-regulating system supplied by the inverter of tracking current SPWM can be expressed by 5th 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 stators current, and are rotors flux linkage in(d,q)axis. is number of poles. is mutual inductance, and is rotors inductance. J is moment of inertia.is rotors time constant, and is load torque.In vector mode, thenSubstituted it into formula (1), then (2)Taking reversibility analyses of forum (2), thenThe state variables are chosen as followsInput variables areTaking 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. Realization Steps of Neural Network Inverse System4.1 Acquisition of the Input and Output Training Samples 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.4.2 The Construction of Neural Network 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 orderderivative 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 .Experiments and Results5.1 Hardware of the System 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 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 inverter in this system.5.2 Software Program5.2.1 Communication IntroductionMPI (Mu Point Interface) is a simple and inexpensive communication strategy using in slowly and non-large data transforming field. The data transforming between and PLC is not large, 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 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 , which can not obtain by reading from PLC or directly. So OPC technology can be used l to obtain the needed data between . Setting as OPC DA server, an OPC client is constructed in Excel by VBA. System real time data is and to Excel by, and then the data in Excel is transform to 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 procedure B. In neural network algorithm normalized the training samples is need to speed up the rate of n input and output data before the final training. 5.3 Experiment ResultsWhen speed reference is square wave signal with 100 seconds cycle, where the inverter is 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. Conclusion 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PLC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123,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45, 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