【作者】 许小静；

【导师】 李京龙；

【作者基本信息】 西北工业大学 ， 材料加工工程， 2005， 硕士

【摘要】 逆变电源技术在电阻点焊领域的成功应用,为点焊设备的精密化、智能化提供了广阔的开发和应用前景。目前,以IGBT为开关器件的逆变主电路技术已经开发成熟,以新一代数字信号处理器(DSP)为主控单元所构建的硬件和软件控制系统,正在取代现有的单片机控制技术。本课题正是在这一背景下,提出并完成了软开关逆变点焊电源智能控制系统的设计与开发。 本文首先从硬件设计的角度,构建了基于DSP的基本系统,设计并搭建了外围电路,包括指令输入电路、采样输入电路、脉宽调制(PWM)输出电路、显示输出电路、以及保护电路。软件设计首先完成了PWM信号的输出与时序控制,并设计了键盘输入功能模块、LED显示功能模块、电流电压信号采样功能模块,以及模糊神经算法自学习软件功能模块。推导了点焊逆变过程正、逆模型,将模糊集合理论与人工神经网络结合,建立了恒流输出控制系统的网络结构。利用BP算法,采用先正弦函数输入后恒定输入的分段方法对模糊神经网络进行训练,实现了在线调整隶属函数参数,使控制系统具有感知和抑制外来干扰和过程变化的能力。 利用MATLAB软件,对整个系统进行了仿真控制。结果表明,模糊神经网络较好地实现了点焊恒流控制。在占空比为0.5,网压波动±20%时,纠偏过程不超过5个逆变周期,系统响应时间较快;电流控制误差不超过2%,稳态精度高。系统可以按需要设定时变参考输入值,从而能够很好地实现电流的缓升、缓降控制。比例因子KE、KEC、KDU选取,需要从响应速率和稳态性能等多方面协调考虑。在抑制超调和振荡的前提下,尽可能提高系统的响应速率。更多还原

【Abstract】 The successful application of power inverter technology in resistance spot welding leads to a more promising future for developing of spot welders in precision and intelligent. At present, the main circuit by using IGBT switch technique has developed perfectly. Being a new generation of microprocessor in control system, DSP (digital signal processor) is being selected to replace existing SCM (Single Chip Micyoco) technique. The subject was proposed from this background, in which the design and development of intelligent control systems for soft switch inverter of spot welding were implemented.The hardware system was constructed based on DSP system with peripheral circuit including command input circuit, sampling circuit, PWM (pulse-width modulation) output circuit , display circuit, and protection circuit. The software design implemented PWM output and sequence control module, keyboard input module, LED display module, current and voltage sampling module and fuzzy neural algorithm self study module. Based on obverse- and reverse-models of spot welding inverting process, which were derived theoretically, and combination of fuzzy set and artificial neural network, the control system of constant current output were constructed. By integrating sine-function input and then constant input in segmentation into BP algorithm, the fuzzy neural network was trained to realize parameter regulation online of the subjection function, which makes the control system perceivable and capable of suppressing the external interference.The entire system control was simulated by using MATLAB coding environment. The result shows that the fuzzy neural network realizes the constant current control with accuracy and quick responding. While the input voltage fluctuates ±20% and the occupation ratio equals 0.5, the deviation correcting time is less than 5 inverting cycles showing quick responding of the system, and the control error less than 2% showing accuracy in steady state.The system can also implement steady rising and descending control of the current according to the settings of time-depend variables. It is necessary to coordinate the aspects of responding speed and stability in steady state while selecting multipliers KE, KEC and KDU, in which over-regulating and surging of the system should be avoided while high responding speed is in pursuit.更多还原