【正文】 ()即是 () 。第四節(jié) 本章小結(jié) 本章介紹了幾種控制器的的設(shè)計和調(diào)試，結(jié)合圖形對控制對象進行控制。真心祝愿他們幸福安康！再次感謝幫助過我的同學和老師，謝謝你們！參考文獻[1] 許杰．基于PC機的磁懸浮控制系統(tǒng)研究[D]．南京航空航天大學碩士學位論文，2007[2] 劉立恒．磁懸浮列車的PID控制器的設(shè)計[J]．浙江萬里學院學報，2005,18（2）[3] 楊鋒力．單自由度磁懸浮系統(tǒng)的設(shè)計與控制研究[D]．中南大學碩士學位論文．2005[4] 何衍慶，姜捷，江艷君、鄭瑩．控制系統(tǒng)分析、設(shè)計和應(yīng)用——MATLAB語言的應(yīng)用[M]．北京：化學工業(yè)出版社，2003[5] 姚小偉．磁懸浮系統(tǒng)的控制研究[D]．哈爾濱理工大學工學碩士學位論文，2007[6] 王義進，席文明．磁懸浮控制系統(tǒng)的設(shè)計研究[J]．計算機測量與控制（工業(yè)控制），2007,15(5)[7] 尚玲艷．基于MATLAB的鋼板磁懸浮控制系統(tǒng)研究[D]．上海交通大學碩士學位論文，2006[8] 劉茹．基于FPGA的磁浮列車空氣彈簧控制系統(tǒng)的設(shè)計[D]．西南交通大學碩士研究生學位論文，2006[9] 劉偉，張紅輝．控制系統(tǒng)穩(wěn)定性分析的MATLAB實現(xiàn)[J]．周口師范學院學報，2008,25(2)[10] 呂輝榜．基于MATLAB快速控制原型的磁懸浮控制系統(tǒng)研究[D]．武漢理工大學碩士學位論文，2008[11] ZiJiang，Kouichi Miyazaki，Shunshoku Kanae，Kiyoshi Wada．Robust Position Control of a Magnetic Levitation System via Dynamic Surface Control Technique[J]．IEEE TRANSACTIONS ONINDUSTRIAL ELECTRONICS，2004,51(1)[12] 張波．基于MATLAB的磁懸浮球數(shù)字控制器的研究[D]．河海大學碩士學位論文，2005[13] Walter Barie，John Chiasson．Linear and nonlinear statespace magnetic levitation[J]．International Journal of systems，1996,27(11)[14] 劉克平，吳建軍．H∞魯棒模糊控制在磁懸浮球系統(tǒng)中的應(yīng)用[J]．長春工業(yè)大學學報（自然科學報），2008,29(4)[15] 蔡君．實現(xiàn)基于xPC的磁懸浮網(wǎng)絡(luò)控制平臺[D]．東南大學碩士學位論文，2007[16] 張宏，李德才，孫明，許海平，何新智．磁懸浮球的實驗研究[J]．運城學報，[17] 上官霞南．永磁偏置磁懸浮球及其控制系統(tǒng)[D]．哈爾濱理工大學工程碩士學位論文，2007[18] 魏克新、王云亮、陳志敏．MATLAB語言與自動控制系統(tǒng)設(shè)計[M]．北京：機械工業(yè)出版社，2001[19] 徐渠．磁懸浮系統(tǒng)的控制[J]．研究科技創(chuàng)新導報，2008[20] 孫亮，楊鵬．自動控制原理[M]．北京：北京工業(yè)大學出版社，2006附 錄一、英文原文PID controller A proportional–integral–derivative controller (PID controller) is a generic control loop feedback mechanism(controller) widely used in industrial control systems –a PID is the most monly used feedback controller. A PID controller calculates an error value as the difference between a measuredprocess variable and a desired setp oint. The controller attempts to minimize the error by adjusting the process control inputs. In the absence of knowledge of the underlying process, PID controllers are the best controllers.[1] However, for best performance, the PID parameters used in the calculation must be tuned according to the nature of the system – while the design is generic, the parameters depend on the specific system. The PID controller calculation (algorithm) involves three separate parameters, and is accordingly sometimes calledthreeterm control: the proportional, the integral and derivative values, denoted P, I, and D. The proportionalvalue determines the reaction to the current error, the integral value determines the reaction based on the sum of recent errors, and the derivative value determines the reaction based on the rate at which the error changing. The weighted sum of these three actions is used to adjust the process via a control element such as the position of a control valve or the power supply of a be interpreted in terms of time: P depends on the present error, I on the accumulation of past errors, and D is a prediction offuture errors, based on current rate of change. By tuning the three constants in the PID controller algorithm, the controller can provide control action designed for specific process requirements. The response of the controller can be described in terms of the responsiveness of the controller to an error, the degree to which the controller overshoots the setpoint and the degree of system oscillation. Note that the use of the PID algorithm for control does not guarantee optimal control of the system or system stability.Some applications may require using only one or two modes to provide the appropriate system control. This is achieved by setting the gain of undesired control outputs to zero. A PID controller will be called a PI, PD, P or I controller in the absence of the respective control actions. PI controllers are fairly mon, since derivative action is sensitive to measurement noise, whereas the absence of an integral value may prevent the system from reaching its target value due to the control action.Note: Due to the diversity of the field of control theory and application, many naming conventions for the relevant variables are in mon use.Control loop basics A familiar example of a control loop is the action taken when adjusting the faucet water at the desired temperature. This typically involves the mixing of two process streams, the touches the water to sense or measure its temperature. Based on this feedback they perform a control action to adjust the ) is called the manipulated variable (MV). The difference between the temperature measurement and the setpoint is the error (e), that quantifies whether the water is too calculating the error, the controller decides when to change the tap position (MV) and by the controller first turns the valve on, they may turn the the valve all the way if very example of a simple proportional control. In the event that example of an integral control. By using only the proportional and integral control methods, it is possible that in some systems the water temperature may oscillate between the interest of achieving a gradual convergence at the desired temperature (SP), the controller may wish to dampthe anticipated future oscillations. So in order to pensate for this effect, the controller may elect to temper their adjustments. This can be thought of as a derivative control method. Making a change that is too large when the error is small is equivalent to a controller and will lead to overshoot. If the controller were to repeatedly make changes that were too large and repeatedly overshoot the target, the output would oscillate around the setpoint in either a constant, growing, or decaying sinusoid. If the oscillations increase with time then the system is unstable, whereas if they decrease the system is stable. If the oscillations remain at a constant magnitude the system is marginally stable. A would not do this because w