技术领域technical field
本发明涉及一种温度控制方法，特别涉及一种基于PSO辨识与自适应对称模糊PID的压水堆核电站一回路冷却剂温度控制方法。The invention relates to a temperature control method, in particular to a temperature control method for primary circuit coolant of a pressurized water reactor nuclear power plant based on PSO identification and self-adaptive symmetrical fuzzy PID.
背景技术Background technique
过程控制系统中往往需要得到系统的数学模型，传统的方法(两点法或近似法等)对系统做阶跃响应所采集的数据进行拟合辨识，初步得到的辨识函数，一般辨识精度不高。其次，在后续的控制策略方面，PID控制又是最早发展起来的控制策略之一，由于其算法简单、稳定性与可靠性好，能为许多控制对象提供比较合适的控制性能，被广泛应用于过程控制和运动控制中，尤其是用于可建立精确数学模型的确定性控制系统，然而，在实际工业生产过程中往往具有非线性、时变不确定性，应用传统常规的PID控制器不能达到理想的控制效果。而相关智能算法的研究，则是现代工程领域优化控制的主要技术之一。In the process control system, it is often necessary to obtain the mathematical model of the system. The traditional method (two-point method or approximation method, etc.) is used to fit and identify the data collected for the step response of the system. The identification function initially obtained generally has low identification accuracy. . Secondly, in terms of follow-up control strategies, PID control is one of the earliest control strategies developed. Because of its simple algorithm, good stability and reliability, it can provide more suitable control performance for many control objects, and is widely used in In process control and motion control, it is especially used for deterministic control systems that can establish precise mathematical models. However, in actual industrial production processes, there are often nonlinear and time-varying uncertainties, which cannot be achieved by using traditional conventional PID controllers. Ideal control effect. The research on relevant intelligent algorithms is one of the main techniques of optimal control in the modern engineering field.
过程通道系统辨识主要依据阶跃扰动的响应的离散点进行模型的辨识，因此主要论述阶跃响应法的求取方法。通过给一回路温度控制系统施加一个输入的阶跃响应信号，然后获得系统的阶跃响应观测值来辨识系统的传递函数。利用绘制出的阶跃响应曲线，然后利用切线法或者是两点法来近似传递函数，切线法和两点法适合规则的阶跃响应曲线，而面积法可以用于阶跃响应曲线不规则的情况下进行辨识，但是面积法计算量大。当阶跃响应曲线比较规则时，近似法、半对数法、切线法和两点法都能比较有效地导出传递函数，但这些方法的通用性比较差，计算精度依赖于测绘仪器；当阶跃响应曲线呈现不规则形状时，可以采用面积法，而面积法存在着易于陷入局部最小等缺点。所以，近年来又出现了粒子群优化PSO算法等相关智能辨识手段。The identification of the process channel system is mainly based on the discrete points of the response to the step disturbance for model identification, so the calculation method of the step response method is mainly discussed. The transfer function of the system is identified by applying an input step response signal to the primary loop temperature control system, and then obtaining the observed value of the system's step response. Use the drawn step response curve, and then use the tangent method or the two-point method to approximate the transfer function. The tangent method and the two-point method are suitable for regular step response curves, while the area method can be used for irregular step response curves. However, the area method has a large amount of calculation. When the step response curve is relatively regular, the approximation method, semi-logarithmic method, tangent method and two-point method can effectively derive the transfer function, but these methods have poor versatility, and the calculation accuracy depends on the surveying and mapping instrument; When the jump response curve has an irregular shape, the area method can be used, but the area method has the disadvantages of being easy to fall into a local minimum. Therefore, in recent years, relevant intelligent identification methods such as particle swarm optimization (PSO) algorithm have appeared.
温度控制通道的PID控制器参数整定也是核电站建设完成后调试期间的一项重要的工作，其参数设定的好坏直接影响系统运行的品质。传统的整定多采用试验和试凑的人工整定法，这种方法难以寻找全局最优的PID参数，因此整定后的系统品质更多的取决于工程人员的经验。目前，PID整定的方法有：基于被控过程对象参数辨识的整定方法；基于模式识别的专家系统整定等。而一般PID不进行整定，在遇到如核电厂温度控制等非线性常规对称数据时，就会出现控制的差异，从而影响控制效果。The parameter setting of the PID controller of the temperature control channel is also an important task during the commissioning period after the construction of the nuclear power plant is completed. The quality of its parameter setting directly affects the quality of the system operation. The traditional tuning mostly adopts the manual tuning method of experiment and trial and error. This method is difficult to find the global optimal PID parameters, so the quality of the system after tuning depends more on the experience of engineers. At present, the methods of PID tuning include: the tuning method based on the parameter identification of the controlled process object; the expert system tuning based on pattern recognition, etc. However, the general PID is not adjusted, and when encountering nonlinear conventional symmetrical data such as the temperature control of nuclear power plants, there will be control differences, which will affect the control effect.
因此，研究一种能适用于各种有效测试信号且精度较高的通用辨识方法具有重要的理论与实际价值意义。Therefore, it is of great theoretical and practical significance to study a general identification method that can be applied to various effective test signals and has high precision.
发明内容Contents of the invention
本发明是针对在实际工业生产过程中的非线性、时变不确定性，应用传统常规的PID控制器不能达到理想的控制效果的问题，提出了一种压水堆核电站一回路冷却剂温度控制方法，通过PSO先对核电仿真机运行得出的温度阶跃响应数据进行辨识，提高数据辨识精度，然后用自适应对称模糊PID控制，提高系统的响应时间、稳定性以及系统的鲁棒性。The present invention aims at the nonlinear and time-varying uncertainty in the actual industrial production process, and the problem that the ideal control effect cannot be achieved by using the traditional conventional PID controller. Method: First, identify the temperature step response data obtained from the operation of the nuclear power simulator through PSO to improve the accuracy of data identification, and then use adaptive symmetrical fuzzy PID control to improve the response time, stability and robustness of the system.
本发明的技术方案为：一种压水堆核电站一回路冷却剂温度控制方法，将温度调节的被控对象用二阶传递函数表示，T1、T2分别为二次项系数与一次项系数，K为增益可从响应的稳态值得到，T1和T2作为粒子进行粒子群优化PS0算法得到优化T1和T2的参数；温度设定值Ts与实际温度T的误差e和误差变化率ec作为模糊控制器输入，PID控制器的三个参数P、I、D的修正值作为模糊控制器输出，模糊控制器输出送PID控制器；温度设定值Ts与实际温度T的误差e送PID控制器，PID控制器输出控制被控对象。The technical solution of the present invention is: a method for controlling the temperature of the coolant in the primary circuit of a pressurized water reactor nuclear power plant. Indicates that T1 and T2 are the coefficients of the quadratic term and the coefficient of the first term respectively, K is the gain which can be obtained from the steady-state value of the response, T1 and T2 are used as particles to optimize the parameters of T1 and T2 through the particle swarm optimization PS0 algorithm; the temperature setting The error e and the error change rate ec between the value Ts and the actual temperature T are used as the input of the fuzzy controller, and the correction values of the three parameters P, I, and D of the PID controller are used as the output of the fuzzy controller, and the output of the fuzzy controller is sent to the PID controller; The error e between the temperature setting value Ts and the actual temperature T is sent to the PID controller, and the output of the PID controller controls the controlled object.
本发明的有益效果在于：本发明压水堆核电站一回路冷却剂温度控制方法，该方法能够在保证系统稳定的前提下，提高系统辨识函数的辨识精度、响应时间和鲁棒性，同时又利于减小系统误差，保证整个系统过程辨识与控制的可靠性，从而保证核电站的安全平稳运行，降低故障率。The beneficial effect of the present invention is that: the primary circuit coolant temperature control method of the pressurized water reactor nuclear power plant of the present invention can improve the identification accuracy, response time and robustness of the system identification function on the premise of ensuring the stability of the system, and at the same time, it is beneficial Reduce system errors and ensure the reliability of process identification and control of the entire system, thereby ensuring the safe and stable operation of nuclear power plants and reducing failure rates.
附图说明Description of drawings
图1为本发明压水堆核电站一回路冷却剂温度控制示意图；Fig. 1 is the schematic diagram of temperature control of primary circuit coolant in pressurized water reactor nuclear power plant of the present invention;
图2为本发明采用的PSO辨识流程图；Fig. 2 is the PSO identification flowchart that the present invention adopts;
图3为传统PID原理结构图；Fig. 3 is a traditional PID principle structure diagram;
图4为本发明自适应对称模糊PID原理结构模型图；Fig. 4 is the structural model diagram of adaptive symmetrical fuzzy PID principle of the present invention;
图5为本发明自适应对称模糊PID原理流程图；Fig. 5 is the flow chart of principle of self-adaptive symmetrical fuzzy PID of the present invention;
图6为本发明PSO辨识函数误差图；Fig. 6 is the error figure of PSO identification function of the present invention;
图7为本发明自适应对称模糊PID与传统PID对比图。Fig. 7 is a comparison diagram between the adaptive symmetrical fuzzy PID of the present invention and the traditional PID.
具体实施方式Detailed ways
如图1所示的为PSO算法结合自适应对称模糊PID的压水堆核电站一回路冷却剂温度控制方法，图中Ts为温度设定值，T为实际温度，Err温度偏差。系统通过对偏差模糊化，对应响应的对称模糊PID规则动态整定PID的比例积分和微分增益。在控制回路的对象环节，这里的对象环节可以看做包含执行器对象传递函数和变送器的广义被控对象，通过PSO辨识算法结合开环响应的数据拟合，可优化传递函数模型，提高传递函数与真实对象的准确度。As shown in Figure 1, the PSO algorithm combined with adaptive symmetric fuzzy PID is used to control the temperature of the coolant in the primary circuit of the pressurized water reactor nuclear power plant. In the figure, Ts is the temperature setting value, T is the actual temperature, and Err is the temperature deviation. The system dynamically adjusts the proportional integral and differential gain of the PID by fuzzifying the deviation and corresponding to the symmetrical fuzzy PID rule of the response. In the object link of the control loop, the object link here can be regarded as a generalized controlled object including the transfer function of the actuator object and the transmitter. Through the PSO identification algorithm combined with the data fitting of the open-loop response, the transfer function model can be optimized to improve Accuracy of transfer function versus real objects.
如图2所示为本发明采用的PSO辨识流程图。用PSO智能算法进行辨识，这里编写PSO主函数与子函数，其中主函数里面包含主要的三个函数，适应度函数F、更新速度函数V、更新位置函数current_position；子函数里面输入几十组数据，采用两阶系统传递函数，设置相关的传递函数参数。FIG. 2 is a flow chart of PSO identification adopted in the present invention. Use the PSO intelligent algorithm to identify, write the PSO main function and sub-functions here, where the main function contains three main functions, the fitness function F, the update speed function V, and the update position function current_position; enter dozens of sets of data in the sub-function , using a two-order system transfer function, and setting the relevant transfer function parameters.
Vi＝Vi+c1·rand()·(g-Xi)+c2·rand()·(Nbesti-Xi) (1)Vi=Vi+c1 rand() (g-Xi)+c2 rand() (Nbesti-Xi) (1)
Xi＝Xi+Vi (2)Xi=Xi+Vi (2)
式中c1和c2是加速常量，分别调节向全局最好粒子和个体最好粒子方向飞行的最大步长，若太小，则粒子可能远离目标区域，若太大则会导致突然向目标区域飞去，或飞过目标区域。合适的c1，c2可以加快收敛且不易陷入局部最优。rand()是0到1之间的随机数。g为种群经历过得最好位置，Nbesti为lizii经历过的最好位置，上式中Vi与Xi是更新后的速度和位置。粒子在每一维飞行的速度不能超过算法设定的最大速度Vmax。设置较大的Vmax可以保证粒子种群的全局搜索能力，Vmax较小则粒子种群优化算法的局部搜索能力加强。In the formula, c1 and c2 are acceleration constants, which respectively adjust the maximum step size of the flight to the global best particle and the best individual particle. If it is too small, the particle may be far away from the target area. If it is too large, it will cause the particle to suddenly fly to the target area. Go, or fly over the target area. Appropriate c1 and c2 can speed up the convergence and not easily fall into local optimum. rand() is a random number between 0 and 1. g is the best position experienced by the population, Nbesti is the best position experienced by lizii, and Vi and Xi are the updated speed and position in the above formula. The speed of particles flying in each dimension cannot exceed the maximum speed Vmax set by the algorithm. Setting a larger Vmax can ensure the global search ability of the particle swarm, and a smaller Vmax can strengthen the local search ability of the particle swarm optimization algorithm.
这里采用粒子群算法优化辨识传递函数需要分析系统阶数方法是，一般被控对象均可以简化为二阶系统，如式(3)所示，我们这里采用待定系数法定义待辨识优化的传递函数，T1、T2分别为二次项系数与一次项系数，K为系统增益可以从响应的稳态值得到。因此粒子群主要优化的参数为T1和T2的数值，与算法相对应，首先通过随机产生一个二维的初始数组，分别代表T1和T2，再通过适应度函数判断当前最佳位置并通过以上的位置和速度更新函数迭代优化T1和T2的参数，直到符合终止条件。Here, the particle swarm optimization algorithm is used to optimize the identification transfer function. The method of analyzing the order of the system is that the general controlled object can be simplified into a second-order system, as shown in formula (3). Here, we use the undetermined coefficient method to define the transfer function to be identified and optimized , T1, T2 are the quadratic term coefficient and the first term coefficient respectively, and K is the system gain which can be obtained from the steady-state value of the response. Therefore, the main parameters of particle swarm optimization are the values of T1 and T2, which correspond to the algorithm. First, a two-dimensional initial array is randomly generated to represent T1 and T2 respectively, and then the current best position is judged by the fitness function and passed the above The position and velocity update function iteratively optimizes the parameters of T1 and T2 until the termination condition is met.
然后，得出符合要求的辨识函数后，与之前初步拟合的函数做对比。接着，对其进行自适应对称模糊PID的优化控制环节，此时，这里建立2输入3输出的自适应模糊控制器的对称规则库和隶属函数，该控制器是以误差e和误差变化率ec作为输入，PID控制器的三个参数P、I、D的修正值作为输出。并且与传统的PID控制方法做了对比，进而得出自适应对称模糊PID的相关优越性能。Then, after the identification function that meets the requirements is obtained, it is compared with the previously fitted function. Then, optimize the control link of self-adaptive symmetric fuzzy PID. At this time, the symmetric rule base and membership function of the self-adaptive fuzzy controller with 2 inputs and 3 outputs is established here. The controller is based on the error e and the error change rate ec As input, the correction values of the three parameters P, I, D of the PID controller are output. And compared with the traditional PID control method, the relevant superior performance of adaptive symmetrical fuzzy PID is obtained.
为说明本发明的正确性和可行性，对大亚湾核电站900MW机组的仿真机上采集的温度数据进行仿真验证。该实验参数为满功率运行工况加5％负阶跃信号的响应数据。46组数据，每组数据中，第一个数代表给阶跃信号后的运行时间，第二个数代表开环系统阶跃响应冷却剂温度随时间变化的数据，具体仿真数据为：In order to illustrate the correctness and feasibility of the present invention, the temperature data collected on the simulator of the 900MW unit of Daya Bay Nuclear Power Plant is simulated and verified. The experimental parameters are the response data of the full power operating condition plus a 5% negative step signal. 46 sets of data. In each set of data, the first number represents the running time after the step signal is given, and the second number represents the data of the open-loop system step response coolant temperature changing with time. The specific simulation data are:
0，310.07；1，309.15；2，308.95；3，308.98；4，309.07；5，309.11；6，309；7，308.9；8，308.83；9，308.81；10，308.81；0,310.07; 1,309.15; 2,308.95; 3,308.98; 4,309.07; 5,309.11; 6,309; 7,308.9;
11，308.8；12，308.8；13，308.81；14，308.83；15，308.85；16，308.84；17，308.82；18，308.81；19，308.81；20，308.81；11,308.8; 12,308.8; 13,308.81; 14,308.83; 15,308.85; 16,308.84; 17,308.82;
21，308.81；22，308.8；23，308.8；24，308.8；25，308.8；26，308.8；27，308.79；28，308.79；29，308.78；30，308.78；21,308.81;22,308.8;23,308.8;24,308.8;25,308.8;26,308.8;27,308.79;28,308.79;29,308.78;30,308.78;
31，308.78；32，308.78；33，308.77；34，308.77；35，308.77；36，308.77；37，308.76；38，308.76；39，308.76；40，308.76；31,308.78;32,308.78;33,308.77;34,308.77;35,308.77;36,308.77;37,308.76;38,308.76;39,308.76;40,308.76;
41，308.76；42，308.76；43，308.76；44，308.76；45，308.76；41,308.76; 42,308.76; 43,308.76; 44,308.76; 45,308.76;
附图3显示了传统PID原理结构，图4显示了自适应对称模糊PID原理结构模型，图5是自适应对称模糊PID原理流程图。Accompanying drawing 3 has shown the principle structure of traditional PID, and Fig. 4 has shown the principle structure model of self-adaptive symmetrical fuzzy PID, and Fig. 5 is the flow chart of self-adaptive symmetrical fuzzy PID principle.
图3传统PID结构中，r(t)为参考输入信号，(e)t为控制偏差信号，u(t)为控制信号，y(t)为被控系统输出信号。其中控制偏差信号(e)t＝r(t)-y(t).控制信号u(t)为In the traditional PID structure in Figure 3, r(t) is the reference input signal, (e)t is the control deviation signal, u(t) is the control signal, and y(t) is the output signal of the controlled system. Among them, the control deviation signal (e)t=r(t)-y(t). The control signal u(t) is
PSO主函数里面的具体参数如下：The specific parameters in the PSO main function are as follows:
种群规模n＝10；最大迭代次数bird_setp＝500；维数dim＝2；PSO的C1学习因子C1＝0.1；PSO的C2学习因子C2＝0.1；PSO惯性权重，w＝0.9；Population size n=10; maximum number of iterations bird_setp=500; dimension dim=2; PSO C1 learning factor C1=0.1; PSO C2 learning factor C2=0.1; PSO inertia weight, w=0.9;
PSO主函数里面的适应度、更新速度函数、更新位置函数分别为The fitness, update speed function, and update position function in the PSO main function are respectively
F＝mean(abs(step(sys,0：45)-a(：，2)))；F=mean(abs(step(sys,0:45)-a(:,2)));
v＝w*v+c1*(R1.*(local_best_position-current_position))+c2*(R2.*(globl_best_position-current_position))；v=w*v+c1*(R1.*(local_best_position-current_position))+c2*(R2.*(globl_best_position-current_position));
current_position＝current_position+v；current_position=current_position+v;
子函数里面输入46组数据，采用两阶系统传递函数辨识为，Enter 46 sets of data in the sub-function, and use the two-order system transfer function to identify as,
num＝[k]；num = [k];
den＝[T1 T2 1]；den = [T1 T2 1];
sys＝tf(num,den)；sys=tf(num,den);
PSO优化辨识二阶传递函数参数K＝1.21，T1、T2如下；PSO optimizes and identifies the second-order transfer function parameter K=1.21, and T1 and T2 are as follows;
结果TI、T2的参数值Result parameter values of TI and T2
ans＝0.220400390912389ans=0.220400390912389
1.0632301643623701.063230164362370
系统二阶传递函数是The second order transfer function of the system is
s＝tf(1.21，[ans′1])s = tf(1.21, [ans'1])
s＝1.21s=1.21
----------------------------------
0.2204s^2+1.063s+10.2204s^2+1.063s+1
误差分析：观察图6，横坐标是迭代次数，纵坐标是辨识相对误差，图中最大的误差0.67％；上述的两个曲线可知由两点法计算出来的传递函数的时域响应的数值与原始数据相差的数值大部分存在于-1％到1％之间，满足要求。Error analysis: Looking at Figure 6, the abscissa is the number of iterations, and the ordinate is the relative error of identification. The largest error in the figure is 0.67%. From the above two curves, it can be seen that the value of the time domain response of the transfer function calculated by the two-point method and Most of the values that differ from the original data are between -1% and 1%, which meets the requirements.
根据要求，用于PID参数调整的模糊控制器采用二输入三输出的形式。该控制器是以误差e和误差变化率ec作为输入，PID控制器的三个参数P、I、D的修正值作为输出。取输入误差e和误差变化率ec及输出的模糊子集为{NB，NM，NS，ZO，PS，PM，PB}，子集中元素分别代表负大，负中，负小，零，正小，正中，正大。误差e和误差变化率ec的取值范围为[-3，3]。表1是自适应对称模糊规则表。According to requirements, the fuzzy controller used for PID parameter adjustment adopts the form of two inputs and three outputs. The controller takes the error e and the error change rate ec as input, and the correction value of the three parameters P, I, D of the PID controller as the output. Take the input error e and the error change rate ec and the fuzzy subset of the output as {NB, NM, NS, ZO, PS, PM, PB}, the elements in the subset represent negative large, negative medium, negative small, zero, positive small , right in the middle, just right. The value range of the error e and the error change rate ec is [-3, 3]. Table 1 is a table of adaptive symmetric fuzzy rules.
由图7自适应对称模糊PID(点画线)与传统PID(直线)对比结果图，可以看出模糊PID控制的上升时间在t＝1s左右，效果要比传统PID效果要好。由仿真结果可知，模糊PID控制与常规的PID控制相比较，具有较高的控制精度，调节时间短，抗扰性好，控制效果好等优点。由此可知，中采用模糊PID控制可以克服常规PID控制器的缺点，将模糊控制与PID控制器结合起来，扬长避短，不仅保持了常规PID控制系统原理简单、使用方便、鲁棒性较强等优点，而且具有更大的灵活性、整定性、控制精度更好。From the comparison results of adaptive symmetrical fuzzy PID (dotted line) and traditional PID (straight line) in Figure 7, it can be seen that the rise time of fuzzy PID control is about t=1s, and the effect is better than that of traditional PID. It can be seen from the simulation results that compared with the conventional PID control, the fuzzy PID control has the advantages of high control precision, short adjustment time, good immunity to disturbance, and good control effect. It can be seen that the use of fuzzy PID control can overcome the shortcomings of conventional PID controllers, and combine fuzzy control with PID controllers to maximize strengths and avoid weaknesses. It not only maintains the advantages of conventional PID control systems such as simple principle, convenient use, and strong robustness. , and has greater flexibility, settability, and better control accuracy.
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| CN110729066A (en)* | 2019-11-02 | 2020-01-24 | 哈尔滨工程大学 | Coordination control method for unattended small pressurized water reactor |
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