跑起来的效果看每个类的test方法，自己调用来测试
目的是看看哪个算法好用，移植的时候比较单纯没有研究懂算法，代码结构也没改动，只是移植到C#方便查看代码和测试，大家要拷贝也很方便，把整个类拷贝到.cs文件即可
这段算法在实际值低于目标值是工作正常，超过后会有问题，不知道如何调教
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using System;using System.Collections.Generic;using System.Diagnostics;using System.Linq;using System.Text;using System.Threading.Tasks;namespace FuzzyPID{ class FuzzyPID { public const int N = 7; double target; //系统的控制目标 double actual; //采样获得的实际值 double e; //误差 double e_pre_1; //上一次的误差 double e_pre_2; //上上次的误差 double de; //误差的变化率 double emax; //误差基本论域上限 double demax; //误差辩化率基本论域的上限 double delta_Kp_max; //delta_kp输出的上限 double delta_Ki_max; //delta_ki输出上限 double delta_Kd_max; //delta_kd输出上限 double Ke; //Ke=n/emax,量化论域为[-3,-2,-1,0,1,2,3] double Kde; //Kde=n/demax,量化论域为[-3,-2,-1,0,1,2,3] double Ku_p; //Ku_p=Kpmax/n,量化论域为[-3,-2,-1,0,1,2,3] double Ku_i; //Ku_i=Kimax/n,量化论域为[-3,-2,-1,0,1,2,3] double Ku_d; //Ku_d=Kdmax/n,量化论域为[-3,-2,-1,0,1,2,3] int[,] Kp_rule_matrix = new int[N, N];//Kp模糊规则矩阵 int[,] Ki_rule_matrix = new int[N, N];//Ki模糊规则矩阵 int[,] Kd_rule_matrix = new int[N, N];//Kd模糊规则矩阵 string mf_t_e; //e的隶属度函数类型 string mf_t_de; //de的隶属度函数类型 string mf_t_Kp; //kp的隶属度函数类型 string mf_t_Ki; //ki的隶属度函数类型 string mf_t_Kd; //kd的隶属度函数类型 double[] e_mf_paras; //误差的隶属度函数的参数 double[] de_mf_paras;//误差的偏差隶属度函数的参数 double[] Kp_mf_paras; //kp的隶属度函数的参数 double[] Ki_mf_paras; //ki的隶属度函数的参数 double[] Kd_mf_paras; //kd的隶属度函数的参数 double Kp; double Ki; double Kd; double A; double B; double C; public FuzzyPID(double e_max, double de_max, double kp_max, double ki_max, double kd_max, double Kp0, double Ki0, double Kd0) { emax = e_max; demax = de_max; delta_Kp_max = kp_max; delta_Ki_max = ki_max; delta_Kd_max = kd_max; e = target - actual; de = e - e_pre_1; Ke = (N / 2) / emax; Kde = (N / 2) / demax; Ku_p = delta_Kp_max / (N / 2); Ku_i = delta_Ki_max / (N / 2); Ku_d = delta_Kd_max / (N / 2); Kp = Kp0; Ki = Ki0; Kd = Kd0; A = Kp + Ki + Kd; B = -2 * Kd - Kp; C = Kd; } //三角隶属度函数 double trimf(double x, double a, double b, double c) { double u; if (x >= a && x <= b) u = (x - a) / (b - a); else if (x > b && x <= c) u = (c - x) / (c - b); else u = 0; return u; } //正态隶属度函数 double gaussmf(double x, double ave, double sigma) { double u; if (sigma < 0) { throw new Exception("In gaussmf, sigma must larger than 0"); } u = Math.Exp(-Math.Pow(((x - ave) / sigma), 2)); return u; } //梯形隶属度函数 double trapmf(double x, double a, double b, double c, double d) { double u; if (x >= a && x < b) u = (x - a) / (b - a); else if (x >= b && x < c) u = 1; else if (x >= c && x <= d) u = (d - x) / (d - c); else u = 0; return u; } //设置模糊规则Matrix public void setRuleMatrix(int[,] kp_m, int[,] ki_m, int[,] kd_m) { for (int i = 0; i < N; i++) for (int j = 0; j < N; j++) { Kp_rule_matrix[i, j] = kp_m[i, j]; Ki_rule_matrix[i, j] = ki_m[i, j]; Kd_rule_matrix[i, j] = kd_m[i, j]; } } //设置模糊隶属度函数的子函数 void setMf_sub(string type, double[] paras, int n) { int N_mf_e = 0, N_mf_de = 0, N_mf_Kp = 0, N_mf_Ki = 0, N_mf_Kd = 0; switch (n) { case 0: if (type == "trimf" || type == "gaussmf" || type == "trapmf") mf_t_e = type; else throw new Exception("Type of membership function must be \"trimf\" or \"gaussmf\" or \"trapmf\""); if (mf_t_e == "trimf") N_mf_e = 3; else if (mf_t_e == "gaussmf") N_mf_e = 2; else if (mf_t_e == "trapmf") N_mf_e = 4; e_mf_paras = new double[N * N_mf_e]; for (int i = 0; i < N * N_mf_e; i++) e_mf_paras[i] = paras[i]; break; case 1: if (type == "trimf" || type == "gaussmf" || type == "trapmf") mf_t_de = type; else throw new Exception("Type of membership function must be \"trimf\" or \"gaussmf\" or \"trapmf\""); if (mf_t_de == "trimf") N_mf_de = 3; else if (mf_t_de == "gaussmf") N_mf_de = 2; else if (mf_t_de == "trapmf") N_mf_de = 4; de_mf_paras = new double[N * N_mf_de]; for (int i = 0; i < N * N_mf_de; i++) de_mf_paras[i] = paras[i]; break; case 2: if (type == "trimf" || type == "gaussmf" || type == "trapmf") mf_t_Kp = type; else throw new Exception("Type of membership function must be \"trimf\" or \"gaussmf\" or \"trapmf\""); if (mf_t_Kp == "trimf") N_mf_Kp = 3; else if (mf_t_Kp == "gaussmf") N_mf_Kp = 2; else if (mf_t_Kp == "trapmf") N_mf_Kp = 4; Kp_mf_paras = new double[N * N_mf_Kp]; for (int i = 0; i < N * N_mf_Kp; i++) Kp_mf_paras[i] = paras[i]; break; case 3: if (type == "trimf" || type == "gaussmf" || type == "trapmf") mf_t_Ki = type; else throw new Exception("Type of membership function must be \"trimf\" or \"gaussmf\" or \"trapmf\""); if (mf_t_Ki == "trimf") N_mf_Ki = 3; else if (mf_t_Ki == "gaussmf") N_mf_Ki = 2; else if (mf_t_Ki == "trapmf") N_mf_Ki = 4; Ki_mf_paras = new double[N * N_mf_Ki]; for (int i = 0; i < N * N_mf_Ki; i++) Ki_mf_paras[i] = paras[i]; break; case 4: if (type == "trimf" || type == "gaussmf" || type == "trapmf") mf_t_Kd = type; else throw new Exception("Type of membership function must be \"trimf\" or \"gaussmf\" or \"trapmf\""); if (mf_t_Kd == "trimf") N_mf_Kd = 3; else if (mf_t_Kd == "gaussmf") N_mf_Kd = 2; else if (mf_t_Kd == "trapmf") N_mf_Kd = 4; Kd_mf_paras = new double[N * N_mf_Kd]; for (int i = 0; i < N * N_mf_Kd; i++) Kd_mf_paras[i] = paras[i]; break; default: break; } } //设置模糊隶属度函数的类型和参数 public void setMf(string mf_type_e, double[] e_mf, string mf_type_de, double[] de_mf, string mf_type_Kp, double[] Kp_mf, string mf_type_Ki, double[] Ki_mf, string mf_type_Kd, double[] Kd_mf) { setMf_sub(mf_type_e, e_mf, 0); setMf_sub(mf_type_de, de_mf, 1); setMf_sub(mf_type_Kp, Kp_mf, 2); setMf_sub(mf_type_Ki, Ki_mf, 3); setMf_sub(mf_type_Kd, Kd_mf, 4); } //实现模糊控制 public double realize(double t, double a) { double[] u_e = new double[N], u_de = new double[N], u_u = new double[N]; int[] u_e_index = new int[3], u_de_index = new int[3];//假设一个输入最多激活3个模糊子集 double delta_Kp, delta_Ki, delta_Kd; double delta_u; target = t; actual = a; e = target - actual; de = e - e_pre_1; e = Ke * e; de = Kde * de; /* 将误差e模糊化*/ int j = 0; for (int i = 0; i < N; i++) { if (mf_t_e == "trimf") u_e[i] = trimf(e, e_mf_paras[i * 3], e_mf_paras[i * 3 + 1], e_mf_paras[i * 3 + 2]);//e模糊化，计算它的隶属度 else if (mf_t_e == "gaussmf") u_e[i] = gaussmf(e, e_mf_paras[i * 2], e_mf_paras[i * 2 + 1]);//e模糊化，计算它的隶属度 else if (mf_t_e == "trapmf") u_e[i] = trapmf(e, e_mf_paras[i * 4], e_mf_paras[i * 4 + 1], e_mf_paras[i * 4 + 2], e_mf_paras[i * 4 + 3]);//e模糊化，计算它的隶属度 if (u_e[i] != 0) u_e_index[j++] = i; //存储被激活的模糊子集的下标，可以减小计算量 } for (; j < 3; j++) u_e_index[j] = 0; //富余的空间填0 /*将误差变化率de模糊化*/ j = 0; for (int i = 0; i < N; i++) { if (mf_t_de == "trimf") u_de[i] = trimf(de, de_mf_paras[i * 3], de_mf_paras[i * 3 + 1], de_mf_paras[i * 3 + 2]);//de模糊化，计算它的隶属度 else if (mf_t_de == "gaussmf") u_de[i] = gaussmf(de, de_mf_paras[i * 2], de_mf_paras[i * 2 + 1]);//de模糊化，计算它的隶属度 else if (mf_t_de == "trapmf") u_de[i] = trapmf(de, de_mf_paras[i * 4], de_mf_paras[i * 4 + 1], de_mf_paras[i * 4 + 2], de_mf_paras[i * 4 + 3]);//de模糊化，计算它的隶属度 if (u_de[i] != 0) u_de_index[j++] = i; //存储被激活的模糊子集的下标，可以减小计算量 } for (; j < 3; j++) u_de_index[j] = 0; //富余的空间填0 double den = 0, num = 0; /*计算delta_Kp和Kp*/ for (int m = 0; m < 3; m++) for (int n = 0; n < 3; n++) { num += u_e[u_e_index[m]] * u_de[u_de_index[n]] * Kp_rule_matrix[u_e_index[m], u_de_index[n]]; den += u_e[u_e_index[m]] * u_de[u_de_index[n]]; } delta_Kp = num / den; delta_Kp = Ku_p * delta_Kp; if (delta_Kp >= delta_Kp_max) delta_Kp = delta_Kp_max; else if (delta_Kp <= -delta_Kp_max) delta_Kp = -delta_Kp_max; Kp += delta_Kp; if (Kp < 0) Kp = 0; /*计算delta_Ki和Ki*/ den = 0; num = 0; for (int m = 0; m < 3; m++) for (int n = 0; n < 3; n++) { num += u_e[u_e_index[m]] * u_de[u_de_index[n]] * Ki_rule_matrix[u_e_index[m], u_de_index[n]]; den += u_e[u_e_index[m]] * u_de[u_de_index[n]]; } delta_Ki = num / den; delta_Ki = Ku_i * delta_Ki; if (delta_Ki >= delta_Ki_max) delta_Ki = delta_Ki_max; else if (delta_Ki <= -delta_Ki_max) delta_Ki = -delta_Ki_max; Ki += delta_Ki; if (Ki < 0) Ki = 0; /*计算delta_Kd和Kd*/ den = 0; num = 0; for (int m = 0; m < 3; m++) for (int n = 0; n < 3; n++) { num += u_e[u_e_index[m]] * u_de[u_de_index[n]] * Kd_rule_matrix[u_e_index[m], u_de_index[n]]; den += u_e[u_e_index[m]] * u_de[u_de_index[n]]; } delta_Kd = num / den; delta_Kd = Ku_d * delta_Kd; if (delta_Kd >= delta_Kd_max) delta_Kd = delta_Kd_max; else if (delta_Kd <= -delta_Kd_max) delta_Kd = -delta_Kd_max; Kd += delta_Kd; if (Kd < 0) Kd = 0; A = Kp + Ki + Kd; B = -2 * Kd - Kp; C = Kd; delta_u = A * e + B * e_pre_1 + C * e_pre_2; delta_u = delta_u / Ke; if (delta_u >= 0.95 * target) delta_u = 0.95 * target; else if (delta_u <= -0.95 * target) delta_u = -0.95 * target; e_pre_2 = e_pre_1; e_pre_1 = e; return delta_u; } void showMf(string type, double[] mf_paras) { int tab = 0; if (type == "trimf") tab = 2; else if (type == "gaussmf") tab = 1; else if (type == "trapmf") tab = 3; this.WriteLine($"函数类型：{mf_t_e}"); this.WriteLine("函数参数列表："); double[] p = mf_paras; for (int i = 0; i < N * (tab + 1); i++) { this.Write(p[i] + " "); if (i % (tab + 1) == tab) this.Write("\r\n"); } } public void showInfo() { this.WriteLine("Info of this fuzzy controller is as following:"); this.WriteLine($"基本论域e：[{-emax},{emax}]"); this.WriteLine($"基本论域de：[{-demax},{demax}]"); this.WriteLine($"基本论域delta_Kp：[{-delta_Kp_max},{delta_Kp_max}]"); this.WriteLine($"基本论域delta_Ki：[{-delta_Ki_max},{delta_Ki_max}]"); this.WriteLine($"基本论域delta_Kd：[{-delta_Kd_max},{delta_Kd_max}]"); this.WriteLine("误差e的模糊隶属度函数参数："); showMf(mf_t_e, e_mf_paras); this.WriteLine("误差变化率de的模糊隶属度函数参数："); showMf(mf_t_de, de_mf_paras); this.WriteLine("delta_Kp的模糊隶属度函数参数："); showMf(mf_t_Kp, Kp_mf_paras); this.WriteLine("delta_Ki的模糊隶属度函数参数："); showMf(mf_t_Ki, Ki_mf_paras); this.WriteLine("delta_Kd的模糊隶属度函数参数："); showMf(mf_t_Kd, Kd_mf_paras); this.WriteLine("模糊规则表："); this.WriteLine("delta_Kp的模糊规则矩阵"); for (int i = 0; i < N; i++) { for (int j = 0; j < N; j++) { this.Write(Kp_rule_matrix[i, j]); } this.Write("\r\n"); } this.WriteLine("delta_Ki的模糊规则矩阵"); ; for (int i = 0; i < N; i++) { for (int j = 0; j < N; j++) { this.WriteLine(Ki_rule_matrix[i, j]); } WriteEnd(); } this.WriteLine("delta_Kd的模糊规则矩阵"); ; for (int i = 0; i < N; i++) { for (int j = 0; j < N; j++) { this.WriteLine(Kd_rule_matrix[i, j]); } WriteEnd(); } this.WriteLine($"误差的量化比例因子Ke={Ke}"); this.WriteLine($"误差变化率的量化比例因子Kde={Kde}"); this.WriteLine($"输出的量化比例因子Ku_p={Ku_p}"); this.WriteLine($"输出的量化比例因子Ku_i={Ku_i}"); this.WriteLine($"输出的量化比例因子Ku_d={Ku_d}"); this.WriteLine($"设定目标target={target}"); this.WriteLine($"误差e={e}"); this.WriteLine($"Kp={Kp}"); this.WriteLine($"Ki={Ki}"); this.WriteLine($"Kd={Kd}"); WriteEnd(); } public void Write(object str) { Console.Write(str); } public void WriteLine(object str) { Console.WriteLine(str); } public void WriteEnd() { Console.Write("\r\n"); } public static void test() { int NB = -3; int NM = -2; int NS = -1; int ZO = 0; int PS = 1; int PM = 2; int PB = 3; double target = 300; double actual = 400; double u = 0; int[,] deltaKpMatrix = new int[7, 7] {{PB,PB,PM,PM,PS,ZO,ZO }, {PB,PB,PM,PS,PS,ZO,NS}, {PM,PM,PM,PS,ZO,NS,NS}, {PM,PM,PS,ZO,NS,NM,NM}, {PS,PS,ZO,NS,NS,NM,NM}, {PS,ZO,NS,NM,NM,NM,NB}, {ZO,ZO,NM,NM,NM,NB,NB}}; int[,] deltaKiMatrix = new int[7, 7]{{NB,NB,NM,NM,NS,ZO,ZO}, {NB,NB,NM,NS,NS,ZO,ZO}, {NB,NM,NS,NS,ZO,PS,PS}, {NM,NM,NS,ZO,PS,PM,PM}, {NM,NS,ZO,PS,PS,PM,PB}, {ZO,ZO,PS,PS,PM,PB,PB}, {ZO,ZO,PS,PM,PM,PB,PB}}; int[,] deltaKdMatrix = new int[7, 7]{{PS,NS,NB,NB,NB,NM,PS}, {PS,NS,NB,NM,NM,NS,ZO}, {ZO,NS,NM,NM,NS,NS,ZO}, {ZO,NS,NS,NS,NS,NS,ZO}, {ZO,ZO,ZO,ZO,ZO,ZO,ZO}, {PB,NS,PS,PS,PS,PS,PB}, {PB,PM,PM,PM,PS,PS,PB}}; double[] e_mf_paras = { -3, -3, -2, -3, -2, -1, -2, -1, 0, -1, 0, 1, 0, 1, 2, 1, 2, 3, 2, 3, 3 }; double[] de_mf_paras = { -3, -3, -2, -3, -2, -1, -2, -1, 0, -1, 0, 1, 0, 1, 2, 1, 2, 3, 2, 3, 3 }; double[] Kp_mf_paras = { -3, -3, -2, -3, -2, -1, -2, -1, 0, -1, 0, 1, 0, 1, 2, 1, 2, 3, 2, 3, 3 }; double[] Ki_mf_paras = { -3, -3, -2, -3, -2, -1, -2, -1, 0, -1, 0, 1, 0, 1, 2, 1, 2, 3, 2, 3, 3 }; double[] Kd_mf_paras = { -3, -3, -2, -3, -2, -1, -2, -1, 0, -1, 0, 1, 0, 1, 2, 1, 2, 3, 2, 3, 3 }; var fuzzypid = new FuzzyPID(1500, 1000, 0.3, 0.9, 0.6, 0.01, 0.04, 0.01); fuzzypid.setMf("trimf", e_mf_paras, "trimf", de_mf_paras, "trimf", Kp_mf_paras, "trimf", Ki_mf_paras, "trimf", Kd_mf_paras); fuzzypid.setRuleMatrix(deltaKpMatrix, deltaKiMatrix, deltaKdMatrix); for (int i = 0; i < 50; i++) { u = fuzzypid.realize(target, actual); actual += u; Console.WriteLine($"{i} {target} {u} {actual}"); //if (i>19) //{ // target = 300; //} } //fuzzypid.showInfo(); } }} |
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原文链接：https://www.cnblogs.com/gxrsprite/p/12155890.html
