使用c语言编写模糊控制算法

以下是一个使用 C 语言编写模糊控制算法的示例：

首先，我们定义模糊控制器的输入和输出：

// 模糊控制器的输入
typedef struct {
    float error;  // 误差
    float delta;  // 误差变化率
} FuzzyInput;

// 模糊控制器的输出
typedef struct {
    float control;  // 控制量
} FuzzyOutput;

接下来，我们定义模糊控制器的模糊集和规则：

// 模糊集
typedef struct {
    char* name;        // 模糊集名称
    float* membership; // 模糊隶属度函数
    int size;          // 模糊集大小
} FuzzySet;

// 规则
typedef struct {
    int antecedent1; // 前提1所属模糊集的索引
    int antecedent2; // 前提2所属模糊集的索引
    int consequent;  // 结论所属模糊集的索引
    float weight;    // 规则权重
} FuzzyRule;

然后，我们定义模糊控制器的模糊集和规则：

// 模糊控制器的模糊集
FuzzySet errorSet = {
    .name = "error",
    .membership = {0, 0.2, 0.4, 0.6, 0.8, 1, 0.8, 0.6, 0.4, 0.2},
    .size = 10
};

FuzzySet deltaSet = {
    .name = "delta",
    .membership = {-1, -0.8, -0.6, -0.4, -0.2, 0, 0.2, 0.4, 0.6, 0.8, 1},
    .size = 11
};

FuzzySet controlSet = {
    .name = "control",
    .membership = {-1, -0.8, -0.6, -0.4, -0.2, 0, 0.2, 0.4, 0.6, 0.8, 1},
    .size = 11
};

// 模糊控制器的规则
FuzzyRule rules[] = {
    {.antecedent1 = 0, .antecedent2 = 0, .consequent = 0, .weight = 1},
    {.antecedent1 = 0, .antecedent2 = 1, .consequent = 1, .weight = 0.8},
    {.antecedent1 = 0, .antecedent2 = 2, .consequent = 2, .weight = 0.6},
    {.antecedent1 = 0, .antecedent2 = 3, .consequent = 3, .weight = 0.4},
    {.antecedent1 = 0, .antecedent2 = 4, .consequent = 4, .weight = 0.2},
    {.antecedent1 = 1, .antecedent2 = 0, .consequent = 1, .weight = 0.8},
    {.antecedent1 = 1, .antecedent2 = 1, .consequent = 2, .weight = 0.6},
    {.antecedent1 = 1, .antecedent2 = 2, .consequent = 3, .weight = 0.4},
    {.antecedent1 = 1, .antecedent2 = 3, .consequent = 4, .weight = 0.2},
    {.antecedent1 = 2, .antecedent2 = 0, .consequent = 2, .weight = 0.6},
    {.antecedent1 = 2, .antecedent2 = 1, .consequent = 3, .weight = 0.4},
    {.antecedent1 = 2, .antecedent2 = 2, .consequent = 4, .weight = 0.2},
    {.antecedent1 = 3, .antecedent2 = 0, .consequent = 3, .weight = 0.4},
    {.antecedent1 = 3, .antecedent2 = 1, .consequent = 4, .weight = 0.2},
    {.antecedent1 = 4, .antecedent2 = 0, .consequent = 4, .weight = 0.2},
};

// 模糊控制器的规则数量
const int numRules = sizeof(rules) / sizeof(FuzzyRule);

最后，我们可以编写模糊控制器的函数：

FuzzyOutput fuzzyController(FuzzyInput input) {
    // 计算每个输入值所属的模糊集
    int errorSetIndex = floor((input.error - (-1)) / (2.0 / errorSet.size));
    int deltaSetIndex = floor((input.delta - (-1)) / (2.0 / deltaSet.size));

    // 计算每个规则的前提部分的隶属度
    float antecedentMemberships[numRules];
    for (int i = 0; i < numRules; i++) {
        antecedentMemberships[i] = fminf(errorSet.membership[errorSetIndex + rules[i].antecedent1], deltaSet.membership[deltaSetIndex + rules[i].antecedent2]);
    }

    // 计算每个规则的结论部分的隶属度
    float consequentMemberships[numRules][controlSet.size];
    for (int i = 0; i < numRules; i++) {
        for (int j = 0; j < controlSet.size; j++) {
            consequentMemberships[i][j] = fminf(antecedentMemberships[i], controlSet.membership[j + rules[i].consequent]);
        }
    }

    // 计算控制量的隶属度
    float controlMemberships[controlSet.size];
    for (int i = 0; i < controlSet.size; i++) {
        controlMemberships[i] = 0;
        for (int j = 0; j < numRules; j++) {
            controlMemberships[i] = fmaxf(controlMemberships[i], consequentMemberships[j][i] * rules[j].weight);
        }
    }

    // 计算控制量的平均值
    float controlSum = 0;
    float membershipSum = 0;
    for (int i = 0; i < controlSet.size; i++) {
        controlSum += controlSet.membership[i] * controlMemberships[i];
        membershipSum += controlMemberships[i];
    }
    float control = controlSum / membershipSum;

    // 返回控制量
    FuzzyOutput output = {.control = control};
    return output;
}

这个模糊控制器函数接受一个 FuzzyInput 结构体作为输入，包含误差和误差变化率两个值。它返回一个 FuzzyOutput 结构体，包含控制量的值。在函数中，我们首先计算输入值所属的模糊集，然后计算每个规则的前提部分和结论部分的隶属度，并计算控制量的隶属度和平均值。最后返回控制量的值。

这只是一个简单的模糊控制器的示例，实际应用中可能需要更复杂的模糊集和规则，以及更复杂的控制算法。