{"id":986963,"date":"2024-12-27T07:49:28","date_gmt":"2024-12-26T23:49:28","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/986963.html"},"modified":"2024-12-27T07:49:29","modified_gmt":"2024-12-26T23:49:29","slug":"python%e5%a6%82%e4%bd%95%e5%88%b6%e4%bd%9c%e7%94%b5%e6%9c%ba%e6%a8%a1%e5%9e%8b","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/986963.html","title":{"rendered":"python\u5982\u4f55\u5236\u4f5c\u7535\u673a\u6a21\u578b"},"content":{"rendered":"

\"python\u5982\u4f55\u5236\u4f5c\u7535\u673a\u6a21\u578b\"<\/p>\n

\u5728Python\u4e2d\u5236\u4f5c\u7535\u673a\u6a21\u578b\uff0c\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528\u79d1\u5b66\u8ba1\u7b97\u5e93\u3001\u7269\u7406\u5efa\u6a21\u5de5\u5177\u548c\u53ef\u89c6\u5316\u5de5\u5177\u6765\u5b9e\u73b0\u3002\u9996\u5148\uff0c\u53ef\u4ee5\u4f7f\u7528NumPy\u548cSciPy\u8fdb\u884c\u6570\u5b66\u8ba1\u7b97\u3001\u5b9a\u4e49\u7535\u673a\u7684\u7269\u7406\u7279\u6027\u548c\u65b9\u7a0b\uff1b\u5176\u6b21\uff0c\u4f7f\u7528SimPy\u6216\u7c7b\u4f3c\u7684\u5e93\u8fdb\u884c\u4eff\u771f\uff1b\u6700\u540e\uff0c\u5229\u7528Matplotlib\u6216\u5176\u4ed6\u53ef\u89c6\u5316\u5de5\u5177\u5c55\u793a\u6a21\u578b\u7684\u8f93\u51fa\u548c\u884c\u4e3a\u3002<\/strong><\/p>\n<\/p>\n

\u4e00\u3001\u7406\u89e3\u7535\u673a\u6a21\u578b\u7684\u57fa\u7840\u539f\u7406<\/h3>\n<\/p>\n

\u5728\u5f00\u59cb\u4f7f\u7528Python\u5236\u4f5c\u7535\u673a\u6a21\u578b\u4e4b\u524d\uff0c\u5fc5\u987b\u9996\u5148\u7406\u89e3\u7535\u673a\u7684\u57fa\u672c\u5de5\u4f5c\u539f\u7406\u548c\u6570\u5b66\u6a21\u578b\u3002\u8fd9\u901a\u5e38\u5305\u62ec\u7535\u673a\u7684\u7535\u6c14\u6a21\u578b\u548c\u673a\u68b0\u6a21\u578b\uff1a<\/p>\n<\/p>\n

    \n
  1. \n

    \u7535\u6c14\u6a21\u578b<\/strong>\uff1a\u7535\u673a\u7684\u7535\u6c14\u6a21\u578b\u4e3b\u8981\u6d89\u53ca\u7535\u538b\u3001\u7535\u6d41\u548c\u7535\u611f\u7b49\u53c2\u6570\u3002\u76f4\u6d41\u7535\u673a\u7684\u7535\u6c14\u65b9\u7a0b\u53ef\u4ee5\u8868\u793a\u4e3a\uff1a
    [ V = L \\frac{di}{dt} + Ri + E ]
    \u5176\u4e2d\uff0c( V )\u662f\u7535\u538b\uff0c( L )\u662f\u7535\u611f\uff0c( R )\u662f\u7535\u963b\uff0c( i )\u662f\u7535\u6d41\uff0c( E )\u662f\u53cd\u7535\u52a8\u52bf\u3002<\/p>\n<\/p>\n<\/li>\n

  2. \n

    \u673a\u68b0\u6a21\u578b<\/strong>\uff1a\u673a\u68b0\u6a21\u578b\u6d89\u53ca\u8f6c\u77e9\u3001\u8f6c\u901f\u548c\u60ef\u6027\u7b49\u53c2\u6570\u3002\u57fa\u672c\u65b9\u7a0b\u4e3a\uff1a
    [ T = J \\frac{d\\omega}{dt} + B\\omega + T_l ]
    \u5176\u4e2d\uff0c( T )\u662f\u7535\u673a\u4ea7\u751f\u7684\u8f6c\u77e9\uff0c( J )\u662f\u8f6c\u52a8\u60ef\u91cf\uff0c( \\omega )\u662f\u89d2\u901f\u5ea6\uff0c( B )\u662f\u7c98\u6027\u6469\u64e6\u7cfb\u6570\uff0c( T_l )\u662f\u8d1f\u8f7d\u8f6c\u77e9\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n

    \u901a\u8fc7\u7406\u89e3\u8fd9\u4e9b\u57fa\u672c\u65b9\u7a0b\uff0c\u53ef\u4ee5\u4e3a\u7535\u673a\u5efa\u6a21\u5960\u5b9a\u57fa\u7840\u3002<\/p>\n<\/p>\n

    \u4e8c\u3001\u4f7f\u7528Python\u8fdb\u884c\u7535\u673a\u5efa\u6a21<\/h3>\n<\/p>\n

    1. \u4f7f\u7528NumPy\u548cSciPy\u8fdb\u884c\u6570\u5b66\u8ba1\u7b97<\/h4>\n<\/p>\n

    NumPy\u548cSciPy\u662fPython\u4e2d\u5f3a\u5927\u7684\u79d1\u5b66\u8ba1\u7b97\u5e93\uff0c\u9002\u7528\u4e8e\u5904\u7406\u7535\u673a\u6a21\u578b\u7684\u6570\u5b66\u8ba1\u7b97\u3002\u53ef\u4ee5\u4f7f\u7528\u8fd9\u4e9b\u5e93\u5b9a\u4e49\u7535\u673a\u7684\u53c2\u6570\u548c\u72b6\u6001\u65b9\u7a0b\uff1a<\/p>\n<\/p>\n

    import numpy as np<\/p>\n
    from scipy.integrate import odeint<\/p>\n
    def motor_dynamics(y, t, L, R, J, B, V, Kt, Ke, Tl):<\/p>\n
        i, omega = y<\/p>\n
        di_dt = (V - R * i - Ke * omega) \/ L<\/p>\n
        domega_dt = (Kt * i - B * omega - Tl) \/ J<\/p>\n
        return [di_dt, domega_dt]<\/p>\n
    \u53c2\u6570\u5b9a\u4e49<\/strong><\/h2>\n
    L = 0.5  # \u7535\u611f<\/p>\n
    R = 1.0  # \u7535\u963b<\/p>\n
    J = 0.01  # \u8f6c\u52a8\u60ef\u91cf<\/p>\n
    B = 0.1  # \u6469\u64e6\u7cfb\u6570<\/p>\n
    V = 12.0  # \u7535\u538b<\/p>\n
    Kt = 0.1  # \u8f6c\u77e9\u5e38\u6570<\/p>\n
    Ke = 0.1  # \u53cd\u7535\u52a8\u52bf\u5e38\u6570<\/p>\n
    Tl = 0.1  # \u8d1f\u8f7d\u8f6c\u77e9<\/p>\n
    \u521d\u59cb\u6761\u4ef6<\/strong><\/h2>\n
    y0 = [0.0, 0.0]<\/p>\n
    t = np.linspace(0, 5, 100)  # \u65f6\u95f4\u70b9<\/p>\n
    \u6c42\u89e3\u5fae\u5206\u65b9\u7a0b<\/strong><\/h2>\n
    solution = odeint(motor_dynamics, y0, t, args=(L, R, J, B, V, Kt, Ke, Tl))<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    2. \u4f7f\u7528SimPy\u8fdb\u884c\u4eff\u771f<\/h4>\n<\/p>\n
    SimPy\u662fPython\u4e2d\u7684\u4e00\u4e2a\u7528\u4e8e\u79bb\u6563\u4e8b\u4ef6\u4eff\u771f\u7684\u5e93\uff0c\u53ef\u4ee5\u7528\u4e8e\u6a21\u62df\u7535\u673a\u5728\u4e0d\u540c\u6761\u4ef6\u4e0b\u7684\u52a8\u6001\u884c\u4e3a\uff1a<\/p>\n<\/p>\n
    import simpy<\/p>\n
    def motor_sim(env, L, R, J, B, V, Kt, Ke, Tl):<\/p>\n
        i = 0.0<\/p>\n
        omega = 0.0<\/p>\n
        while True:<\/p>\n
            di_dt = (V - R * i - Ke * omega) \/ L<\/p>\n
            domega_dt = (Kt * i - B * omega - Tl) \/ J<\/p>\n
            i += di_dt * env.now<\/p>\n
            omega += domega_dt * env.now<\/p>\n
            print(f'Time {env.now}: Current {i:.2f}, Speed {omega:.2f}')<\/p>\n
            yield env.timeout(0.1)<\/p>\n
    env = simpy.Environment()<\/p>\n
    env.process(motor_sim(env, L, R, J, B, V, Kt, Ke, Tl))<\/p>\n
    env.run(until=5)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    3. \u4f7f\u7528Matplotlib\u8fdb\u884c\u7ed3\u679c\u53ef\u89c6\u5316<\/h4>\n<\/p>\n
    Matplotlib\u53ef\u4ee5\u7528\u4e8e\u7ed8\u5236\u7535\u673a\u6a21\u578b\u7684\u7ed3\u679c\uff0c\u4ee5\u4fbf\u76f4\u89c2\u5730\u5206\u6790\u7535\u673a\u7684\u6027\u80fd\uff1a<\/p>\n<\/p>\n
    import matplotlib.pyplot as plt<\/p>\n
    plt.figure()<\/p>\n
    plt.subplot(2, 1, 1)<\/p>\n
    plt.plot(t, solution[:, 0], 'b-', label='Current (i)')<\/p>\n
    plt.ylabel('Current (A)')<\/p>\n
    plt.legend()<\/p>\n
    plt.subplot(2, 1, 2)<\/p>\n
    plt.plot(t, solution[:, 1], 'r-', label='Speed (omega)')<\/p>\n
    plt.xlabel('Time (s)')<\/p>\n
    plt.ylabel('Speed (rad\/s)')<\/p>\n
    plt.legend()<\/p>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e09\u3001\u7535\u673a\u6a21\u578b\u7684\u9ad8\u7ea7\u5e94\u7528<\/h3>\n<\/p>\n
    1. \u5f15\u5165\u63a7\u5236\u7cfb\u7edf<\/h4>\n<\/p>\n
    \u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u7535\u673a\u901a\u5e38\u4e0e\u63a7\u5236\u7cfb\u7edf\u4e00\u8d77\u4f7f\u7528\uff0c\u4ee5\u5b9e\u73b0\u671f\u671b\u7684\u52a8\u6001\u54cd\u5e94\u3002\u53ef\u4ee5\u4f7f\u7528Python\u7684\u63a7\u5236\u7cfb\u7edf\u5e93\uff08\u5982\u63a7\u5236\u5e93\uff09\u6765\u8bbe\u8ba1\u548c\u6a21\u62df\u7535\u673a\u7684\u63a7\u5236\u7cfb\u7edf\uff1a<\/p>\n<\/p>\n
    import control as ctrl<\/p>\n
    \u5b9a\u4e49\u4f20\u9012\u51fd\u6570<\/strong><\/h2>\n
    num = [Kt]<\/p>\n
    den = [L*J, L*B + R*J, R*B + Ke*Kt]<\/p>\n
    motor_system = ctrl.TransferFunction(num, den)<\/p>\n
    \u8bbe\u8ba1PID\u63a7\u5236\u5668<\/strong><\/h2>\n
    Kp = 1.0<\/p>\n
    Ki = 0.1<\/p>\n
    Kd = 0.05<\/p>\n
    pid_controller = ctrl.TransferFunction([Kd, Kp, Ki], [1, 0])<\/p>\n
    \u95ed\u73af\u7cfb\u7edf<\/strong><\/h2>\n
    closed_loop_system = ctrl.feedback(pid_controller * motor_system)<\/p>\n
    \u4eff\u771f<\/strong><\/h2>\n
    t, y = ctrl.step_response(closed_loop_system, T=np.linspace(0, 5, 100))<\/p>\n
    plt.figure()<\/p>\n
    plt.plot(t, y)<\/p>\n
    plt.xlabel('Time (s)')<\/p>\n
    plt.ylabel('Speed (rad\/s)')<\/p>\n
    plt.title('Step Response with PID Controller')<\/p>\n
    plt.grid()<\/p>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    2. \u591a\u57df\u5efa\u6a21<\/h4>\n<\/p>\n
    \u7535\u673a\u7cfb\u7edf\u901a\u5e38\u6d89\u53ca\u591a\u4e2a\u7269\u7406\u57df\uff0c\u5982\u7535\u3001\u78c1\u3001\u673a\u68b0\u548c\u70ed\u57df\u3002\u53ef\u4ee5\u4f7f\u7528Python\u7684\u591a\u7269\u7406\u573a\u4eff\u771f\u5de5\u5177\uff08\u5982PySim\u6216OpenModelica\uff09\u8fdb\u884c\u591a\u57df\u5efa\u6a21\u3002\u8fd9\u4e9b\u5de5\u5177\u5141\u8bb8\u7528\u6237\u4f7f\u7528\u7edf\u4e00\u7684\u5efa\u6a21\u73af\u5883\u6765\u63cf\u8ff0\u548c\u4eff\u771f\u4e0d\u540c\u7269\u7406\u57df\u7684\u76f8\u4e92\u4f5c\u7528\u3002<\/p>\n<\/p>\n
    3. \u6570\u636e\u9a71\u52a8\u7684\u6a21\u578b<\/h4>\n<\/p>\n
    \u968f\u7740\u673a\u5668\u5b66\u4e60<\/a>\u548c\u6570\u636e\u79d1\u5b66\u7684\u53d1\u5c55\uff0c\u53ef\u4ee5\u4f7f\u7528\u6570\u636e\u9a71\u52a8\u7684\u65b9\u6cd5\u8fdb\u884c\u7535\u673a\u5efa\u6a21\u3002\u8fd9\u6d89\u53ca\u4f7f\u7528\u4f20\u611f\u5668\u6570\u636e\u548c\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u6765\u9884\u6d4b\u7535\u673a\u7684\u6027\u80fd\u548c\u6545\u969c\u3002\u53ef\u4ee5\u4f7f\u7528Python\u7684\u673a\u5668\u5b66\u4e60\u5e93\uff08\u5982scikit-learn\u6216TensorFlow\uff09\u6765\u5b9e\u73b0\u6570\u636e\u9a71\u52a8\u7684\u7535\u673a\u6a21\u578b\u3002<\/p>\n<\/p>\n
    \u56db\u3001\u7ed3\u8bba<\/h3>\n<\/p>\n
    \u4f7f\u7528Python\u8fdb\u884c\u7535\u673a\u5efa\u6a21\u662f\u4e00\u79cd\u7075\u6d3b\u4e14\u5f3a\u5927\u7684\u65b9\u6cd5\uff0c\u901a\u8fc7\u7ed3\u5408\u79d1\u5b66\u8ba1\u7b97\u3001\u4eff\u771f\u548c\u53ef\u89c6\u5316\u5de5\u5177\uff0c\u53ef\u4ee5\u6df1\u5165\u7406\u89e3\u7535\u673a\u7684\u52a8\u6001\u884c\u4e3a\u548c\u6027\u80fd\u3002\u901a\u8fc7\u8fdb\u4e00\u6b65\u5f15\u5165\u63a7\u5236\u7cfb\u7edf\u3001\u591a\u57df\u5efa\u6a21\u548c\u6570\u636e\u9a71\u52a8\u6a21\u578b\uff0c\u53ef\u4ee5\u6269\u5c55\u7535\u673a\u6a21\u578b\u7684\u5e94\u7528\u8303\u56f4\uff0c\u6ee1\u8db3\u5de5\u4e1a\u5e94\u7528\u4e2d\u7684\u5404\u79cd\u9700\u6c42\u3002\u901a\u8fc7\u6301\u7eed\u5b66\u4e60\u548c\u5b9e\u8df5\uff0c\u5de5\u7a0b\u5e08\u548c\u5f00\u53d1\u8005\u53ef\u4ee5\u66f4\u597d\u5730\u5229\u7528Python\u6765\u8bbe\u8ba1\u548c\u4f18\u5316\u7535\u673a\u7cfb\u7edf\u3002<\/p>\n<\/p>\n
    \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
     \u5982\u4f55\u5728Python\u4e2d\u521b\u5efa\u7535\u673a\u6a21\u578b\u7684\u6b65\u9aa4\u662f\u4ec0\u4e48\uff1f<\/strong>
    \u5728Python\u4e2d\u521b\u5efa\u7535\u673a\u6a21\u578b\u901a\u5e38\u5305\u62ec\u51e0\u4e2a\u6b65\u9aa4\u3002\u9996\u5148\uff0c\u60a8\u9700\u8981\u9009\u62e9\u4e00\u4e2a\u9002\u5408\u7684\u5e93\uff0c\u4f8b\u5982NumPy\u548cSciPy\uff0c\u8fd9\u4e9b\u5e93\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u5b66\u5de5\u5177\uff0c\u9002\u5408\u6a21\u62df\u7535\u673a\u7684\u52a8\u6001\u884c\u4e3a\u3002\u63a5\u7740\uff0c\u60a8\u53ef\u4ee5\u5b9a\u4e49\u7535\u673a\u7684\u53c2\u6570\uff0c\u5982\u7535\u538b\u3001\u963b\u6297\u548c\u8f6c\u52a8\u60ef\u91cf\u7b49\uff0c\u7136\u540e\u7f16\u5199\u65b9\u7a0b\u4ee5\u63cf\u8ff0\u7535\u673a\u7684\u8fd0\u52a8\u7279\u6027\u3002\u6700\u7ec8\uff0c\u4f7f\u7528Matplotlib\u7b49\u53ef\u89c6\u5316\u5de5\u5177\u6765\u5c55\u793a\u7535\u673a\u6a21\u578b\u7684\u54cd\u5e94\u548c\u6027\u80fd\u6570\u636e\u3002<\/p>\n
    \u7535\u673a\u6a21\u578b\u7684\u4eff\u771f\u9700\u8981\u54ea\u4e9b\u57fa\u672c\u53c2\u6570\uff1f<\/strong>
    \u7535\u673a\u6a21\u578b\u7684\u4eff\u771f\u901a\u5e38\u9700\u8981\u4e00\u4e9b\u57fa\u672c\u53c2\u6570\uff0c\u5305\u62ec\u7535\u673a\u7684\u989d\u5b9a\u529f\u7387\u3001\u989d\u5b9a\u7535\u538b\u3001\u8f6c\u901f\u3001\u626d\u77e9\u4ee5\u53ca\u7535\u673a\u7684\u963b\u6297\u548c\u8f6c\u52a8\u60ef\u91cf\u7b49\u3002\u8fd9\u4e9b\u53c2\u6570\u5c06\u5e2e\u52a9\u60a8\u51c6\u786e\u5730\u63cf\u8ff0\u7535\u673a\u7684\u884c\u4e3a\u3002\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u60a8\u8fd8\u53ef\u80fd\u9700\u8981\u8003\u8651\u8d1f\u8f7d\u6761\u4ef6\u3001\u6469\u64e6\u529b\u548c\u73af\u5883\u56e0\u7d20\uff0c\u8fd9\u5c06\u5f71\u54cd\u7535\u673a\u7684\u6027\u80fd\u3002<\/p>\n
    \u5728Python\u4e2d\u8c03\u8bd5\u7535\u673a\u6a21\u578b\u65f6\u9047\u5230\u95ee\u9898\u8be5\u5982\u4f55\u89e3\u51b3\uff1f<\/strong>
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