51单片机步进电机驱动程序源代码分享

版权申诉
单片机开发
C/C++
0 下载量 194 浏览量 更新于2024-11-05 收藏 29KB ZIP 举报
资源摘要信息:"步进电机驱动程序是由51单片机开发的，使用C/C++语言编写。这是一个非常适合初学者学习单片机开发的资源。" 知识点一：单片机开发 单片机是一种集成电路芯片，它可以完成微处理器的大部分功能，因此被称为单片微型计算机。单片机开发是指使用单片机进行各种电子设备的开发过程，包括硬件电路设计和软件程序设计。 知识点二：51单片机 51单片机是一种经典的单片机，其核心是8051微控制器。51单片机具有较强的控制能力，可以进行定时器/计数器、串行通信、中断系统等多种功能，因此被广泛应用于嵌入式系统、工业控制等领域。 知识点三：步进电机驱动 步进电机是一种电动机，它的特点是将电脉冲信号转换成角位移。步进电机驱动是指驱动步进电机进行精确控制的过程。步进电机驱动的方法有很多种，常见的有恒压驱动、恒流驱动、细分数驱动等。 知识点四：C/C++语言 C/C++语言是一种通用的编程语言，广泛应用于系统软件、游戏软件、应用软件的开发。C语言具有简洁、高效、灵活等特点，C++语言是在C语言的基础上增加了面向对象的特性。 知识点五：程序编写 程序编写是根据设计要求，使用编程语言将解决问题的方法转换成计算机可以执行的指令的过程。程序编写需要遵循一定的规则和标准，这些规则和标准被称为编程规范。 在这个资源中，作者使用51单片机和C/C++语言编写了一个步进电机驱动程序。这个程序可以帮助初学者了解和学习单片机开发和步进电机驱动的基本知识。

step-motor-control-accurately.zip_step motor

2022-07-15 上传
在这个名为"step-motor-control-accurately.zip"的压缩包中，我们可能找到了一个基于C语言编写的步进电机控制程序，该程序旨在实现步进电机的精确控制，包括防抖动功能。 步进电机的工作原理是通过接收一系列的脉冲...

STEP-BY-STEP--LUA.zip_lua_lua脚本_step by step

2022-09-24 上传
"STEP-BY-STEP--LUA.zip_lua_lua脚本_step by step" 这个标题表明，这是一个关于Lua脚本语言逐步学习的压缩包资源。它可能包含了逐步学习教程，帮助用户从基础到进阶掌握Lua编程。 **描述分析：** "Lua脚本语方学习...

81404578PPC---step--3k3.zip_control motor_predictive motor_pred

2022-07-14 上传
标题 "81404578PPC---step--3k3.zip_control motor_predictive motor_pred" 提示我们关注的是一个与永磁同步电机（Permanent Magnet Synchronous Motor, PMSM）相关的项目，该项目重点在于电机的预测控制策略。...

下面代码转化为paddle2.2.2代码 ：from __future__ import division import os, time, scipy.io import torch import torch.nn as nn import torch.optim as optim import numpy as np import glob import cv2 import argparse from PIL import Image from skimage.measure import compare_psnr,compare_ssim from tensorboardX import SummaryWriter from models import RViDeNet from utils import * parser = argparse.ArgumentParser(description='Pretrain denoising model') parser.add_argument('--gpu_id', dest='gpu_id', type=int, default=0, help='gpu id') parser.add_argument('--num_epochs', dest='num_epochs', type=int, default=33, help='num_epochs') parser.add_argument('--patch_size', dest='patch_size', type=int, default=128, help='patch_size') parser.add_argument('--batch_size', dest='batch_size', type=int, default=1, help='batch_size') args = parser.parse_args() os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu_id) save_dir = './pretrain_model' if not os.path.isdir(save_dir): os.makedirs(save_dir) gt_paths1 = glob.glob('./data/SRVD_data/raw_clean/MOT17-02_raw/*.tiff') gt_paths2 = glob.glob('./data/SRVD_data/raw_clean/MOT17-09_raw/*.tiff') gt_paths3 = glob.glob('./data/SRVD_data/raw_clean/MOT17-10_raw/*.tiff') gt_paths4 = glob.glob('./data/SRVD_data/raw_clean/MOT17-11_raw/*.tiff') gt_paths = gt_paths1 + gt_paths2 + gt_paths3 + gt_paths4 ps = args.patch_size # patch size for training batch_size = args.batch_size # batch size for training

122 浏览量
gt_paths1 = glob.glob('./data/SRVD_data/raw_clean/MOT17-02_raw/*.tiff') gt_paths2 = glob.glob('./data/SRVD_data/raw_clean/MOT17-09_raw/*.tiff') gt_paths3 = glob.glob('./data/SRVD_data/raw_clean/MOT17-...

if (g_adjustInf.passAdvance != 0) { if (job_info.m_nPass != 0) { signed long long step1, step2; if (printedBands % job_info.m_nPass == 0) { g_adjustInf.PassStarY = data_header.Inf.band_inf.band_Y; } step1 = (signed long long)((INT32S)data_header.Inf.band_inf.band_Y - g_adjustInf.PassStarY) * g_adjustInf.passStepValue / g_adjustInf.passAdvance + data_header.Inf.band_inf.shiftY; step2 = (signed long long)((INT32S)data_header.Inf.band_inf.nextBandY - g_adjustInf.PassStarY) * g_adjustInf.passStepValue / g_adjustInf.passAdvance + data_header.Inf.band_inf.shiftY; step_value = step2 - step1; } else { step_value = (signed long long)(data_header.Inf.band_inf.nextBandY - data_header.Inf.band_inf.band_Y) * g_adjustInf.passStepValue / g_adjustInf.passAdvance + data_header.Inf.band_inf.shiftY; } } else step_value = data_header.Inf.band_inf.step_distance;

125 浏览量
在整个条件语句的最后一个分支中，直接将`data_header.Inf.band_inf.step_distance`赋值给`step_value`。 这段代码的逻辑比较复杂，根据不同的条件执行不同的计算，最终得到`step_value`的值。

D:/LenovoQt/Tools/mingw1120_64/bin/mingw32-make -f Makefile.Debug mingw32-make[1]: Entering directory 'D:/OtTest/build-TestQtSDK20230708-Desktop_Qt_6_5_1_MinGW_64_bit-Debug' g++ -c -fno-keep-inline-dllexport -w<-Wunknown-pragmas> -g -std=gnu++1z -Wall -Wextra -Wextra -fexceptions -mthreads -DUNICODE -D_UNICODE -DWIN32 -DMINGW_HAS_SECURE_API=1 -DQT_QML_DEBUG -DQT_WIDGETS_LIB -DQT_GUI_LIB -DQT_XML_LIB -DQT_CORE_LIB -I../TestQtSDK20230708 -I. -I../TestQtSDK20230708 -I../../LenovoQt/6.5.1/mingw_64/include -I../../LenovoQt/6.5.1/mingw_64/include/QtWidgets -I../../LenovoQt/6.5.1/mingw_64/include/QtGui -I../../LenovoQt/6.5.1/mingw_64/include/QtXml -I../../LenovoQt/6.5.1/mingw_64/include/QtCore -Idebug -I/include -I../../LenovoQt/6.5.1/mingw_64/mkspecs/win32-g++ -o debug\configreadxml.o ..\TestQtSDK20230708\configreadxml.cpp 系统找不到指定的文件。 mingw32-make[1]: *** [Makefile.Debug:606: debug/configreadxml.o] Error 1 mingw32-make[1]: Leaving directory 'D:/OtTest/build-TestQtSDK20230708-Desktop_Qt_6_5_1_MinGW_64_bit-Debug' mingw32-make: *** [Makefile:45: debug] Error 2 23:54:42: 进程"D:\LenovoQt\Tools\mingw1120_64\bin\mingw32-make.exe"退出，退出代码 2 。 Error while building/deploying project TestQtSDK20230708 (kit: Desktop Qt 6.5.1 MinGW 64-bit) When executing step "Make" 23:54:42: Elapsed time: 00:00.

720 浏览量
When executing step "Make" ``` 这个错误表明在编译过程中，无法找到 `configreadxml.cpp` 文件，导致编译失败。 请确保以下几点： 1. 检查 `configreadxml.cpp` 文件的路径是否正确，它应该位于 `D:/OtTest/...

代码解释并给每行代码添加注释:class CosineAnnealingWarmbootingLR: def __init__(self, optimizer, epochs=0, eta_min=0.05, steps=[], step_scale=0.8, lf=None, batchs=0, warmup_epoch=0, epoch_scale=1.0): self.warmup_iters = batchs * warmup_epoch self.optimizer = optimizer self.eta_min = eta_min self.iters = -1 self.iters_batch = -1 self.base_lr = [group['lr'] for group in optimizer.param_groups] self.step_scale = step_scale steps.sort() self.steps = [warmup_epoch] + [i for i in steps if (i < epochs and i > warmup_epoch)] + [epochs] self.gap = 0 self.last_epoch = 0 self.lf = lf self.epoch_scale = epoch_scale for group in optimizer.param_groups: group.setdefault('initial_lr', group['lr']) def step(self, external_iter = None): self.iters += 1 if external_iter is not None: self.iters = external_iter iters = self.iters + self.last_epoch scale = 1.0 for i in range(len(self.steps)-1): if (iters <= self.steps[i+1]): self.gap = self.steps[i+1] - self.steps[i] iters = iters - self.steps[i] if i != len(self.steps)-2: self.gap += self.epoch_scale break scale *= self.step_scale if self.lf is None: for group, lr in zip(self.optimizer.param_groups, self.base_lr): group['lr'] = scale * lr * ((((1 + math.cos(iters * math.pi / self.gap)) / 2) ** 1.0) * (1.0 - self.eta_min) + self.eta_min) else: for group, lr in zip(self.optimizer.param_groups, self.base_lr): group['lr'] = scale * lr * self.lf(iters, self.gap) return self.optimizer.param_groups[0]['lr'] def step_batch(self): self.iters_batch += 1 if self.iters_batch < self.warmup_iters: rate = self.iters_batch / self.warmup_iters for group, lr in zip(self.optimizer.param_groups, self.base_lr): group['lr'] = lr * rate return self.optimizer.param_groups[0]['lr'] else: return None

225 浏览量
def __init__(self, optimizer, epochs=0, eta_min=0.05, steps=[], step_scale=0.8, lf=None, batchs=0, warmup_epoch=0, epoch_scale=1.0): # 初始化函数，接受一些参数 self.warmup_iters = batchs * warmup_...

给以下代码写注释，要求每行写一句：class CosineAnnealingWarmbootingLR: # cawb learning rate scheduler: given the warm booting steps, calculate the learning rate automatically def __init__(self, optimizer, epochs=0, eta_min=0.05, steps=[], step_scale=0.8, lf=None, batchs=0, warmup_epoch=0, epoch_scale=1.0): self.warmup_iters = batchs * warmup_epoch self.optimizer = optimizer self.eta_min = eta_min self.iters = -1 self.iters_batch = -1 self.base_lr = [group['lr'] for group in optimizer.param_groups] self.step_scale = step_scale steps.sort() self.steps = [warmup_epoch] + [i for i in steps if (i < epochs and i > warmup_epoch)] + [epochs] self.gap = 0 self.last_epoch = 0 self.lf = lf self.epoch_scale = epoch_scale # Initialize epochs and base learning rates for group in optimizer.param_groups: group.setdefault('initial_lr', group['lr']) def step(self, external_iter = None): self.iters += 1 if external_iter is not None: self.iters = external_iter # cos warm boot policy iters = self.iters + self.last_epoch scale = 1.0 for i in range(len(self.steps)-1): if (iters <= self.steps[i+1]): self.gap = self.steps[i+1] - self.steps[i] iters = iters - self.steps[i] if i != len(self.steps)-2: self.gap += self.epoch_scale break scale *= self.step_scale if self.lf is None: for group, lr in zip(self.optimizer.param_groups, self.base_lr): group['lr'] = scale * lr * ((((1 + math.cos(iters * math.pi / self.gap)) / 2) ** 1.0) * (1.0 - self.eta_min) + self.eta_min) else: for group, lr in zip(self.optimizer.param_groups, self.base_lr): group['lr'] = scale * lr * self.lf(iters, self.gap) return self.optimizer.param_groups[0]['lr'] def step_batch(self): self.iters_batch += 1 if self.iters_batch < self.warmup_iters: rate = self.iters_batch / self.warmup_iters for group, lr in zip(self.optimizer.param_groups, self.base_lr): group['lr'] = lr * rate return self.optimizer.param_groups[0]['lr'] else: return None

146 浏览量
这是一个类的定义，名字叫做CosineAnnealingWarmbootingLR。 def __init__(self, optimizer, T_max, T_mult=1, eta_min=0, last_epoch=-1): 这是类的初始化函数，接受五个参数：优化器optimizer、最大迭代次数T_...

这段代码中加一个test loss功能 class LSTM(nn.Module): def __init__(self, input_size, hidden_size, num_layers, output_size, batch_size, device): super().__init__() self.device = device self.input_size = input_size self.hidden_size = hidden_size self.num_layers = num_layers self.output_size = output_size self.num_directions = 1 # 单向LSTM self.batch_size = batch_size self.lstm = nn.LSTM(self.input_size, self.hidden_size, self.num_layers, batch_first=True) self.linear = nn.Linear(65536, self.output_size) def forward(self, input_seq): h_0 = torch.randn(self.num_directions * self.num_layers, self.batch_size, self.hidden_size).to(self.device) c_0 = torch.randn(self.num_directions * self.num_layers, self.batch_size, self.hidden_size).to(self.device) output, _ = self.lstm(input_seq, (h_0, c_0)) pred = self.linear(output.contiguous().view(self.batch_size, -1)) return pred if __name__ == '__main__': # 加载已保存的模型参数 saved_model_path = '/content/drive/MyDrive/危急值/model/dangerous.pth' device = 'cuda:0' lstm_model = LSTM(input_size=1, hidden_size=64, num_layers=1, output_size=3, batch_size=256, device='cuda:0').to(device) state_dict = torch.load(saved_model_path) lstm_model.load_state_dict(state_dict) dataset = ECGDataset(X_train_df.to_numpy()) dataloader = DataLoader(dataset, batch_size=256, shuffle=True, num_workers=0, drop_last=True) loss_fn = nn.CrossEntropyLoss() optimizer = optim.SGD(lstm_model.parameters(), lr=1e-4) for epoch in range(200000): print(f'epoch:{epoch}') lstm_model.train() epoch_bar = tqdm(dataloader) for x, y in epoch_bar: optimizer.zero_grad() x_out = lstm_model(x.to(device).type(torch.cuda.FloatTensor)) loss = loss_fn(x_out, y.long().to(device)) loss.backward() epoch_bar.set_description(f'loss:{loss.item():.4f}') optimizer.step() if epoch % 100 == 0 or epoch == epoch - 1: torch.save(lstm_model.state_dict(), "/content/drive/MyDrive/危急值/model/dangerous.pth") print("权重成功保存一次")

172 浏览量
optimizer.step() # 计算测试集上的平均损失 test_loss = 0.0 lstm_model.eval() with torch.no_grad(): for x, y in test_loader: x_out = lstm_model(x.to(device).type(torch.cuda.FloatTensor)) loss ...

class RandomWalk(): def __init__(self,point_num=5000): self.point_num = point_num self.xval = [0] self.yval = [0] def fill_walk(self): while len(self.xval)<self.point_num: x_direction = choice([1,-1]) step_num = choice([0,1,2,3,4]) xstep = x_direction * step_num y_direction = choice([1,-1]) step_num = choice([0,1,2,3,4]) ystep = y_direction * step_num if xstep == 0 and ystep==0: continue x_next = self.xval[-1] + xstep y_next = self.yval[-1] + ystep self.xval.append(x_next) self.yval.append(y_next) rw = RandomWalk(50000) rw.fill_walk() point_numbers = list(range(rw.point_num())) plt.scatter(rw.x_val,rw.y_val,c=point_numbers,cmap=plt.cm.Blues,edgecolor = 'none',s=2) plt.scatter(0,0,c='green',edgecolors='none',s=100) plt.scatter(rw.x_val[-1],rw.y_val[-1],c='red',edgecolor='none',s=100) plt.axes().get_xaxis().set_visible(False) plt.axes().get_xaxis().set_yisible(False) plt.show()修改代码

2023-05-27 上传
plt.scatter(rw.x_val, rw.y_val, c=point_numbers, cmap=plt.cm.Blues, edgecolor='none', s=2) plt.scatter(0, 0, c='green', edgecolors='none', s=100) plt.scatter(rw.x_val[-1], rw.y_val[-1], c='red', ...
pudn01
  • 粉丝: 49
  • 资源: 4万+
上传资源 快速赚钱

最新资源