int PositionPID(float deviation,PID pid) { float Position_KP=pid.kp,Position_KI=pid.ki,Position_KD=pid.kd; int Pwm; static float Bias,Integral_bias,Last_Bias; Bias=deviation; //计算偏差 Integral_bias+=Bias; //求出偏差的积分 Pwm=Position_KP*Bias+Position_KI*Integral_bias+Position_KD*(Bias-Last_Bias); //位置式PID控制器 Last_Bias=Bias; //保存上一次偏差 return Pwm; }这个代码什么意思
时间: 2024-04-18 07:28:58 浏览: 38
这段代码是一个位置式PID控制器的实现。PID控制器是一种常用的控制算法,用于控制系统的稳定性和精确性。该函数的作用是根据给定的偏差值,计算出对应的PWM输出。下面是对代码的解释:
1. 首先,函数定义了一个名为PositionPID的函数,接受两个参数:偏差值(deviation)和PID参数结构体(pid)。
2. 接下来,函数内部声明了一些变量,包括位置PID控制器的比例系数(Position_KP)、积分系数(Position_KI)、微分系数(Position_KD),以及PWM输出(Pwm)、偏差(Bias)、偏差积分(Integral_bias)和上一次偏差(Last_Bias)。
3. Bias变量被赋值为输入的偏差值,表示当前的偏差。
4. Integral_bias变量累加了Bias变量,用于计算偏差的积分项。
5. Pwm变量通过使用位置式PID控制器的公式计算得到。公式中包括比例项(Position_KP * Bias)、积分项(Position_KI * Integral_bias)和微分项(Position_KD * (Bias - Last_Bias))。
6. Last_Bias变量被更新为当前的偏差值,以便在下一次计算中使用。
7. 最后,函数返回计算得到的PWM输出。
总体来说,这段代码实现了一个位置式PID控制器,通过计算偏差的比例、积分和微分项,得到对应的PWM输出,用于控制某个系统的位置。
相关问题
import numpy as np import pandas as pd import talib def initialize(context): context.symbol = 'BTCUSDT' context.window_size = 5 context.deviation = 1 context.trade_size = 0.01 context.stop_loss = 0.05 context.take_profit = 0.1 schedule_function(rebalance, date_rules.every_day(), time_rules.market_open()) def rebalance(context, data): price = data.history(context.symbol, 'close', context.window_size + 1, '1d') signal = mean_reversion_signal(price, context.window_size, context.deviation) current_position = context.portfolio.positions[context.symbol].amount if signal[-1] == 1 and current_position <= 0: target_position_size = context.trade_size / data.current(context.symbol, 'close') order_target_percent(context.symbol, target_position_size) elif signal[-1] == -1 and current_position >= 0: order_target(context.symbol, 0) elif current_position > 0: current_price = data.current(context.symbol, 'close') stop_loss_price = current_price * (1 - context.stop_loss) take_profit_price = current_price * (1 + context.take_profit) if current_price <= stop_loss_price or current_price >= take_profit_price: order_target(context.symbol, 0) def moving_average(x, n): ma = talib.SMA(x, timeperiod=n) return ma def std_deviation(x, n): std = talib.STDDEV(x, timeperiod=n) return std def mean_reversion_signal(price, window_size, deviation): ma = moving_average(price, window_size) std = std_deviation(price, window_size) upper_band = ma + deviation * std lower_band = ma - deviation * std signal = np.zeros_like(price) signal[price > upper_band] = -1 # 卖出信号 signal[price < lower_band] = 1 # 买入信号 return signal ''' 运行回测 ''' start_date = pd.to_datetime('2019-01-01', utc=True) end_date = pd.to_datetime('2021-01-01', utc=True) results = run_algorithm( start=start_date, end=end_date, initialize=initialize, capital_base=10000, data_frequency='daily', bundle='binance' ) ''' 查看回测结果 ''' print(results.portfolio_value)格式错误
这段代码存在一些格式错误。具体来说,第一行和第二行之间需要有一个空行,否则会提示语法错误。另外,代码中的注释也需要使用 # 符号进行标记。
以下是修改后的代码:
```
import numpy as np
import pandas as pd
import talib
def initialize(context):
context.symbol = 'BTCUSDT'
context.window_size = 5
context.deviation = 1
context.trade_size = 0.01
context.stop_loss = 0.05
context.take_profit = 0.1
schedule_function(rebalance, date_rules.every_day(), time_rules.market_open())
def rebalance(context, data):
price = data.history(context.symbol, 'close', context.window_size + 1, '1d')
signal = mean_reversion_signal(price, context.window_size, context.deviation)
current_position = context.portfolio.positions[context.symbol].amount
if signal[-1] == 1 and current_position <= 0:
target_position_size = context.trade_size / data.current(context.symbol, 'close')
order_target_percent(context.symbol, target_position_size)
elif signal[-1] == -1 and current_position >= 0:
order_target(context.symbol, 0)
elif current_position > 0:
current_price = data.current(context.symbol, 'close')
stop_loss_price = current_price * (1 - context.stop_loss)
take_profit_price = current_price * (1 + context.take_profit)
if current_price <= stop_loss_price or current_price >= take_profit_price:
order_target(context.symbol, 0)
def moving_average(x, n):
ma = talib.SMA(x, timeperiod=n)
return ma
def std_deviation(x, n):
std = talib.STDDEV(x, timeperiod=n)
return std
def mean_reversion_signal(price, window_size, deviation):
ma = moving_average(price, window_size)
std = std_deviation(price, window_size)
upper_band = ma + deviation * std
lower_band = ma - deviation * std
signal = np.zeros_like(price)
signal[price > upper_band] = -1 # 卖出信号
signal[price < lower_band] = 1 # 买入信号
return signal
''' 运行回测 '''
start_date = pd.to_datetime('2019-01-01', utc=True)
end_date = pd.to_datetime('2021-01-01', utc=True)
results = run_algorithm(
start=start_date,
end=end_date,
initialize=initialize,
capital_base=10000,
data_frequency='daily',
bundle='binance'
)
''' 查看回测结果 '''
print(results.portfolio_value)
```
upZscore = (data_heat - data_heat.mean(axis=0)) / data_heat.std(axis=0)
This code calculates the z-score of the data_heat variable. The z-score is a way to standardize the values of a variable by subtracting the mean and dividing by the standard deviation. This makes it easier to compare different variables that have different scales or units.
The code first calculates the mean of the data_heat variable along the 0 axis (which is usually the rows). It then subtracts this mean from each value in the data_heat variable. Next, it calculates the standard deviation of the data_heat variable along the 0 axis, and divides each value in the data_heat variable by this standard deviation.
The resulting upZscore variable will have a mean of 0 and a standard deviation of 1, which makes it easier to compare the values to other variables or to each other.
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