编程人 cdmana.com

Arduino ten filtering algorithm program

Arduino Ten filtering algorithm program complete ( Accurate Edition )

【 Reprint 】https://www.geek-workshop.com/thread-7694-1-1.html

  • Limiting filtering ( Program judgment filtering )
  • Median filtering
  • Arithmetic average filtering
  • Recursive average filtering ( Moving average filtering )
  • Median average filtering method ( Anti pulse interference average filtering method )
  • Limiting average filtering method
  • First order lag filtering
  • Weighted recursive average filtering method
  • Dithering filtering method
  • The method of limiting amplitude and eliminating dithering
  • Kalman filtering ( Non extended Kalman )

The program defaults to int Type data to filter , If you need to filter other types , Just put all the int Replace with longfloat perhaps double that will do .

 

Limiting filtering ( Program judgment filtering )

/*
A、 name : Limiting filtering ( It is also called program judgment filtering method )
B、 Method :
     Judge by experience , Determine the maximum allowable deviation between two samples ( Set to A),
     Judge every time a new value is detected :
     If the difference between the current value and the last value <=A, Then this value is valid ,
     If the difference between the current value and the last value >A, Then this value is invalid , Abandon this value , Replace the current value with the last value .
C、 advantage :
     It can effectively overcome the impulse interference caused by accidental factors .
D、 shortcoming :
     There's no way to suppress that periodic interference .
     Poor smoothness .
E、 Arrangement :shenhaiyu 2013-11-01
*/

int Filter_Value;
int Value;

void setup() {
  Serial.begin(9600);       //  Initialize serial communication 
  randomSeed(analogRead(0)); //  Generate random seeds 
  Value = 300;
}

void loop() {
  Filter_Value = Filter();       //  Get the filter output value 
  Value = Filter_Value;          //  The value of the last valid sample , This variable is a global variable 
  Serial.println(Filter_Value); //  Serial output 
  delay(50);
}

//  Used to randomly generate a 300 The current value around 
int Get_AD() {
  return random(295, 305);
}

//  Limiting filtering ( It is also called program judgment filtering method )
#define FILTER_A 1
int Filter() {
  int NewValue;
  NewValue = Get_AD();
  if(((NewValue - Value) > FILTER_A) || ((Value - NewValue) > FILTER_A))
    return Value;
  else
    return NewValue;
}

Median filtering

/*
A、 name : Median filtering 
B、 Method :
     Continuous sampling N Time (N Take an odd number ), hold N The subsamples are sorted by size ,
     Take the intermediate value as the current effective value .
C、 advantage :
     It can effectively overcome the fluctuation interference caused by accidental factors ;
     For temperature 、 The measured parameters with slow change of liquid level have good filtering effect .
D、 shortcoming :
     For flow 、 Fast changing parameters such as speed should not be .
E、 Arrangement :shenhaiyu 2013-11-01
*/

int Filter_Value;

void setup() {
  Serial.begin(9600);       //  Initialize serial communication 
  randomSeed(analogRead(0)); //  Generate random seeds 
}

void loop() {
  Filter_Value = Filter();       //  Get the filter output value 
  Serial.println(Filter_Value); //  Serial output 
  delay(50);
}

//  Used to randomly generate a 300 The current value around 
int Get_AD() {
  return random(295, 305);
}

//  Median filtering 
#define FILTER_N 101
int Filter() {
  int filter_buf[FILTER_N];
  int i, j;
  int filter_temp;
  for(i = 0; i < FILTER_N; i++) {
    filter_buf[i] = Get_AD();
    delay(1);
  }
  //  Sample values are arranged from small to large ( Bubbling )
  for(j = 0; j < FILTER_N - 1; j++) {
    for(i = 0; i < FILTER_N - 1 - j; i++) {
      if(filter_buf[i] > filter_buf[i + 1]) {
        filter_temp = filter_buf[i];
        filter_buf[i] = filter_buf[i + 1];
        filter_buf[i + 1] = filter_temp;
      }
    }
  }
  return filter_buf[(FILTER_N - 1) / 2];
}

 

Arithmetic average filtering

/*
A、 name : Arithmetic average filtering 
B、 Method :
     Continuous access N Sample values are arithmetic averaged :
    N When it's worth more : High signal smoothness , But the sensitivity is low ;
    N It's worth less : The signal smoothness is low , But the sensitivity is high ;
    N Value selection : General flow ,N=12; pressure :N=4.
C、 advantage :
     It is suitable for filtering signals with random interference ;
     This signal is characterized by an average value , The signal fluctuates up and down near a certain range of values .
D、 shortcoming :
     It is not suitable for real-time control with slow measurement speed or fast data calculation speed ;
     More wasteful RAM.
E、 Arrangement :shenhaiyu 2013-11-01
*/

int Filter_Value;

void setup() {
  Serial.begin(9600);       //  Initialize serial communication 
  randomSeed(analogRead(0)); //  Generate random seeds 
}

void loop() {
  Filter_Value = Filter();       //  Get the filter output value 
  Serial.println(Filter_Value); //  Serial output 
  delay(50);
}

//  Used to randomly generate a 300 The current value around 
int Get_AD() {
  return random(295, 305);
}

//  Arithmetic average filtering 
#define FILTER_N 12
int Filter() {
  int i;
  int filter_sum = 0;
  for(i = 0; i < FILTER_N; i++) {
    filter_sum += Get_AD();
    delay(1);
  }
  return (int)(filter_sum / FILTER_N);
}

 

Recursive average filtering ( Moving average filtering )

/*
A、 name : Recursive average filtering ( It is also called moving average filtering method )
B、 Method :
     Take the continuous acquisition of N Sample values as a queue , The length of the queue is fixed to N,
     Every time a new data is sampled, it is put at the end of the queue , And throw away the data of the original team leader ( First in, first out principle ),
     Put... In the queue N Arithmetic average of data , New filtering results are obtained .
    N Value selection : Traffic ,N=12; pressure ,N=4; liquid surface ,N=4-12; temperature ,N=1-4.
C、 advantage :
     It has a good inhibiting effect on periodic interference , High smoothness ;
     For systems with high frequency oscillations .
D、 shortcoming :
     Low sensitivity , The inhibition of the occasional impulsive interference is poor ;
     It is not easy to eliminate the sampling value deviation caused by pulse interference ;
     It is not suitable for the occasion of serious pulse interference ;
     More wasteful RAM.
E、 Arrangement :shenhaiyu 2013-11-01
*/

int Filter_Value;

void setup() {
  Serial.begin(9600);       //  Initialize serial communication 
  randomSeed(analogRead(0)); //  Generate random seeds 
}

void loop() {
  Filter_Value = Filter();       //  Get the filter output value 
  Serial.println(Filter_Value); //  Serial output 
  delay(50);
}

//  Used to randomly generate a 300 The current value around 
int Get_AD() {
  return random(295, 305);
}

//  Recursive average filtering ( It is also called moving average filtering method )
#define FILTER_N 12
int filter_buf[FILTER_N + 1];
int Filter() {
  int i;
  int filter_sum = 0;
  filter_buf[FILTER_N] = Get_AD();
  for(i = 0; i < FILTER_N; i++) {
    filter_buf[i] = filter_buf[i + 1]; //  All data moves left , The low post is still falling 
    filter_sum += filter_buf[i];
  }
  return (int)(filter_sum / FILTER_N);
}

 

Median average filtering method ( Anti pulse interference average filtering method )

/*
A、 name : Median average filtering method ( It is also called the average filtering method of anti pulse interference )
B、 Method :
     Take a group of queues, remove the maximum and minimum, and take the average ,
     amount to “ Median filtering ”+“ Arithmetic average filtering ”.
     Continuous sampling N Data , Take out a maximum and a minimum ,
     And then calculate N-2 The arithmetic mean of data .
    N Value selection :3-14.
C、 advantage :
     Integrated “ Median filtering ”+“ Arithmetic average filtering ” The advantages of the two filtering methods .
     For occasional impulsive interference , It can eliminate the sampling value deviation caused by it .
     It has a good inhibition on the cycle .
     High smoothness , A system suitable for high frequency oscillation .
D、 shortcoming :
     The calculation speed is slow , It's the same as arithmetic average filtering .
     More wasteful RAM.
E、 Arrangement :shenhaiyu 2013-11-01
*/

int Filter_Value;

void setup() {
  Serial.begin(9600);       //  Initialize serial communication 
  randomSeed(analogRead(0)); //  Generate random seeds 
}

void loop() {
  Filter_Value = Filter();       //  Get the filter output value 
  Serial.println(Filter_Value); //  Serial output 
  delay(50);
}

//  Used to randomly generate a 300 The current value around 
int Get_AD() {
  return random(295, 305);
}

//  Median average filtering method ( It is also called the average filtering method of anti pulse interference )( Algorithm 1)
#define FILTER_N 100
int Filter() {
  int i, j;
  int filter_temp, filter_sum = 0;
  int filter_buf[FILTER_N];
  for(i = 0; i < FILTER_N; i++) {
    filter_buf[i] = Get_AD();
    delay(1);
  }
  //  Sample values are arranged from small to large ( Bubbling )
  for(j = 0; j < FILTER_N - 1; j++) {
    for(i = 0; i < FILTER_N - 1 - j; i++) {
      if(filter_buf[i] > filter_buf[i + 1]) {
        filter_temp = filter_buf[i];
        filter_buf[i] = filter_buf[i + 1];
        filter_buf[i + 1] = filter_temp;
      }
    }
  }
  //  Average after removing the maximum and minimum extremum 
  for(i = 1; i < FILTER_N - 1; i++) filter_sum += filter_buf[i];
  return filter_sum / (FILTER_N - 2);
}


//   Median average filtering method ( It is also called the average filtering method of anti pulse interference )( Algorithm 2)
/*
#define FILTER_N 100
int Filter() {
  int i;
  int filter_sum = 0;
  int filter_max, filter_min;
  int filter_buf[FILTER_N];
  for(i = 0; i < FILTER_N; i++) {
    filter_buf[i] = Get_AD();
    delay(1);
  }
  filter_max = filter_buf[0];
  filter_min = filter_buf[0];
  filter_sum = filter_buf[0];
  for(i = FILTER_N - 1; i > 0; i--) {
    if(filter_buf[i] > filter_max)
      filter_max=filter_buf[i];
    else if(filter_buf[i] < filter_min)
      filter_min=filter_buf[i];
    filter_sum = filter_sum + filter_buf[i];
    filter_buf[i] = filter_buf[i - 1];
  }
  i = FILTER_N - 2;
  filter_sum = filter_sum - filter_max - filter_min + i / 2; // +i/2  The purpose is to round 
  filter_sum = filter_sum / i;
  return filter_sum;
}*/

 

Limiting average filtering method

/*
A、 name : Limiting average filtering method 
B、 Method :
     amount to “ Limiting filtering ”+“ Recursive average filtering ”;
     Each time the new data is sampled, the amplitude is limited ,
     Then it is sent to the queue for recursive average filtering .
C、 advantage :
     It combines the advantages of the two filtering methods ;
     For occasional impulsive interference , It can eliminate the sampling value deviation caused by pulse interference .
D、 shortcoming :
     More wasteful RAM.
E、 Arrangement :shenhaiyu 2013-11-01
*/

#define FILTER_N 12
int Filter_Value;
int filter_buf[FILTER_N];

void setup() {
  Serial.begin(9600);       //  Initialize serial communication 
  randomSeed(analogRead(0)); //  Generate random seeds 
  filter_buf[FILTER_N - 2] = 300;
}

void loop() {
  Filter_Value = Filter();       //  Get the filter output value 
  Serial.println(Filter_Value); //  Serial output 
  delay(50);
}

//  Used to randomly generate a 300 The current value around 
int Get_AD() {
  return random(295, 305);
}

//  Limiting average filtering method 
#define FILTER_A 1
int Filter() {
  int i;
  int filter_sum = 0;
  filter_buf[FILTER_N - 1] = Get_AD();
  if(((filter_buf[FILTER_N - 1] - filter_buf[FILTER_N - 2]) > FILTER_A) || ((filter_buf[FILTER_N - 2] - filter_buf[FILTER_N - 1]) > FILTER_A))
    filter_buf[FILTER_N - 1] = filter_buf[FILTER_N - 2];
  for(i = 0; i < FILTER_N - 1; i++) {
    filter_buf[i] = filter_buf[i + 1];
    filter_sum += filter_buf[i];
  }
  return (int)filter_sum / (FILTER_N - 1);
}

 

First order lag filtering

/*
A、 name : First order lag filtering 
B、 Method :
     take a=0-1, The result of this filtering is =(1-a)* This sample value +a* Last filter result .
C、 advantage :
     It has a good inhibiting effect on periodic interference ;
     It is suitable for the occasion with high fluctuation frequency .
D、 shortcoming :
     Phase lag , Low sensitivity ;
     The degree of lag depends on a Value size ;
     Can not eliminate the filter frequency higher than the sampling frequency 1/2 The interference signal of .
E、 Arrangement :shenhaiyu 2013-11-01
*/

int Filter_Value;
int Value;

void setup() {
  Serial.begin(9600);       //  Initialize serial communication 
  randomSeed(analogRead(0)); //  Generate random seeds 
  Value = 300;
}

void loop() {
  Filter_Value = Filter();       //  Get the filter output value 
  Serial.println(Filter_Value); //  Serial output 
  delay(50);
}

//  Used to randomly generate a 300 The current value around 
int Get_AD() {
  return random(295, 305);
}

//  First order lag filtering 
#define FILTER_A 0.01
int Filter() {
  int NewValue;
  NewValue = Get_AD();
  Value = (int)((float)NewValue * FILTER_A + (1.0 - FILTER_A) * (float)Value);
  return Value;
}

 

Weighted recursive average filtering method

/*
A、 name : Weighted recursive average filtering method 
B、 Method :
     It is an improvement of the recursive average filtering method , That is, the data at different times are given different weights ;
     Usually , The closer we get to the current data , The more power you have .
     The larger the weight coefficient given to the new sample value , The higher the sensitivity , But the less smooth the signal is .
C、 advantage :
     It is suitable for the object with large delay time constant , And systems with shorter sampling periods .
D、 shortcoming :
     For pure delay, the time constant is small 、 The sampling period is longer 、 Signals of slow change ;
     Can't quickly respond to the severity of the system's current interference , The filtering effect is poor .
E、 Arrangement :shenhaiyu 2013-11-01
*/

int Filter_Value;

void setup() {
  Serial.begin(9600);       //  Initialize serial communication 
  randomSeed(analogRead(0)); //  Generate random seeds 
}

void loop() {
  Filter_Value = Filter();       //  Get the filter output value 
  Serial.println(Filter_Value); //  Serial output 
  delay(50);
}

//  Used to randomly generate a 300 The current value around 
int Get_AD() {
  return random(295, 305);
}

//  Weighted recursive average filtering method 
#define FILTER_N 12
int coe[FILTER_N] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12};    //  Table of weighting coefficients 
int sum_coe = 1 + 2 + 3 + 4 + 5 + 6 + 7 + 8 + 9 + 10 + 11 + 12; //  Weighted coefficients and 
int filter_buf[FILTER_N + 1];
int Filter() {
  int i;
  int filter_sum = 0;
  filter_buf[FILTER_N] = Get_AD();
  for(i = 0; i < FILTER_N; i++) {
    filter_buf[i] = filter_buf[i + 1]; //  All data moves left , The low post is still falling 
    filter_sum += filter_buf[i] * coe[i];
  }
  filter_sum /= sum_coe;
  return filter_sum;
}

 

Dithering filtering method

/*
A、 name : Dithering filtering method 
B、 Method :
     Set up a filter counter , Compare each sample value with the current valid value :
     If the sample value = Currently valid value , Then the counter is cleared ;
     If the sample value <> Currently valid value , Then the counter +1, And determine whether the counter is >= ceiling N( overflow );
     If the counter overflows , The current value will be replaced by the current value , And clear the counter .
C、 advantage :
     It has a good filtering effect for the measured parameters which change slowly ;
     It can avoid the repeated opening of the controller near the critical value / Turn off the beat or the value jitter on the display .
D、 shortcoming :
     It is not suitable for fast changing parameters ;
     If the value sampled at the time the counter overflows is exactly the interference value , The interference value will be imported into the system as a valid value .
E、 Arrangement :shenhaiyu 2013-11-01
*/

int Filter_Value;
int Value;

void setup() {
  Serial.begin(9600);       //  Initialize serial communication 
  randomSeed(analogRead(0)); //  Generate random seeds 
  Value = 300;
}

void loop() {
  Filter_Value = Filter();       //  Get the filter output value 
  Serial.println(Filter_Value); //  Serial output 
  delay(50);
}

//  Used to randomly generate a 300 The current value around 
int Get_AD() {
  return random(295, 305);
}

//  Dithering filtering method 
#define FILTER_N 12
int i = 0;
int Filter() {
  int new_value;
  new_value = Get_AD();
  if(Value != new_value) {
    i++;
    if(i > FILTER_N) {
      i = 0;
      Value = new_value;
    }
  }
  else
    i = 0;
  return Value;
}

 

The method of limiting amplitude and eliminating dithering

/*
A、 name : The method of limiting amplitude and eliminating dithering 
B、 Method :
     amount to “ Limiting filtering ”+“ Dithering filtering method ”;
     First limit the amplitude , After the shaking .
C、 advantage :
     Inherited “ Limiting ” and “ Desquamation ” The advantages of ;
     Improved “ Dithering filtering method ” Some of the flaws in , Avoid introducing interference values into the system .
D、 shortcoming :
     It is not suitable for fast changing parameters .
E、 Arrangement :shenhaiyu 2013-11-01
*/

int Filter_Value;
int Value;

void setup() {
  Serial.begin(9600);       //  Initialize serial communication 
  randomSeed(analogRead(0)); //  Generate random seeds 
  Value = 300;
}

void loop() {
  Filter_Value = Filter();       //  Get the filter output value 
  Serial.println(Filter_Value); //  Serial output 
  delay(50);
}

//  Used to randomly generate a 300 The current value around 
int Get_AD() {
  return random(295, 305);
}

//  The method of limiting amplitude and eliminating dithering 
#define FILTER_A 1
#define FILTER_N 5
int i = 0;
int Filter() {
  int NewValue;
  int new_value;
  NewValue = Get_AD();
  if(((NewValue - Value) > FILTER_A) || ((Value - NewValue) > FILTER_A))
    new_value = Value;
  else
    new_value = NewValue;
  if(Value != new_value) {
    i++;
    if(i > FILTER_N) {
      i = 0;
      Value = new_value;
    }
  }
  else
    i = 0;
  return Value;
}

 

Kalman filtering ( Non extended Kalman )

#include <Wire.h> // I2C library, gyroscope

// Accelerometer ADXL345
#define ACC (0x53)    //ADXL345 ACC address
#define A_TO_READ (6)        //num of bytes we are going to read each time (two bytes for each axis)


// Gyroscope ITG3200 
#define GYRO 0x68 // gyro address, binary = 11101000 when AD0 is connected to Vcc (see schematics of your breakout board)
#define G_SMPLRT_DIV 0x15   
#define G_DLPF_FS 0x16   
#define G_INT_CFG 0x17
#define G_PWR_MGM 0x3E

#define G_TO_READ 8 // 2 bytes for each axis x, y, z


// offsets are chip specific. 
int a_offx = 0;
int a_offy = 0;
int a_offz = 0;

int g_offx = 0;
int g_offy = 0;
int g_offz = 0;



char str[512]; 

void initAcc() {
  //Turning on the ADXL345
  writeTo(ACC, 0x2D, 0);      
  writeTo(ACC, 0x2D, 16);
  writeTo(ACC, 0x2D, 8);
  //by default the device is in +-2g range reading
}

void getAccelerometerData(int* result) {
  int regAddress = 0x32;    //first axis-acceleration-data register on the ADXL345
  byte buff[A_TO_READ];
  
  readFrom(ACC, regAddress, A_TO_READ, buff); //read the acceleration data from the ADXL345
  
  //each axis reading comes in 10 bit resolution, ie 2 bytes.  Least Significat Byte first!!
  //thus we are converting both bytes in to one int
  result[0] = (((int)buff[1]) << 8) | buff[0] + a_offx;   
  result[1] = (((int)buff[3]) << 8) | buff[2] + a_offy;
  result[2] = (((int)buff[5]) << 8) | buff[4] + a_offz;
}

//initializes the gyroscope
void initGyro()
{
  /*****************************************
  * ITG 3200
  * power management set to:
  * clock select = internal oscillator
  *     no reset, no sleep mode
  *   no standby mode
  * sample rate to = 125Hz
  * parameter to +/- 2000 degrees/sec
  * low pass filter = 5Hz
  * no interrupt
  ******************************************/
  writeTo(GYRO, G_PWR_MGM, 0x00);
  writeTo(GYRO, G_SMPLRT_DIV, 0x07); // EB, 50, 80, 7F, DE, 23, 20, FF
  writeTo(GYRO, G_DLPF_FS, 0x1E); // +/- 2000 dgrs/sec, 1KHz, 1E, 19
  writeTo(GYRO, G_INT_CFG, 0x00);
}


void getGyroscopeData(int * result)
{
  /**************************************
  Gyro ITG-3200 I2C
  registers:
  temp MSB = 1B, temp LSB = 1C
  x axis MSB = 1D, x axis LSB = 1E
  y axis MSB = 1F, y axis LSB = 20
  z axis MSB = 21, z axis LSB = 22
  *************************************/

  int regAddress = 0x1B;
  int temp, x, y, z;
  byte buff[G_TO_READ];
  
  readFrom(GYRO, regAddress, G_TO_READ, buff); //read the gyro data from the ITG3200
  
  result[0] = ((buff[2] << 8) | buff[3]) + g_offx;
  result[1] = ((buff[4] << 8) | buff[5]) + g_offy;
  result[2] = ((buff[6] << 8) | buff[7]) + g_offz;
  result[3] = (buff[0] << 8) | buff[1]; // temperature
  
}


float xz=0,yx=0,yz=0;
float p_xz=1,p_yx=1,p_yz=1;
float q_xz=0.0025,q_yx=0.0025,q_yz=0.0025;
float k_xz=0,k_yx=0,k_yz=0;
float r_xz=0.25,r_yx=0.25,r_yz=0.25;
  //int acc_temp[3];
  //float acc[3];
  int acc[3];
  int gyro[4];
  float Axz;
  float Ayx;
  float Ayz;
  float t=0.025;
void setup()
{
  Serial.begin(9600);
  Wire.begin();
  initAcc();
  initGyro();
  
}

//unsigned long timer = 0;
//float o;
void loop()
{
  
  getAccelerometerData(acc);
  getGyroscopeData(gyro);
  //timer = millis();
  sprintf(str, "%d,%d,%d,%d,%d,%d", acc[0],acc[1],acc[2],gyro[0],gyro[1],gyro[2]);
  
  //acc[0]=acc[0];
  //acc[2]=acc[2];
  //acc[1]=acc[1];
  //r=sqrt(acc[0]*acc[0]+acc[1]*acc[1]+acc[2]*acc[2]);
  gyro[0]=gyro[0]/ 14.375;
  gyro[1]=gyro[1]/ (-14.375);
  gyro[2]=gyro[2]/ 14.375;
  
   
  Axz=(atan2(acc[0],acc[2]))*180/PI;
  Ayx=(atan2(acc[0],acc[1]))*180/PI;
  /*if((acc[0]!=0)&&(acc[1]!=0))
    {
      Ayx=(atan2(acc[0],acc[1]))*180/PI;
    }
    else
    {
      Ayx=t*gyro[2];
    }*/
  Ayz=(atan2(acc[1],acc[2]))*180/PI;
  
  
 //kalman filter
  calculate_xz();
  calculate_yx();
  calculate_yz();
  
  //sprintf(str, "%d,%d,%d", xz_1, xy_1, x_1);
  //Serial.print(xz);Serial.print(",");
  //Serial.print(yx);Serial.print(",");
  //Serial.print(yz);Serial.print(",");
  //sprintf(str, "%d,%d,%d,%d,%d,%d", acc[0],acc[1],acc[2],gyro[0],gyro[1],gyro[2]);
  //sprintf(str, "%d,%d,%d",gyro[0],gyro[1],gyro[2]);
    Serial.print(Axz);Serial.print(",");
    //Serial.print(Ayx);Serial.print(",");
    //Serial.print(Ayz);Serial.print(",");
  //Serial.print(str);
  //o=gyro[2];//w=acc[2];
  //Serial.print(o);Serial.print(",");
  //Serial.print(w);Serial.print(",");
  Serial.print("\n");

  
  //delay(50);
}
void calculate_xz()
{

 xz=xz+t*gyro[1];
 p_xz=p_xz+q_xz;
 k_xz=p_xz/(p_xz+r_xz);
 xz=xz+k_xz*(Axz-xz);
 p_xz=(1-k_xz)*p_xz;
}
void calculate_yx()
{
  
  yx=yx+t*gyro[2];
  p_yx=p_yx+q_yx;
  k_yx=p_yx/(p_yx+r_yx);
  yx=yx+k_yx*(Ayx-yx);
  p_yx=(1-k_yx)*p_yx;

}
void calculate_yz()
{
  yz=yz+t*gyro[0];
  p_yz=p_yz+q_yz;
  k_yz=p_yz/(p_yz+r_yz);
  yz=yz+k_yz*(Ayz-yz);
  p_yz=(1-k_yz)*p_yz;
 
}


//---------------- Functions
//Writes val to address register on ACC
void writeTo(int DEVICE, byte address, byte val) {
   Wire.beginTransmission(DEVICE); //start transmission to ACC 
   Wire.write(address);        // send register address
   Wire.write(val);        // send value to write
   Wire.endTransmission(); //end transmission
}


//reads num bytes starting from address register on ACC in to buff array
void readFrom(int DEVICE, byte address, int num, byte buff[]) {
  Wire.beginTransmission(DEVICE); //start transmission to ACC 
  Wire.write(address);        //sends address to read from
  Wire.endTransmission(); //end transmission
  
  Wire.beginTransmission(DEVICE); //start transmission to ACC
  Wire.requestFrom(DEVICE, num);    // request 6 bytes from ACC
  
  int i = 0;
  while(Wire.available())    //ACC may send less than requested (abnormal)
  { 
    buff[i] = Wire.read(); // receive a byte
    i++;
  }
  Wire.endTransmission(); //end transmission
}

 

Scroll to Top