cascade pid control

1. incremental PID control algorithm Void pid_control (void) {offside1 = standard_speed-actual_speed; // calculate the current speed deviation R1 = Kp * (offside1-offside2); r2 = Ki * offside1; pid_result = (unsigned char) (R1 + R2); // The floating point number is forcibly converted to the hexadecimal number offside2 = offside1 ;} 2. PID

=error (k); Endfigure (1);p lot (Time,rin, ' B ', time,yout, ' R '); Xlabel (' Time (s) '); Ylabel (' Rin,yout ')　　Three, Gaussian distribution of input%pid controlerclear All;close all;s=1:1:500;in = 0.1*randn (1,500) +1; % of 500 (0.95,1.05) of the random number of the distribution of the plot (S,in, ' * '); grid ONTS=0.001;SYS=TF (5.235e005,[1,87.35,1.047e004,0]);% Build transfer function dsys= C2D (sys,ts, ' z ');% converts a continuous time model

In HLA simulation, we have a keyboard input member responsible for inputting the target values that control the pressure, temperature, and speed of various objects, after the input, the attribute data is transmitted to the Controller member through the update attribute. The controller member receives the attribute value for normalization (the so-called normalization is only to process the data so that it does not contain units) then, the data is norma

); //Pulse for 10μs to trigger ultrasonic detectionDelaymicroseconds (Ten); Digitalwrite (Outputpin, low); intDistance = Pulsein (Inputpin, high);//Read receiver Pulse timeDistance= distance/ -;//Transform Pulse time to distanceSerial.print (distance);//Ourput DistanceDelay -); returndistance; }I've said it before.I'll send it again.Sensor SRF05 UltrasonicVCC------------5VGND------------GNDPIN ECHO-----D4PIN TRIG-----D5A self-brought library is used in the code Pid_v1.hMypid (input, out

called re-excitation learning. In this learning mode, the learning system is based on an evaluation, and the evaluation will convert the original enhanced signal received from the external environment into a high-quality enhancement signal that inspires enhanced signals. It is important to note that the external environment gives an evaluation (award or punishment) to the output of the learning system, rather than the correct answer, and the learning system improves its performance by reinforci

To realize the parameter self-tuning fuzzy PID algorithm in engineering, the most common method is to look up the table method. The specific implementation method is to be different E (temperature error), EC (Error rate of change) and kp, Ki, KD rules made a table stored in the microcontroller inside. Then after the temperature data obtained by each sample is blurred and the E and EC are obtained, the corresponding kp, ki and kd can be obtained by che

Introduced Developing custom controls from zero is often unnecessary because the standard control group is fairly comprehensive and can be fixed if it is not sufficient for subclasses or drawings. This is an important point that should not be overlooked. When you develop a custom control from scratch, the controls you get are often inferior to the standard (control