testPredCtrl.cpp

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#define EIGEN_DONT_PARALLELIZE 
#include "testPredCtrl.hpp"
#include "ar_controller.hpp"
 
#include <random>
#include <cmath>
 
#include <vector>
#include <algorithm>
#include <numeric>
#include <chrono>
 
double variance(double data[], int size, int num_skip=0){
    double mean = 0.0;
    for(int i=num_skip; i<size; i++){
        mean += data[i];
    }
    mean /= (size - num_skip);
    std::cout << "Mean: " << mean << std::endl;
 
    double var = 0.0;
    for(int i=num_skip; i<size; i++){
        var += (data[i] - mean) * (data[i] - mean);
    }
 
    return var / (size - num_skip);
}
 
double standard_dev(double data[], int size, int num_skip=0){
    return std::sqrt(variance(data, size, num_skip));
}
 
void write_to_file(std::string filename, double data[], int size){
    std::ofstream out(filename);
    for(int i=0; i<size; i++){
        out << data[i];
        if(i < (size - 1))
            out << ',';
    }
}
 
int main(int argc, char **argv){
    std::default_random_engine generator;
    std::normal_distribution<DDSPC::realT> distribution(0, 1.0);
 
    int num_steps = 10000;
    DDSPC::realT x[num_steps] = {0.0};
    DDSPC::realT err[num_steps] = {0.0};
    DDSPC::realT signal[num_steps] = {0.0};
 
    DDSPC::realT err_pc[num_steps] = {0.0};
    DDSPC::realT signal_pc[num_steps] = {0.0};
 
    for(int i=0; i<num_steps; i++){
        x[i] = std::sin(2 * 3.14 * 20.0 * i / 1000.0);
    }
    
    DDSPC::realT gain = 0.5;
    DDSPC::realT gamma = 1.001;
    DDSPC::realT initial_regularization = 100.0;
    int num_history = 50;
    int num_future = 10;
    int num_actuators = 1;
    
    DDSPC::Matrix measurement;
    measurement.resize(num_actuators,1);
 
    DDSPC::Matrix exploration_noise;
    exploration_noise.resize(num_actuators,1);
 
    DDSPC::PredictiveController controller = DDSPC::PredictiveController(num_actuators, num_history, num_future, gain, gamma, initial_regularization, 1.0e5);
 
    std::chrono::steady_clock::time_point begin = std::chrono::steady_clock::now();
    std::chrono::steady_clock::time_point end = std::chrono::steady_clock::now();
 
    double command_calc = 0.0;
    double update_system = 0.0;
    double update_controller = 0.0;
    for(int i=0; i < (num_steps - 1); i++){
        if(i == 250)
            controller.set_regularization(1.0);
        if(i == 500)
            controller.set_regularization(0.1);
        
        if(i < 500){
            exploration_noise(0,0) = 0.1 * distribution(generator);
        }else{
            exploration_noise(0,0) = 0.0;
        }            
 
        err[i] = x[i] + signal[i];
        signal[i+1] = signal[i] - gain * err[i] + exploration_noise(0, 0);
 
        err_pc[i] = x[i] + signal_pc[i];
        measurement(0,0) = err_pc[i];
 
        begin = std::chrono::steady_clock::now();
        DDSPC::Matrix new_command = controller.calculate_command(measurement, exploration_noise);
        end = std::chrono::steady_clock::now();
        command_calc += std::chrono::duration_cast<std::chrono::nanoseconds>(end - begin).count();
 
        signal_pc[i + 1] = signal_pc[i] + new_command(0,0);
        
        if((i+1) > (num_future + num_history)){
            begin = std::chrono::steady_clock::now();
            controller.update_system();
            end = std::chrono::steady_clock::now();
            update_system += std::chrono::duration_cast<std::chrono::nanoseconds>(end - begin).count();
 
            begin = std::chrono::steady_clock::now();
            controller.update_controller();
            end = std::chrono::steady_clock::now();
            update_controller += std::chrono::duration_cast<std::chrono::nanoseconds>(end - begin).count();
        }
    }
    
    std::cout << command_calc / num_steps / 1000.0 << "  " << update_system / num_steps / 1000.0 << "  " << update_controller / num_steps / 1000.0 << std::endl;
 
    std::cout<< standard_dev(x, num_steps, 500) << std::endl;
    std::cout<< standard_dev(err, num_steps, 500) << std::endl;
    std::cout<< standard_dev(err_pc, num_steps, 500) << std::endl;
    
    write_to_file("x.csv", x, num_steps);
    write_to_file("err.csv", err, num_steps);
    write_to_file("err_pc.csv", err_pc, num_steps);
 
    return 0;
 
}