#include "acado_common.h" #include "acado_auxiliary_functions.h" #include #include #define NX ACADO_NX /* Number of differential state variables. */ #define NXA ACADO_NXA /* Number of algebraic variables. */ #define NU ACADO_NU /* Number of control inputs. */ #define NOD ACADO_NOD /* Number of online data values. */ #define NY ACADO_NY /* Number of measurements/references on nodes 0..N - 1. */ #define NYN ACADO_NYN /* Number of measurements/references on node N. */ #define N ACADO_N /* Number of intervals in the horizon. */ ACADOvariables acadoVariables; ACADOworkspace acadoWorkspace; typedef struct { double x_ego, v_ego, a_ego; } state_t; typedef struct { double x_ego[N+1]; double v_ego[N+1]; double a_ego[N+1]; double t[N+1]; double j_ego[N]; double cost; } log_t; void init(double xCost, double vCost, double aCost, double accelCost, double jerkCost) { acado_initializeSolver(); int i; const int STEP_MULTIPLIER = 3; /* Initialize the states and controls. */ for (i = 0; i < NX * (N + 1); ++i) acadoVariables.x[ i ] = 0.0; for (i = 0; i < NU * N; ++i) acadoVariables.u[ i ] = 0.0; /* Initialize the measurements/reference. */ for (i = 0; i < NY * N; ++i) acadoVariables.y[ i ] = 0.0; for (i = 0; i < NYN; ++i) acadoVariables.yN[ i ] = 0.0; /* MPC: initialize the current state feedback. */ for (i = 0; i < NX; ++i) acadoVariables.x0[ i ] = 0.0; // Set weights for (i = 0; i < N; i++) { int f = 1; if (i > 4) { f = STEP_MULTIPLIER; } // Setup diagonal entries acadoVariables.W[NY*NY*i + (NY+1)*0] = xCost * f; acadoVariables.W[NY*NY*i + (NY+1)*1] = vCost * f; acadoVariables.W[NY*NY*i + (NY+1)*2] = aCost * f; acadoVariables.W[NY*NY*i + (NY+1)*3] = accelCost * f; acadoVariables.W[NY*NY*i + (NY+1)*4] = jerkCost * f; } acadoVariables.WN[(NYN+1)*0] = xCost * STEP_MULTIPLIER; acadoVariables.WN[(NYN+1)*1] = vCost * STEP_MULTIPLIER; acadoVariables.WN[(NYN+1)*2] = aCost * STEP_MULTIPLIER; acadoVariables.WN[(NYN+1)*3] = accelCost * STEP_MULTIPLIER; } void init_with_simulation(double v_ego) { int i; double x_ego = 0.0; double dt = 0.2; double t = 0.0; for (i = 0; i < N + 1; ++i) { if (i > 4) { dt = 0.6; } acadoVariables.x[i*NX] = x_ego; acadoVariables.x[i*NX+1] = v_ego; acadoVariables.x[i*NX+2] = 0; acadoVariables.x[i*NX+3] = t; x_ego += v_ego * dt; t += dt; } for (i = 0; i < NU * N; ++i) acadoVariables.u[ i ] = 0.0; for (i = 0; i < NY * N; ++i) acadoVariables.y[ i ] = 0.0; for (i = 0; i < NYN; ++i) acadoVariables.yN[ i ] = 0.0; } int run_mpc(state_t * x0, log_t * solution, double x_poly[4], double v_poly[4], double a_poly[4]) { int i; for (i = 0; i < N + 1; ++i) { acadoVariables.od[i*NOD+0] = x_poly[0]; acadoVariables.od[i*NOD+1] = x_poly[1]; acadoVariables.od[i*NOD+2] = x_poly[2]; acadoVariables.od[i*NOD+3] = x_poly[3]; acadoVariables.od[i*NOD+4] = v_poly[0]; acadoVariables.od[i*NOD+5] = v_poly[1]; acadoVariables.od[i*NOD+6] = v_poly[2]; acadoVariables.od[i*NOD+7] = v_poly[3]; acadoVariables.od[i*NOD+8] = a_poly[0]; acadoVariables.od[i*NOD+9] = a_poly[1]; acadoVariables.od[i*NOD+10] = a_poly[2]; acadoVariables.od[i*NOD+11] = a_poly[3]; } acadoVariables.x[0] = acadoVariables.x0[0] = x0->x_ego; acadoVariables.x[1] = acadoVariables.x0[1] = x0->v_ego; acadoVariables.x[2] = acadoVariables.x0[2] = x0->a_ego; acadoVariables.x[3] = acadoVariables.x0[3] = 0; acado_preparationStep(); acado_feedbackStep(); for (i = 0; i <= N; i++) { solution->x_ego[i] = acadoVariables.x[i*NX]; solution->v_ego[i] = acadoVariables.x[i*NX+1]; solution->a_ego[i] = acadoVariables.x[i*NX+2]; solution->t[i] = acadoVariables.x[i*NX+3]; if (i < N) { solution->j_ego[i] = acadoVariables.u[i]; } } solution->cost = acado_getObjective(); // Dont shift states here. Current solution is closer to next timestep than if // we shift by 0.1 seconds. return acado_getNWSR(); }