#include "acado_common.h" #include "acado_auxiliary_functions.h" #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, x_l, v_l, a_l; } state_t; typedef struct { double x_ego[N]; double v_ego[N]; double a_ego[N]; double j_ego[N]; double x_l[N]; double v_l[N]; double a_l[N]; } log_t; void init(double ttcCost, double distanceCost, double accelerationCost, 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; } acadoVariables.W[16 * i + 0] = ttcCost * f; // exponential cost for time-to-collision (ttc) acadoVariables.W[16 * i + 5] = distanceCost * f; // desired distance acadoVariables.W[16 * i + 10] = accelerationCost * f; // acceleration acadoVariables.W[16 * i + 15] = jerkCost * f; // jerk } acadoVariables.WN[0] = ttcCost * STEP_MULTIPLIER; // exponential cost for danger zone acadoVariables.WN[4] = distanceCost * STEP_MULTIPLIER; // desired distance acadoVariables.WN[8] = accelerationCost * STEP_MULTIPLIER; // acceleration } void init_with_simulation(double v_ego, double x_l, double v_l, double a_l, double l){ int i; double x_ego = 0.0; double a_ego = 0.0; if (v_ego > v_l){ a_ego = -(v_ego - v_l) * (v_ego - v_l) / (2.0 * x_l + 0.01) + a_l; } double dt = 0.2; 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] = a_ego; acadoVariables.x[i*NX+3] = x_l; acadoVariables.x[i*NX+4] = v_l; acadoVariables.x[i*NX+5] = a_l; x_ego += v_ego * dt; v_ego += a_ego * dt; x_l += v_l * dt; v_l += a_l * dt; a_l += -l * a_l * dt; if (v_ego <= 0.0) { v_ego = 0.0; a_ego = 0.0; } } 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 l){ int i; for (i = 0; i <= NOD * N; i+= NOD){ acadoVariables.od[i] = l; } 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] = x0->x_l; acadoVariables.x[4] = acadoVariables.x0[4] = x0->v_l; acadoVariables.x[5] = acadoVariables.x0[5] = x0->a_l; 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->x_l[i] = acadoVariables.x[i*NX+3]; solution->v_l[i] = acadoVariables.x[i*NX+4]; solution->a_l[i] = acadoVariables.x[i*NX+5]; solution->j_ego[i] = acadoVariables.u[i]; } // Dont shift states here. Current solution is closer to next timestep than if // we shift by 0.2 seconds. return acado_getNWSR(); }