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108 lines
3.3 KiB
108 lines
3.3 KiB
#include "acado_common.h"
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#include "acado_auxiliary_functions.h"
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#include "common/modeldata.h"
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#include <stdio.h>
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#include <math.h>
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#define NX ACADO_NX /* Number of differential state variables. */
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#define NXA ACADO_NXA /* Number of algebraic variables. */
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#define NU ACADO_NU /* Number of control inputs. */
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#define NOD ACADO_NOD /* Number of online data values. */
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#define NY ACADO_NY /* Number of measurements/references on nodes 0..N - 1. */
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#define NYN ACADO_NYN /* Number of measurements/references on node N. */
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#define N ACADO_N /* Number of intervals in the horizon. */
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ACADOvariables acadoVariables;
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ACADOworkspace acadoWorkspace;
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typedef struct {
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double x_ego, v_ego, a_ego;
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} state_t;
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typedef struct {
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double x_ego[N+1];
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double v_ego[N+1];
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double a_ego[N+1];
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double t[N+1];
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double j_ego[N];
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double cost;
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} log_t;
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void init(double xCost, double vCost, double aCost, double jerkCost, double constraintCost){
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acado_initializeSolver();
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int i;
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const int STEP_MULTIPLIER = 3;
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/* Initialize the states and controls. */
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for (i = 0; i < NX * (N + 1); ++i) acadoVariables.x[ i ] = 0.0;
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for (i = 0; i < NU * N; ++i) acadoVariables.u[ i ] = 0.0;
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/* Initialize the measurements/reference. */
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for (i = 0; i < NY * N; ++i) acadoVariables.y[ i ] = 0.0;
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for (i = 0; i < NYN; ++i) acadoVariables.yN[ i ] = 0.0;
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/* MPC: initialize the current state feedback. */
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for (i = 0; i < NX; ++i) acadoVariables.x0[ i ] = 0.0;
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// Set weights
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for (i = 0; i < N; i++) {
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double f = 20 * (T_IDXS[i+1] - T_IDXS[i]);
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// Setup diagonal entries
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acadoVariables.W[NY*NY*i + (NY+1)*0] = xCost * f;
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acadoVariables.W[NY*NY*i + (NY+1)*1] = vCost * f;
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acadoVariables.W[NY*NY*i + (NY+1)*2] = aCost * f;
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acadoVariables.W[NY*NY*i + (NY+1)*3] = jerkCost * f;
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acadoVariables.W[NY*NY*i + (NY+1)*4] = constraintCost * f;
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}
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acadoVariables.WN[(NYN+1)*0] = xCost * STEP_MULTIPLIER;
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acadoVariables.WN[(NYN+1)*1] = vCost * STEP_MULTIPLIER;
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acadoVariables.WN[(NYN+1)*2] = aCost * STEP_MULTIPLIER;
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}
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int run_mpc(state_t * x0, log_t * solution,
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double target_x[N+1], double target_v[N+1], double target_a[N+1],
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double min_a, double max_a){
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int i;
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for (i = 0; i < N + 1; ++i){
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acadoVariables.od[i*NOD] = min_a;
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acadoVariables.od[i*NOD+1] = max_a;
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}
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for (i = 0; i < N; i+= 1){
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acadoVariables.y[NY*i + 0] = target_x[i];
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acadoVariables.y[NY*i + 1] = target_v[i];
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acadoVariables.y[NY*i + 2] = target_a[i];
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acadoVariables.y[NY*i + 3] = 0.0;
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acadoVariables.y[NY*i + 4] = 0.0;
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}
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acadoVariables.yN[0] = target_x[N];
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acadoVariables.yN[1] = target_v[N];
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acadoVariables.yN[2] = target_a[N];
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acadoVariables.x0[0] = x0->x_ego;
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acadoVariables.x0[1] = x0->v_ego;
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acadoVariables.x0[2] = x0->a_ego;
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acado_preparationStep();
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acado_feedbackStep();
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for (i = 0; i <= N; i++) {
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solution->x_ego[i] = acadoVariables.x[i*NX];
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solution->v_ego[i] = acadoVariables.x[i*NX+1];
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solution->a_ego[i] = acadoVariables.x[i*NX+2];
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if (i < N) {
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solution->j_ego[i] = acadoVariables.u[i*NU];
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}
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}
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solution->cost = acado_getObjective();
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// Dont shift states here. Current solution is closer to next timestep than if
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// we shift by 0.1 seconds.
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return acado_getNWSR();
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}
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