def main(discretization='shooting_nodes'): # create ocp object to formulate the OCP ocp = AcadosOcp() # set model model = export_pendulum_ode_model() ocp.model = model integrator_type = 'LIFTED_IRK' # ERK, IRK, GNSF, LIFTED_IRK if integrator_type == 'GNSF': acados_dae_model_json_dump(model) # structure detection in Matlab/Octave -> produces 'pendulum_ode_gnsf_functions.json' status = os.system('octave detect_gnsf_from_json.m') # load gnsf from json with open(model.name + '_gnsf_functions.json', 'r') as f: gnsf_dict = json.load(f) ocp.gnsf_model = gnsf_dict Tf = 1.0 nx = model.x.size()[0] nu = model.u.size()[0] ny = nx + nu ny_e = nx N = 15 # discretization ocp.dims.N = N # shooting_nodes = np.linspace(0, Tf, N+1) time_steps = np.linspace(0, 1, N) time_steps = Tf * time_steps / sum(time_steps) shooting_nodes = np.zeros((N + 1, )) for i in range(len(time_steps)): shooting_nodes[i + 1] = shooting_nodes[i] + time_steps[i] # nonuniform discretizations can be defined either by shooting_nodes or time_steps: if discretization == 'shooting_nodes': ocp.solver_options.shooting_nodes = shooting_nodes elif discretization == 'time_steps': ocp.solver_options.time_steps = time_steps else: raise NotImplementedError( f"discretization type {discretization} not supported.") # set num_steps ocp.solver_options.sim_method_num_steps = 2 * np.ones((N, )) ocp.solver_options.sim_method_num_steps[0] = 3 # set num_stages ocp.solver_options.sim_method_num_stages = 2 * np.ones((N, )) ocp.solver_options.sim_method_num_stages[0] = 4 # set cost Q = 2 * np.diag([1e3, 1e3, 1e-2, 1e-2]) R = 2 * np.diag([1e-2]) ocp.cost.W_e = Q ocp.cost.W = scipy.linalg.block_diag(Q, R) ocp.cost.cost_type = 'LINEAR_LS' ocp.cost.cost_type_e = 'LINEAR_LS' ocp.cost.Vx = np.zeros((ny, nx)) ocp.cost.Vx[:nx, :nx] = np.eye(nx) Vu = np.zeros((ny, nu)) Vu[4, 0] = 1.0 ocp.cost.Vu = Vu ocp.cost.Vx_e = np.eye(nx) ocp.cost.yref = np.zeros((ny, )) ocp.cost.yref_e = np.zeros((ny_e, )) # set constraints Fmax = 80 ocp.constraints.lbu = np.array([-Fmax]) ocp.constraints.ubu = np.array([+Fmax]) x0 = np.array([0.0, np.pi, 0.0, 0.0]) ocp.constraints.x0 = x0 ocp.constraints.idxbu = np.array([0]) ocp.solver_options.qp_solver = 'PARTIAL_CONDENSING_HPIPM' # FULL_CONDENSING_QPOASES ocp.solver_options.hessian_approx = 'GAUSS_NEWTON' ocp.solver_options.integrator_type = integrator_type ocp.solver_options.print_level = 0 ocp.solver_options.nlp_solver_type = 'SQP' # SQP_RTI, SQP # set prediction horizon ocp.solver_options.tf = Tf ocp.solver_options.initialize_t_slacks = 1 # Set additional options for Simulink interface: acados_path = get_acados_path() json_path = os.path.join(acados_path, 'interfaces/acados_template/acados_template') with open(json_path + '/simulink_default_opts.json', 'r') as f: simulink_opts = json.load(f) ocp_solver = AcadosOcpSolver(ocp, json_file='acados_ocp.json', simulink_opts=simulink_opts) # ocp_solver = AcadosOcpSolver(ocp, json_file = 'acados_ocp.json') simX = np.ndarray((N + 1, nx)) simU = np.ndarray((N, nu)) # change options after creating ocp_solver ocp_solver.options_set("step_length", 0.99999) ocp_solver.options_set("globalization", "fixed_step") # fixed_step, merit_backtracking ocp_solver.options_set("tol_eq", TOL) ocp_solver.options_set("tol_stat", TOL) ocp_solver.options_set("tol_ineq", TOL) ocp_solver.options_set("tol_comp", TOL) # initialize solver for i in range(N): ocp_solver.set(i, "x", x0) status = ocp_solver.solve() if status not in [0, 2]: raise Exception('acados returned status {}. Exiting.'.format(status)) # get primal solution for i in range(N): simX[i, :] = ocp_solver.get(i, "x") simU[i, :] = ocp_solver.get(i, "u") simX[N, :] = ocp_solver.get(N, "x") print("inequality multipliers at stage 1") print(ocp_solver.get(1, "lam")) # inequality multipliers at stage 1 print("slack values at stage 1") print(ocp_solver.get(1, "t")) # slack values at stage 1 print("multipliers of dynamic conditions between stage 1 and 2") print(ocp_solver.get( 1, "pi")) # multipliers of dynamic conditions between stage 1 and 2 # initialize ineq multipliers and slacks at stage 1 ocp_solver.set(1, "lam", np.zeros(2, )) ocp_solver.set(1, "t", np.zeros(2, )) ocp_solver.print_statistics( ) # encapsulates: stat = ocp_solver.get_stats("statistics") # timings time_tot = ocp_solver.get_stats("time_tot") time_lin = ocp_solver.get_stats("time_lin") time_sim = ocp_solver.get_stats("time_sim") time_qp = ocp_solver.get_stats("time_qp") print( f"timings OCP solver: total: {1e3*time_tot}ms, lin: {1e3*time_lin}ms, sim: {1e3*time_sim}ms, qp: {1e3*time_qp}ms" ) # print("simU", simU) # print("simX", simX) iterate_filename = f'final_iterate_{discretization}.json' ocp_solver.store_iterate(filename=iterate_filename, overwrite=True) plot_pendulum(shooting_nodes, Fmax, simU, simX, latexify=False) del ocp_solver
def main(interface_type='ctypes'): # create ocp object to formulate the OCP ocp = AcadosOcp() # set model model = export_pendulum_ode_model() ocp.model = model nx = model.x.size()[0] nu = model.u.size()[0] ny = nx + nu ny_e = nx # define the different options for the use-case demonstration N0 = 20 # original number of shooting nodes N12 = 15 # change the number of shooting nodes for use-cases 1 and 2 condN12 = max(1, round(N12/1)) # change the number of cond_N for use-cases 1 and 2 (for PARTIAL_* solvers only) Tf_01 = 1.0 # original final time and for use-case 1 Tf_2 = Tf_01 * 0.7 # change final time for use-case 2 (but keep N identical) # set dimensions ocp.dims.N = N0 # set cost Q = 2 * np.diag([1e3, 1e3, 1e-2, 1e-2]) R = 2 * np.diag([1e-2]) ocp.cost.W_e = Q ocp.cost.W = scipy.linalg.block_diag(Q, R) ocp.cost.cost_type = 'LINEAR_LS' ocp.cost.cost_type_e = 'LINEAR_LS' ocp.cost.Vx = np.zeros((ny, nx)) ocp.cost.Vx[:nx, :nx] = np.eye(nx) Vu = np.zeros((ny, nu)) Vu[4, 0] = 1.0 ocp.cost.Vu = Vu ocp.cost.Vx_e = np.eye(nx) ocp.cost.yref = np.zeros((ny,)) ocp.cost.yref_e = np.zeros((ny_e,)) # set constraints Fmax = 80 ocp.constraints.lbu = np.array([-Fmax]) ocp.constraints.ubu = np.array([+Fmax]) ocp.constraints.idxbu = np.array([0]) ocp.constraints.x0 = np.array([0.0, np.pi, 0.0, 0.0]) # set options ocp.solver_options.qp_solver = 'PARTIAL_CONDENSING_HPIPM' # FULL_CONDENSING_QPOASES # PARTIAL_CONDENSING_HPIPM, FULL_CONDENSING_QPOASES, FULL_CONDENSING_HPIPM, # PARTIAL_CONDENSING_QPDUNES, PARTIAL_CONDENSING_OSQP ocp.solver_options.hessian_approx = 'GAUSS_NEWTON' ocp.solver_options.integrator_type = 'ERK' # ocp.solver_options.print_level = 1 ocp.solver_options.nlp_solver_type = 'SQP' # SQP_RTI, SQP # set prediction horizon ocp.solver_options.tf = Tf_01 print(80*'-') print('generate code and compile...') if interface_type == 'cython': AcadosOcpSolver.generate(ocp, json_file='acados_ocp.json') AcadosOcpSolver.build(ocp.code_export_directory, with_cython=True) ocp_solver = AcadosOcpSolver.create_cython_solver('acados_ocp.json') elif interface_type == 'ctypes': ocp_solver = AcadosOcpSolver(ocp, json_file='acados_ocp.json') elif interface_type == 'cython_prebuilt': from c_generated_code.acados_ocp_solver_pyx import AcadosOcpSolverCython ocp_solver = AcadosOcpSolverCython(ocp.model.name, ocp.solver_options.nlp_solver_type, ocp.dims.N) # test setting HPIPM options ocp_solver.options_set('qp_tol_ineq', 1e-8) ocp_solver.options_set('qp_tau_min', 1e-10) ocp_solver.options_set('qp_mu0', 1e0) # -------------------------------------------------------------------------------- # 0) solve the problem defined here (original from code export), analog to 'minimal_example_ocp.py' nvariant = 0 simX0 = np.ndarray((N0 + 1, nx)) simU0 = np.ndarray((N0, nu)) print(80*'-') print(f'solve original code with N = {N0} and Tf = {Tf_01} s:') status = ocp_solver.solve() if status != 0: ocp_solver.print_statistics() # encapsulates: stat = ocp_solver.get_stats("statistics") raise Exception(f'acados returned status {status}.') # get solution for i in range(N0): simX0[i, :] = ocp_solver.get(i, "x") simU0[i, :] = ocp_solver.get(i, "u") simX0[N0, :] = ocp_solver.get(N0, "x") ocp_solver.print_statistics() # encapsulates: stat = ocp_solver.get_stats("statistics") ocp_solver.store_iterate(filename=f'final_iterate_{interface_type}_variant{nvariant}.json', overwrite=True) if PLOT:# plot but don't halt plot_pendulum(np.linspace(0, Tf_01, N0 + 1), Fmax, simU0, simX0, latexify=False, plt_show=False, X_true_label=f'original: N={N0}, Tf={Tf_01}')
ocp_solver.print_statistics() if status != 0: raise Exception(f'acados returned status {status}.') # get solution for i in range(N): simX[i,:] = ocp_solver.get(i, "x") simU[i,:] = ocp_solver.get(i, "u") simX[N,:] = ocp_solver.get(N, "x") # plot results plot_pendulum(np.linspace(0, Tf, N+1), Fmax, simU, simX, latexify=False) ocp_solver.store_iterate(filename='solution.json', overwrite=True) ocp_solver.load_iterate(filename='solution.json') # timings # time_tot = 1e8 # time_lin = 1e8 # for k in range(1000): # status = ocp_solver.solve() # time_tot = min(time_tot, ocp_solver.get_stats("time_tot")[0]) # time_lin = min(time_lin, ocp_solver.get_stats("time_lin")[0]) # print("CPU time = ", time_tot * 1e3, "ms") # print("CPU time linearization = ", time_lin * 1e3, "ms")