examples/eg_plds_ctrl.cpp #
Functions #
Name | |
---|---|
auto | main() |
Function Details #
main #
auto main()
Going to simulate a switching disturbance (m) acting on system
Source code #
//===-- eg_plds_ctrl.cpp - Example PLDS Control ---------------------===//
//
// Copyright 2021 Michael Bolus
// Copyright 2021 Georgia Institute of Technology
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//
//===----------------------------------------------------------------------===//
//===----------------------------------------------------------------------===//
#include <ldsCtrlEst>
using lds::Matrix;
using lds::Vector;
using lds::data_t;
using std::cout;
auto main() -> int {
cout << " ********** Example Poisson LDS Control ********** \n\n";
// Make SISO system sampled at 1kHz
data_t dt = 1e-3;
size_t n_u = 1;
size_t n_x = 1;
size_t n_y = 1;
// no time steps for simulation.
auto n_t = static_cast<size_t>(10.0 / dt);
// Control variables: _reference/target output, controller gains
// n.b., Can either use Vector (arma::Col) or std::vector
Vector y_ref0 = Vector(n_y, arma::fill::ones) * 30.0 * dt;
Matrix k_x =
Matrix(n_u, n_x, arma::fill::zeros) + 1; // gains on state error
Matrix k_inty = Matrix(n_u, n_y, arma::fill::zeros) +
10; // gains on integrated output err
// Set control type bit mask, so controller knows what to do
size_t control_type = lds::kControlTypeIntY; // integral action
// Ground-truth parameters for the controlled system
// (stand-in for physical system to be controlled)
Matrix a_true(n_x, n_x, arma::fill::eye);
a_true[0] = 0.986;
Matrix b_true(n_x, n_u, arma::fill::zeros);
b_true[0] = 0.054;
Vector x0_true = Vector(n_x, arma::fill::ones) * log(1 * dt);
size_t which_m = 0;
data_t m_low = log(1 * dt) * (1 - a_true[0]);
data_t pr_lo2hi = 1e-3;
data_t m_high = log(20 * dt) * (1 - a_true[0]);
data_t pr_hi2lo = pr_lo2hi;
Vector m0_true = Vector(n_x, arma::fill::ones) * m_low;
// construct ground truth system to be controlled...
lds::poisson::System controlled_system(n_u, n_x, n_y, dt);
// Assign params.
controlled_system.set_A(a_true);
controlled_system.set_B(b_true);
controlled_system.set_m(m0_true);
controlled_system.set_x0(x0_true);
// reset to initial conditions
controlled_system.Reset();
cout << ".....................................\n";
cout << "controlled_system:\n";
cout << ".....................................\n";
controlled_system.Print();
cout << ".....................................\n";
// Create the controller
lds::poisson::Controller controller;
{
// Create model used for control.
lds::poisson::System controller_system(controlled_system);
// for this example, assume model correct, except disturbance
Vector m0_controller = Vector(n_x, arma::fill::ones) * m_low;
Vector x0_controller = arma::log(y_ref0);
controller_system.set_m(m0_controller);
controller_system.set_x0(x0_controller);
controller_system.Reset(); //reset to new init condition
// adaptively re-estimate process disturbance (m)
controller_system.do_adapt_m = true;
// set adaptation rate by changing covariance of assumed process noise
// acting on random-walk evolution of m
Matrix q_m = Matrix(n_x, n_x, arma::fill::eye) * 1e-5;
controller_system.set_Q_m(q_m);
data_t u_lb = 0.0;
data_t u_ub = 5.0;
controller = std::move(
lds::poisson::Controller(std::move(controller_system), u_lb, u_ub));
}
// set controller type
controller.set_control_type(control_type);
// set controller gains
controller.set_Kc(k_x);
controller.set_Kc_inty(k_inty);
// to protect against integral windup when output is consistently above
// target:
data_t tau_awu(0.1);
controller.set_tau_awu(tau_awu);
cout << ".....................................\n";
cout << "controller:\n";
cout << ".....................................\n";
controller.Print();
cout << ".....................................\n";
// create Matrix to save outputs in...
Matrix y_ref = Matrix(n_y, n_t, arma::fill::zeros);
y_ref.each_col() += y_ref0;
// Simulated measurements
Matrix z(n_y, n_t, arma::fill::zeros);
// simulated control signal ([=] V)
Matrix u(n_u, n_t, arma::fill::zeros);
// outputs, states and gain/disturbance params
// *_hat indicates online estimates
Matrix y_hat(n_y, n_t, arma::fill::zeros);
Matrix x_hat(n_x, n_t, arma::fill::zeros);
Matrix m_hat(n_y, n_t, arma::fill::zeros);
// *_true indicates ground truth (system being controlled)
Matrix y_true(n_y, n_t, arma::fill::zeros);
Matrix x_true(n_x, n_t, arma::fill::zeros);
Matrix m_true(n_y, n_t, arma::fill::zeros);
// set initial val
y_hat.col(0) = controller.sys().y();
y_true.col(0) = controlled_system.y();
x_hat.col(0) = controller.sys().x();
x_true.col(0) = controlled_system.x();
m_hat.col(0) = controller.sys().m();
m_true.col(0) = controlled_system.m();
cout << "Starting " << n_t * dt << " sec simulation ... \n";
auto start = std::chrono::high_resolution_clock::now();
for (size_t t = 1; t < n_t; t++) {
// simulate a stochastically switched disturbance
Vector chance = arma::randu<Vector>(1);
if (which_m == 0) // low disturbance
{
if (chance[0] < pr_lo2hi) { // switches low -> high disturbance
m0_true = std::vector<data_t>(n_x, m_high);
which_m = 1;
}
} else { // high disturbance
if (chance[0] < pr_hi2lo) { // swithces high -> low disturbance
m0_true = std::vector<data_t>(n_x, m_low);
which_m = 0;
}
}
controlled_system.set_m(m0_true);
// e.g., use sinusoidal reference
data_t f = 0.5; // freq [=] Hz
Vector t_vec = Vector(n_y, arma::fill::ones) * t;
y_ref.col(t) +=
y_ref0 % arma::sin(f * 2 * lds::kPi * dt * t_vec - lds::kPi / 4);
// Simulate the true system.
z.col(t)=controlled_system.Simulate(u.col(t-1));
// This method uses a steady-state solution to control problem to calculate
// x_ref, u_ref from reference output y_ref. Notably, it does this in the
// log-linear space (i.e., log(y)).
//
// Therefore, it is only applicable to regulation problems or cases where
// reference trajectory changes slowly compared to system dynamics.
controller.set_y_ref(y_ref.col(t));
u.col(t)=controller.ControlOutputReference(z.col(t));
y_true.col(t) = controlled_system.y();
x_true.col(t) = controlled_system.x();
m_true.col(t) = controlled_system.m();
y_hat.col(t) = controller.sys().y();
x_hat.col(t) = controller.sys().x();
m_hat.col(t) = controller.sys().m();
}
auto finish = std::chrono::high_resolution_clock::now();
std::chrono::duration<data_t, std::milli> sim_time_ms = finish - start;
cout << "Finished simulation in " << sim_time_ms.count() << " ms.\n";
cout << "(app. " << (sim_time_ms.count() / n_t) * 1e3 << " us/time-step)\n";
// saved variables: dt, y_hat, x_hat, m_hat, z, u, y_ref, y_true,
// x_true, m_true saving with hdf5 via armadillo
arma::hdf5_opts::opts replace = arma::hdf5_opts::replace;
auto dt_vec = Vector(1).fill(dt);
dt_vec.save(arma::hdf5_name("eg_plds_ctrl.h5", "dt"));
y_ref.save(arma::hdf5_name("eg_plds_ctrl.h5", "y_ref", replace));
u.save(arma::hdf5_name("eg_plds_ctrl.h5", "u", replace));
z.save(arma::hdf5_name("eg_plds_ctrl.h5", "z", replace));
x_true.save(arma::hdf5_name("eg_plds_ctrl.h5", "x_true", replace));
m_true.save(arma::hdf5_name("eg_plds_ctrl.h5", "m_true", replace));
y_true.save(arma::hdf5_name("eg_plds_ctrl.h5", "y_true", replace));
x_hat.save(arma::hdf5_name("eg_plds_ctrl.h5", "x_hat", replace));
m_hat.save(arma::hdf5_name("eg_plds_ctrl.h5", "m_hat", replace));
y_hat.save(arma::hdf5_name("eg_plds_ctrl.h5", "y_hat", replace));
return 0;
}
Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time