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finite_horizon_linear_quadratic_regulator.cc
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finite_horizon_linear_quadratic_regulator.cc
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#include "drake/systems/controllers/finite_horizon_linear_quadratic_regulator.h"
#include <algorithm>
#include <limits>
#include <memory>
#include <utility>
#include <vector>
#include "drake/common/drake_assert.h"
#include "drake/common/is_approx_equal_abstol.h"
#include "drake/math/autodiff.h"
#include "drake/math/autodiff_gradient.h"
#include "drake/math/matrix_util.h"
#include "drake/systems/analysis/simulator.h"
#include "drake/systems/framework/leaf_system.h"
#include "drake/systems/framework/scalar_conversion_traits.h"
namespace drake {
namespace systems {
namespace controllers {
using trajectories::PiecewisePolynomial;
using trajectories::Trajectory;
namespace {
// Implements the *time-reversed* Riccati differential equation. When this
// system evaluates the contained system/cost at time t, it will always replace
// t=-t.
class RiccatiSystem : public LeafSystem<double> {
public:
DRAKE_NO_COPY_NO_MOVE_NO_ASSIGN(RiccatiSystem);
RiccatiSystem(const System<double>& system, const Context<double>& context,
const Eigen::Ref<const Eigen::MatrixXd>& Q,
const Eigen::Ref<const Eigen::MatrixXd>& R,
const Trajectory<double>& x0, const Trajectory<double>& u0,
const FiniteHorizonLinearQuadraticRegulatorOptions& options)
: system_(System<double>::ToAutoDiffXd(system)),
input_port_(
system_->get_input_port_selection(options.input_port_index)),
context_(system_->CreateDefaultContext()),
num_states_(context_->num_total_states()),
num_inputs_(input_port_->size()),
Q_(Q),
R_(R),
Rinv_(R.inverse()),
N_(options.N ? *options.N
: Eigen::MatrixXd::Zero(num_states_, num_inputs_)),
x0_(x0),
u0_(u0),
options_(options) {
if (input_port_->get_data_type() == PortDataType::kAbstractValued) {
throw std::logic_error(
"The specified input port is abstract-valued, but "
"FiniteHorizonLinearQuadraticRegulator only supports vector-valued "
"input ports. Did you perhaps forget to pass a non-default "
"`options.input_port_index`?");
}
DRAKE_DEMAND(input_port_->get_data_type() == PortDataType::kVectorValued);
DRAKE_DEMAND(context_->has_only_continuous_state());
DRAKE_DEMAND(num_states_ > 0);
DRAKE_DEMAND(num_inputs_ > 0);
const double kSymmetryTolerance = 1e-8;
DRAKE_DEMAND(Q.rows() == num_states_ && Q.cols() == num_states_);
DRAKE_DEMAND(math::IsPositiveDefinite(Q, 0.0, kSymmetryTolerance));
DRAKE_DEMAND(R.rows() == num_inputs_ && R.cols() == num_inputs_);
DRAKE_DEMAND(math::IsPositiveDefinite(
R, std::numeric_limits<double>::epsilon(), kSymmetryTolerance));
DRAKE_DEMAND(x0_.rows() == num_states_ && x0_.cols() == 1);
DRAKE_DEMAND(u0_.rows() == num_inputs_ && u0_.cols() == 1);
// If use_square_root_method = true, then the state is P (vectorized), then
// sx, then s0, where Sxx = PP'. Otherwise, the state is Sxx (vectorized),
// followed by sx, then s0.
this->DeclareContinuousState(num_states_ * num_states_ + num_states_ + 1);
// Initialize autodiff.
context_->SetTimeStateAndParametersFrom(context);
system_->FixInputPortsFrom(system, context, context_.get());
}
// Implement the (time-reversed) Riccati equation, as described in
// http://underactuated.mit.edu/lqr.html#finite_horizon_derivation .
void DoCalcTimeDerivatives(
const Context<double>& context,
ContinuousState<double>* derivatives) const override {
// Unpack the state.
const Eigen::VectorXd S_vectorized =
context.get_continuous_state_vector().CopyToVector();
const Eigen::VectorBlock<const Eigen::VectorXd> sx =
S_vectorized.segment(num_states_ * num_states_, num_states_);
// Allocate a vectorized version of the derivatives, and map into it.
Eigen::VectorXd minus_Sdot_vectorized = S_vectorized;
Eigen::VectorBlock<Eigen::VectorXd> minus_sxdot =
minus_Sdot_vectorized.segment(num_states_ * num_states_, num_states_);
double& minus_s0dot = minus_Sdot_vectorized[S_vectorized.size() - 1];
// Get (time-varying) linearization of the plant.
const double system_time = -context.get_time();
context_->SetTime(system_time);
auto autodiff_args = math::InitializeAutoDiffTuple(x0_.value(system_time),
u0_.value(system_time));
context_->SetContinuousState(std::get<0>(autodiff_args));
input_port_->FixValue(context_.get(), std::get<1>(autodiff_args));
const VectorX<AutoDiffXd> autodiff_xdot0 =
system_->EvalTimeDerivatives(*context_).CopyToVector();
const Eigen::MatrixXd AB = math::ExtractGradient(autodiff_xdot0);
const Eigen::Ref<const Eigen::MatrixXd>& A = AB.leftCols(num_states_);
const Eigen::Ref<const Eigen::MatrixXd>& B = AB.rightCols(num_inputs_);
const Eigen::VectorXd c =
math::ExtractValue(autodiff_xdot0) - x0_.EvalDerivative(system_time, 1);
// Desired trajectories relative to the nominal.
const Eigen::VectorXd xd0 =
options_.xd
? (options_.xd->value(system_time) - x0_.value(system_time)).eval()
: Eigen::VectorXd::Zero(num_states_);
const Eigen::VectorXd ud0 =
options_.ud
? (options_.ud->value(system_time) - u0_.value(system_time)).eval()
: Eigen::VectorXd::Zero(num_inputs_);
// Compute the Riccati dynamics.
Eigen::MatrixXd Sxx;
if (options_.use_square_root_method) {
const Eigen::Map<const Eigen::MatrixXd> P(S_vectorized.data(),
num_states_, num_states_);
Sxx = P * P.transpose();
const Eigen::MatrixXd PinvT = P.inverse().transpose();
Eigen::Map<Eigen::MatrixXd> minus_Pdot(minus_Sdot_vectorized.data(),
num_states_, num_states_);
minus_Pdot = A.transpose() * P -
0.5 * (N_ + Sxx * B) * Rinv_ *
(B.transpose() * P + N_.transpose() * PinvT) +
0.5 * Q_ * PinvT;
} else {
Sxx = Eigen::Map<const Eigen::MatrixXd>(S_vectorized.data(), num_states_,
num_states_);
Eigen::Map<Eigen::MatrixXd> minus_Sxxdot(
minus_Sdot_vectorized.data(), num_states_, num_states_);
minus_Sxxdot = Sxx * A + A.transpose() * Sxx -
(N_ + Sxx * B) * Rinv_ * (N_ + Sxx * B).transpose() + Q_;
}
const Eigen::VectorXd qx = -Q_ * xd0 - N_ * ud0;
const Eigen::VectorXd ru_plus_BTsx =
-R_ * ud0 - N_.transpose() * xd0 + B.transpose() * sx;
minus_sxdot = qx - (N_ + Sxx * B) * Rinv_ * ru_plus_BTsx +
A.transpose() * sx + Sxx * c;
minus_s0dot = xd0.dot(Q_ * xd0) + ud0.dot(R_ * ud0) +
2 * xd0.dot(N_ * ud0) -
ru_plus_BTsx.dot(Rinv_ * ru_plus_BTsx) + 2.0 * sx.dot(c);
derivatives->SetFromVector(minus_Sdot_vectorized);
}
// TODO(russt): It would be more elegant to think of S and K as an output of
// the RiccatiSystem, but we don't (yet) have a way to get the dense
// integration results of an output port.
// Takes a time-reversed dense solution for this Riccati system, applies the
// time-reversal, and unwraps the vectorized solution into its matrix
// components.
void MakeSTrajectories(
const PiecewisePolynomial<double>& riccati_solution,
FiniteHorizonLinearQuadraticRegulatorResult* result) const {
auto Sxx = std::make_unique<PiecewisePolynomial<double>>(
riccati_solution.Block(0, 0, num_states_ * num_states_, 1));
Sxx->Reshape(num_states_, num_states_);
if (options_.use_square_root_method) {
result->S = std::make_unique<PiecewisePolynomial<double>>(
*Sxx * (Sxx->Transpose()));
} else {
result->S = std::move(Sxx);
}
result->sx = std::make_unique<PiecewisePolynomial<double>>(
riccati_solution.Block(num_states_ * num_states_, 0, num_states_, 1));
result->s0 =
std::make_unique<PiecewisePolynomial<double>>(riccati_solution.Block(
num_states_ * num_states_ + num_states_, 0, 1, 1));
}
// Given a result structure already populated with the S and sx trajectories
// (already time-reversed and reshaped), constructs the K and k0 trajectories.
// Since derivatives are not easily available for B(t), we form a piecewise
// cubic polynomial approximation of the elements of Kx and k0 using Lagrange
// interpolating polynomials. Cubic seems natural, as the default "dense
// output" from the integrators is a cubic hermite spline; if we had
// considered K as an output from a simulation it seems natural to interpolate
// K with the same order approximation.
void MakeKTrajectories(
FiniteHorizonLinearQuadraticRegulatorResult* result) const {
const std::vector<double>& breaks =
dynamic_cast<PiecewisePolynomial<double>&>(*result->S)
.get_segment_times();
auto Kx = std::make_unique<PiecewisePolynomial<double>>();
auto k0 = std::make_unique<PiecewisePolynomial<double>>();
// TODO(russt): I believe there is a sampling theorem that should give me
// the optimal sampling times for the fixed-degree polynomial over the
// finite time interval. Find it and use it here.
const std::vector<double> scaled_sample_times = {0.0, 1. / 3., 2. / 3., 1.};
const int num_samples = scaled_sample_times.size();
std::vector<double> times(num_samples);
std::vector<Eigen::MatrixXd> Kx_samples(num_samples);
std::vector<Eigen::MatrixXd> k0_samples(num_samples);
for (size_t i = 0; i < breaks.size() - 1; ++i) {
int j_start = 0;
if (i > 0) {
// Then my first sample is the last sample from the previous segment.
times[0] = times[num_samples - 1];
Kx_samples[0] = Kx_samples[num_samples - 1];
k0_samples[0] = k0_samples[num_samples - 1];
j_start = 1;
}
for (int j = j_start; j < num_samples; ++j) {
const double time = scaled_sample_times[j] * breaks[i + 1] +
(1.0 - scaled_sample_times[j]) * breaks[i];
times[j] = time;
// Compute B.
context_->SetTime(time);
context_->SetContinuousState(x0_.value(time).cast<AutoDiffXd>().eval());
input_port_->FixValue(context_.get(),
math::InitializeAutoDiff(u0_.value(time)));
const VectorX<AutoDiffXd> autodiff_xdot0 =
system_->EvalTimeDerivatives(*context_).CopyToVector();
const Eigen::MatrixXd B = math::ExtractGradient(autodiff_xdot0);
// Desired trajectories relative to the nominal.
const Eigen::VectorXd xd0 =
options_.xd ? (options_.xd->value(time) - x0_.value(time)).eval()
: Eigen::VectorXd::Zero(num_states_);
const Eigen::VectorXd ud0 =
options_.ud ? (options_.ud->value(time) - u0_.value(time)).eval()
: Eigen::VectorXd::Zero(num_inputs_);
Kx_samples[j] = Rinv_ * (N_ + result->S->value(time) * B).transpose();
k0_samples[j] =
-ud0 + Rinv_ * (-N_.transpose() * xd0 +
B.transpose() * result->sx->value(time));
}
Kx->ConcatenateInTime(
PiecewisePolynomial<double>::LagrangeInterpolatingPolynomial(
times, Kx_samples));
k0->ConcatenateInTime(
PiecewisePolynomial<double>::LagrangeInterpolatingPolynomial(
times, k0_samples));
}
result->K = std::move(Kx);
result->k0 = std::move(k0);
}
private:
const std::unique_ptr<const System<AutoDiffXd>> system_;
const InputPort<AutoDiffXd>* const input_port_;
// Note: Use a mutable context here to avoid reallocating on every call to
// e.g. CalcTimeDerivatives. WARNING: this means that evaluation of the
// RiccatiSystem is not thread safe, but since the implementation is internal
// to this file, there are no entry points that would allow multi-threaded
// execution with the same RiccatiSystem instance.
// Note: This context_ is for system_, not `this`.
const std::unique_ptr<Context<AutoDiffXd>> context_;
const int num_states_;
const int num_inputs_;
const Eigen::Ref<const Eigen::MatrixXd>& Q_;
const Eigen::Ref<const Eigen::MatrixXd>& R_;
const Eigen::MatrixXd Rinv_;
const Eigen::MatrixXd N_;
const Trajectory<double>& x0_;
const Trajectory<double>& u0_;
const FiniteHorizonLinearQuadraticRegulatorOptions& options_;
};
} // namespace
FiniteHorizonLinearQuadraticRegulatorResult
FiniteHorizonLinearQuadraticRegulator(
const System<double>& system, const Context<double>& context, double t0,
double tf, const Eigen::Ref<const Eigen::MatrixXd>& Q,
const Eigen::Ref<const Eigen::MatrixXd>& R,
const FiniteHorizonLinearQuadraticRegulatorOptions& options) {
// Most argument consistency checks are handled by RiccatiSystem, but that
// System doesn't need to understand the time range, so we perform (only)
// those checks here.
system.ValidateContext(context);
DRAKE_DEMAND(system.num_input_ports() > 0);
DRAKE_DEMAND(tf > t0);
const int num_states = context.num_total_states();
std::unique_ptr<PiecewisePolynomial<double>> x0;
if (options.x0) {
DRAKE_DEMAND(options.x0->start_time() <= t0);
DRAKE_DEMAND(options.x0->end_time() >= tf);
DRAKE_DEMAND(options.x0->has_derivative());
} else {
// Make a constant trajectory with the state from context.
x0 = std::make_unique<PiecewisePolynomial<double>>(
context.get_continuous_state_vector().CopyToVector());
}
if (options.use_square_root_method && !options.Qf) {
throw std::logic_error(
"options.Qf is required when options.use_square_root_method is set to "
"'true'.");
}
std::unique_ptr<PiecewisePolynomial<double>> u0;
if (options.u0) {
DRAKE_DEMAND(options.u0->start_time() <= t0);
DRAKE_DEMAND(options.u0->end_time() >= tf);
} else {
// Make a constant trajectory with the input from context.
u0 = std::make_unique<PiecewisePolynomial<double>>(
system.get_input_port_selection(options.input_port_index)
->Eval(context));
}
RiccatiSystem riccati(system, context, Q, R, options.x0 ? *options.x0 : *x0,
options.u0 ? *options.u0 : *u0, options);
// Simulator doesn't support integrating backwards in time, so simulate the
// time-reversed Riccati equation from -tf to -t0, and reverse it after the
// fact.
Simulator<double> simulator(riccati);
// Set the initial conditions (the Riccati solution at tf).
if (options.Qf) {
const double kSymmetryTolerance = 1e-8;
DRAKE_DEMAND(options.Qf->rows() == num_states &&
options.Qf->cols() == num_states);
Eigen::VectorXd S_vectorized =
Eigen::VectorXd::Zero(num_states * num_states + num_states + 1);
if (options.use_square_root_method) {
// We require Qf = Qfᵀ ≻ 0.
DRAKE_DEMAND(math::IsSymmetric(*options.Qf, kSymmetryTolerance));
const double kEigenvalueTolerance = 1e-8;
// We use the SelfAdjointEigenSolver both to check PSD and to get the
// sqrt. The eigenvalue check is adopted from math::IsPositiveDefinite.
Eigen::SelfAdjointEigenSolver<Eigen::MatrixXd> eigensolver(*options.Qf);
DRAKE_THROW_UNLESS(eigensolver.info() == Eigen::Success);
const double max_abs_eigenvalue =
eigensolver.eigenvalues().cwiseAbs().maxCoeff();
if (eigensolver.eigenvalues().minCoeff() <
kEigenvalueTolerance * std::max(1., max_abs_eigenvalue)) {
throw std::logic_error(
"The SQRT method solution to the Riccati equation requires that Qf "
"is strictly positive definite. To use a positive semi-definite "
"Qf, set `options.use_square_root_method` to `false`.");
}
Eigen::Map<Eigen::MatrixXd> P(S_vectorized.data(), num_states,
num_states);
P = eigensolver.operatorSqrt();
} else {
// Qf = Qfᵀ ≽ 0 is sufficient.
DRAKE_DEMAND(
math::IsPositiveDefinite(*options.Qf, 0.0, kSymmetryTolerance));
Eigen::Map<Eigen::MatrixXd> Sxx(S_vectorized.data(), num_states,
num_states);
Sxx = *options.Qf;
}
if (options.xd) {
Eigen::VectorBlock<Eigen::VectorXd> sx =
S_vectorized.segment(num_states * num_states, num_states);
double& s0 = S_vectorized[S_vectorized.size() - 1];
const Eigen::VectorXd xd0 = options.xd->value(tf) - x0->value(tf);
sx = -(*options.Qf) * xd0;
s0 = xd0.dot((*options.Qf) * xd0);
}
simulator.get_mutable_context().SetContinuousState(S_vectorized);
}
simulator.get_mutable_context().SetTime(-tf);
IntegratorBase<double>& integrator = simulator.get_mutable_integrator();
integrator.StartDenseIntegration();
simulator.AdvanceTo(-t0);
FiniteHorizonLinearQuadraticRegulatorResult result;
result.x0 = options.x0 ? options.x0->Clone() : std::move(x0);
result.u0 = options.u0 ? options.u0->Clone() : std::move(u0);
std::unique_ptr<PiecewisePolynomial<double>> riccati_solution =
integrator.StopDenseIntegration();
riccati_solution->ReverseTime();
riccati.MakeSTrajectories(*riccati_solution, &result);
riccati.MakeKTrajectories(&result);
return result;
}
namespace {
// Implements the (time-varying) linear controller described by a
// FiniteHorizonLinearQuadraticRegulatorResult.
// TODO(russt): Consider removing this class entirely and using
// TrajectoryAffineSystem instead, once we support enough Trajectory algebra
// (including adding and multiplying PiecewisePolynomial trajectories with
// different segment times).
template <typename T>
class Controller final : public LeafSystem<T> {
public:
DRAKE_NO_COPY_NO_MOVE_NO_ASSIGN(Controller)
// Constructs the controller, and assumes ownership of the required components
// of the FiniteHorizonLinearQuadraticRegulatorResult. This is appropriate,
// since the class is internal to this .cc file and is only ever constructed
// via a Make*Regulator() workflow which will discard the result structure.
Controller(std::unique_ptr<trajectories::Trajectory<double>> x0,
std::unique_ptr<trajectories::Trajectory<double>> u0,
std::unique_ptr<trajectories::Trajectory<double>> K,
std::unique_ptr<trajectories::Trajectory<double>> k0)
: LeafSystem<T>(SystemTypeTag<Controller>()),
x0_(std::move(x0)),
u0_(std::move(u0)),
K_(std::move(K)),
k0_(std::move(k0)) {
this->DeclareVectorInputPort("plant_state", K_->cols());
this->DeclareVectorOutputPort(
"command", K_->rows(), &Controller::CalcOutput,
{this->time_ticket(), this->input_port_ticket(InputPortIndex(0))});
}
// Scalar-type converting copy constructor. See @ref system_scalar_conversion.
template <typename U>
explicit Controller(const Controller<U>& other)
: Controller(other.x0_->Clone(), other.u0_->Clone(), other.K_->Clone(),
other.k0_->Clone()) {}
private:
template <typename U>
friend class Controller;
// Calculate the (time-varying) output.
void CalcOutput(const Context<T>& context, BasicVector<T>* output) const {
// Note: The stored trajectories are always double, even when T != double.
const double t = ExtractDoubleOrThrow(context.get_time());
const auto& x = this->get_input_port(0).Eval(context);
output->get_mutable_value() =
u0_->value(t) - K_->value(t) * (x - x0_->value(t)) - k0_->value(t);
}
std::unique_ptr<trajectories::Trajectory<double>> x0_;
std::unique_ptr<trajectories::Trajectory<double>> u0_;
std::unique_ptr<trajectories::Trajectory<double>> K_;
std::unique_ptr<trajectories::Trajectory<double>> k0_;
};
} // namespace
std::unique_ptr<System<double>> MakeFiniteHorizonLinearQuadraticRegulator(
const System<double>& system, const Context<double>& context, double t0,
double tf, const Eigen::Ref<const Eigen::MatrixXd>& Q,
const Eigen::Ref<const Eigen::MatrixXd>& R,
const FiniteHorizonLinearQuadraticRegulatorOptions& options) {
FiniteHorizonLinearQuadraticRegulatorResult result =
FiniteHorizonLinearQuadraticRegulator(system, context, t0, tf, Q, R,
options);
return std::make_unique<Controller<double>>(
std::move(result.x0), std::move(result.u0), std::move(result.K),
std::move(result.k0));
}
} // namespace controllers
} // namespace systems
} // namespace drake