Drake
Drake C++ Documentation
Feedback Control Design

## Detailed Description

A collection of algorithms for synthesizing feedback control.

A number of control design algorithms can also be found in Controllers (if the design produces a systems::System).

## Functions

std::pair< std::unique_ptr< BarycentricMeshSystem< double > >, Eigen::RowVectorXd > FittedValueIteration (Simulator< double > *simulator, const std::function< double(const Context< double > &context)> &cost_function, const math::BarycentricMesh< double >::MeshGrid &state_grid, const math::BarycentricMesh< double >::MeshGrid &input_grid, double time_step, const DynamicProgrammingOptions &options=DynamicProgrammingOptions())
Implements Fitted Value Iteration on a (triangulated) Barycentric Mesh, which designs a state-feedback policy to minimize the infinite-horizon cost ∑ γⁿ g(x[n],u[n]), where γ is the discount factor in options. More...

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=FiniteHorizonLinearQuadraticRegulatorOptions())
Solves the differential Riccati equation to compute the optimal controller and optimal cost-to-go for the finite-horizon linear quadratic regulator: More...

LinearQuadraticRegulatorResult LinearQuadraticRegulator (const Eigen::Ref< const Eigen::MatrixXd > &A, const Eigen::Ref< const Eigen::MatrixXd > &B, const Eigen::Ref< const Eigen::MatrixXd > &Q, const Eigen::Ref< const Eigen::MatrixXd > &R, const Eigen::Ref< const Eigen::MatrixXd > &N=Eigen::Matrix< double, 0, 0 >::Zero(), const Eigen::Ref< const Eigen::MatrixXd > &F=Eigen::Matrix< double, 0, 0 >::Zero())
Computes the optimal feedback controller, u=-Kx, and the optimal cost-to-go J = x'Sx for the problem: More...

LinearQuadraticRegulatorResult DiscreteTimeLinearQuadraticRegulator (const Eigen::Ref< const Eigen::MatrixXd > &A, const Eigen::Ref< const Eigen::MatrixXd > &B, const Eigen::Ref< const Eigen::MatrixXd > &Q, const Eigen::Ref< const Eigen::MatrixXd > &R)
Computes the optimal feedback controller, u=-Kx, and the optimal cost-to-go J = x'Sx for the problem: More...

## Function Documentation

 LinearQuadraticRegulatorResult drake::systems::controllers::DiscreteTimeLinearQuadraticRegulator ( const Eigen::Ref< const Eigen::MatrixXd > & A, const Eigen::Ref< const Eigen::MatrixXd > & B, const Eigen::Ref< const Eigen::MatrixXd > & Q, const Eigen::Ref< const Eigen::MatrixXd > & R )

Computes the optimal feedback controller, u=-Kx, and the optimal cost-to-go J = x'Sx for the problem:

$x[n+1] = Ax[n] + Bu[n]$

$\min_u \sum_0^\infty x'Qx + u'Ru$

Parameters
 A The state-space dynamics matrix of size num_states x num_states. B The state-space input matrix of size num_states x num_inputs. Q A symmetric positive semi-definite cost matrix of size num_states x num_states. R A symmetric positive definite cost matrix of size num_inputs x num_inputs.
Returns
A structure that contains the optimal feedback gain K and the quadratic cost term S. The optimal feedback control is u = -Kx;
Exceptions
 std::exception if R is not positive definite.

 FiniteHorizonLinearQuadraticRegulatorResult drake::systems::controllers::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 = FiniteHorizonLinearQuadraticRegulatorOptions() )

Solves the differential Riccati equation to compute the optimal controller and optimal cost-to-go for the finite-horizon linear quadratic regulator:

$\min_u (x(t_f)-x_d(t_f))'Q_f(x(t_f)-x_d(t_f)) + \int_{t_0}^{t_f} (x(t)-x_d(t))'Q(x(t)-x_d(t)) dt + \int_{t_0}^{t_f} (u(t)-u_d(t))'R(u(t)-u_d(t)) dt + \int_{t_0}^{t_f} 2(x(t)-x_d(t))'N(u(t)-u_d(t)) dt \\ \text{s.t. } \dot{x} - \dot{x}_0(t) = A(t)(x(t) - x_0(t)) + B(t)(u(t) - u_0(t)) + c(t)$

where A(t), B(t), and c(t) are taken from the gradients of the continuous-time dynamics ẋ = f(t,x,u), as A(t) = dfdx(t, x0(t), u0(t)), B(t) = dfdu(t, x0(t), u0(t)), and c(t) = f(t, x0(t), u0(t)) - ẋ0(t). x0(t) and u0(t) can be specified in options, otherwise are taken to be constant trajectories with values given by context.

Parameters
 system a System representing the plant. context a Context used to pass the default input, state, and parameters. Note: Use options to specify time-varying nominal state and/or input trajectories. t0 is the initial time. tf is the final time (with tf > t0). Q is nxn positive semi-definite. R is mxm positive definite. options is the optional FiniteHorizonLinearQuadraticRegulatorOptions.
Precondition
system must be a System<double> with (only) n continuous state variables and m inputs. It must be convertible to System<AutoDiffXd>.
Note
Support for difference-equation systems (
System<T>::IsDifferenceEquationSystem()) by solving the differential Riccati equation and richer specification of the objective are anticipated (they are listed in the code as TODOs).

## ◆ FittedValueIteration()

 std::pair >, Eigen::RowVectorXd> drake::systems::controllers::FittedValueIteration ( Simulator< double > * simulator, const std::function< double(const Context< double > &context)> & cost_function, const math::BarycentricMesh< double >::MeshGrid & state_grid, const math::BarycentricMesh< double >::MeshGrid & input_grid, double time_step, const DynamicProgrammingOptions & options = DynamicProgrammingOptions() )

Implements Fitted Value Iteration on a (triangulated) Barycentric Mesh, which designs a state-feedback policy to minimize the infinite-horizon cost ∑ γⁿ g(x[n],u[n]), where γ is the discount factor in options.

For background, and a description of this algorithm, see http://underactuated.csail.mit.edu/underactuated.html?chapter=dp . It currently requires that the system to be optimized has only continuous state and it is assumed to be time invariant. This code makes a discrete-time approximation (using time_step) for the value iteration update.

Parameters
 simulator contains the reference to the System being optimized and to a Context for that system, which may contain non-default Parameters, etc. The simulator is run for time_step seconds from every point on the mesh in order to approximate the dynamics; all of the simulation parameters (integrator, etc) are relevant during that evaluation. cost_function is the continuous-time instantaneous cost. This implementation of the discrete-time formulation above uses the approximation g(x,u) = time_step*cost_function(x,u). state_grid defines the mesh on the state space used to represent the cost-to-go function and the resulting policy. input_grid defines the discrete action space used in the value iteration update. time_step a time in seconds used for the discrete-time approximation. options optional DynamicProgrammingOptions structure.
Returns
a std::pair containing the resulting policy, implemented as a BarycentricMeshSystem, and the RowVectorXd J that defines the expected cost-to-go on a BarycentricMesh using state_grid. The policy has a single vector input (which is the continuous state of the system passed in through simulator) and a single vector output (which is the input of the system passed in through simulator).

 LinearQuadraticRegulatorResult drake::systems::controllers::LinearQuadraticRegulator ( const Eigen::Ref< const Eigen::MatrixXd > & A, const Eigen::Ref< const Eigen::MatrixXd > & B, const Eigen::Ref< const Eigen::MatrixXd > & Q, const Eigen::Ref< const Eigen::MatrixXd > & R, const Eigen::Ref< const Eigen::MatrixXd > & N = Eigen::Matrix< double, 0, 0 >::Zero(), const Eigen::Ref< const Eigen::MatrixXd > & F = Eigen::Matrix< double, 0, 0 >::Zero() )

Computes the optimal feedback controller, u=-Kx, and the optimal cost-to-go J = x'Sx for the problem:

$\min_u \int_0^\infty x'Qx + u'Ru + 2x'Nu dt$

$\dot{x} = Ax + Bu$

$Fx = 0$

Parameters
 A The state-space dynamics matrix of size num_states x num_states. B The state-space input matrix of size num_states x num_inputs. Q A symmetric positive semi-definite cost matrix of size num_states x num_states. R A symmetric positive definite cost matrix of size num_inputs x num_inputs. N A cost matrix of size num_states x num_inputs. If N.rows() == 0, N will be treated as a num_states x num_inputs zero matrix. F A constraint matrix of size num_constraints x num_states. rank(F) must be < num_states. If F.rows() == 0, F will be treated as a 0 x num_states zero matrix.
Returns
A structure that contains the optimal feedback gain K and the quadratic cost term S. The optimal feedback control is u = -Kx;
Exceptions
 std::exception if R is not positive definite.
Note
The system (A₁, B) should be stabilizable, where A₁=A−BR⁻¹Nᵀ.
The system (Q₁, A₁) should be detectable, where Q₁=Q−NR⁻¹Nᵀ.