Classes
struct	DynamicProgrammingOptions
	Consolidates the many possible options to be passed to the dynamic programming algorithms. More...
struct	FiniteHorizonLinearQuadraticRegulatorOptions
	A structure to facilitate passing the myriad of optional arguments to the FiniteHorizonLinearQuadraticRegulator algorithms. More...
struct	FiniteHorizonLinearQuadraticRegulatorResult
	A structure that contains the basic FiniteHorizonLinearQuadraticRegulator results. More...
class	InverseDynamics
	Solves inverse dynamics with no consideration for joint actuator force limits. More...
class	InverseDynamicsController
	A state feedback controller that uses a PidController to generate desired accelerations, which are then converted into MultibodyPlant actuation inputs using InverseDynamics (with mode = InverseDynamics::kInverseDynamics). More...
class	JointImpedanceController
	Drake does not yet offer a joint impedance controller, which would use feedback to shape the stiffness, damping, and inertia of the closed-loop system. More...
class	JointStiffnessController
	Implements a joint-space stiffness controller of the form. More...
struct	LinearQuadraticRegulatorResult
class	PidControlledSystem
	A system that encapsulates a PidController and a controlled System (a.k.a the "plant"). More...
class	PidController
	Implements the PID controller. More...
class	StateFeedbackControllerInterface
	Interface for state feedback controllers. More...

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`.
Eigen::VectorXd	LinearProgrammingApproximateDynamicProgramming (Simulator< double > *simulator, const std::function< double(const Context< double > &context)> &cost_function, const std::function< symbolic::Expression(const Eigen::Ref< const Eigen::VectorXd > &state, const VectorX< symbolic::Variable > &parameters)> &linearly_parameterized_cost_to_go_function, int num_parameters, const Eigen::Ref< const Eigen::MatrixXd > &state_samples, const Eigen::Ref< const Eigen::MatrixXd > &input_samples, double time_step, const DynamicProgrammingOptions &options=DynamicProgrammingOptions())
	Implements the Linear Programming approach to approximate dynamic programming.
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())
	If `system` is a continuous-time system, then solves the differential Riccati equation to compute the optimal controller and optimal cost-to-go for the continuous-time finite-horizon linear quadratic regulator:
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=FiniteHorizonLinearQuadraticRegulatorOptions())
	Variant of FiniteHorizonLinearQuadraticRegulator that returns a System implementing the regulator (controller) as a System, with a single "plant_state" input for the estimated plant state, and a single "control" output for the regulator control output.
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:
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, const Eigen::Ref< const Eigen::MatrixXd > &N=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:
std::unique_ptr< LinearSystem< double > >	LinearQuadraticRegulator (const LinearSystem< double > &system, 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())
	Creates a system that implements the optimal time-invariant linear quadratic regulator (LQR).
std::unique_ptr< AffineSystem< double > >	LinearQuadraticRegulator (const System< double > &system, const Context< double > &context, 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(), int input_port_index=0)
	Linearizes the System around the specified Context, computes the optimal time-invariant linear quadratic regulator (LQR), and returns a System which implements that regulator in the original System's coordinates.

Classes

Functions