|
Drake
|
|
Drake
|
A structure to facilitate passing the myriad of optional arguments to the FiniteHorizonLinearQuadraticRegulator algorithms.
#include <drake/systems/controllers/finite_horizon_linear_quadratic_regulator.h>
Public Member Functions | |
| FiniteHorizonLinearQuadraticRegulatorOptions ()=default | |
Public Attributes | |
| std::optional< Eigen::MatrixXd > | Qf |
| A num_states x num_states positive semi-definite matrix which specified the cost at the final time. More... | |
| std::optional< Eigen::MatrixXd > | N |
| A num_states x num_inputs matrix that describes the running cost 2(x-xd(t))'N(u-ud(t)). More... | |
| const trajectories::Trajectory< double > * | x0 {nullptr} |
| A nominal state trajectory. More... | |
| const trajectories::Trajectory< double > * | u0 {nullptr} |
| A nominal input trajectory. More... | |
| const trajectories::Trajectory< double > * | xd {nullptr} |
| A desired state trajectory. More... | |
| const trajectories::Trajectory< double > * | ud {nullptr} |
| A desired input trajectory. More... | |
| std::variant< systems::InputPortSelection, InputPortIndex > | input_port_index |
| For systems with multiple input ports, we must specify which input port is being used in the control design. More... | |
| bool | use_square_root_method {false} |
| Enables the "square-root" method solution to the Riccati equation. More... | |
| SimulatorConfig | simulator_config {} |
| For continuous-time dynamical systems, the Riccati equation is solved by the Simulator (running backwards in time). More... | |
|
default |
| std::variant<systems::InputPortSelection, InputPortIndex> input_port_index |
For systems with multiple input ports, we must specify which input port is being used in the control design.
| std::optional<Eigen::MatrixXd> N |
A num_states x num_inputs matrix that describes the running cost 2(x-xd(t))'N(u-ud(t)).
If unset, then N will be set to the zero matrix.
| std::optional<Eigen::MatrixXd> Qf |
A num_states x num_states positive semi-definite matrix which specified the cost at the final time.
If unset, then Qf will be set to the zero matrix.
| SimulatorConfig simulator_config {} |
For continuous-time dynamical systems, the Riccati equation is solved by the Simulator (running backwards in time).
Use this parameter to configure the simulator (e.g. choose non-default integrator or integrator parameters).
| const trajectories::Trajectory<double>* u0 {nullptr} |
A nominal input trajectory.
The system is linearized about this trajectory. u0 must be defined over the entire interval, [t0, tf]. If null, then u0 is taken to be a constant trajectory (whose value is specified by the context passed into the LQR method).
| const trajectories::Trajectory<double>* ud {nullptr} |
A desired input trajectory.
The objective is to regulate to this trajectory – the input component of the quadratic running cost is (u-ud(t))'R(u-ud(t)). If null, then ud(t) = u0(t).
| bool use_square_root_method {false} |
Enables the "square-root" method solution to the Riccati equation.
This is slightly more expensive and potentially less numerically accurate (errors are bounded on the square root), but is more numerically robust. When true, then you must also set a (positive definite and symmetric) Qf in this options struct.
| const trajectories::Trajectory<double>* x0 {nullptr} |
A nominal state trajectory.
The system is linearized about this trajectory. x0 must be defined over the entire interval [t0, tf]. If null, then x0 is taken to be a constant trajectory (whose value is specified by the context passed into the LQR method).
| const trajectories::Trajectory<double>* xd {nullptr} |
A desired state trajectory.
The objective is to regulate to this trajectory – the state component of the quadratic running cost is (x-xd(t))'Q(x-xd(t)) and the final cost is (x-xd(t))'Qf(x-xd(t)). If null, then xd(t) = x0(t).