| Directory: | ./ |
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| File: | include/crocoddyl/core/diff-action-base.hpp |
| Date: | 2025-02-24 23:41:29 |
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| 1 | /////////////////////////////////////////////////////////////////////////////// | ||
| 2 | // BSD 3-Clause License | ||
| 3 | // | ||
| 4 | // Copyright (C) 2019-2024, LAAS-CNRS, University of Edinburgh, | ||
| 5 | // University of Oxford, Heriot-Watt University | ||
| 6 | // Copyright note valid unless otherwise stated in individual files. | ||
| 7 | // All rights reserved. | ||
| 8 | /////////////////////////////////////////////////////////////////////////////// | ||
| 9 | |||
| 10 | #ifndef CROCODDYL_CORE_DIFF_ACTION_BASE_HPP_ | ||
| 11 | #define CROCODDYL_CORE_DIFF_ACTION_BASE_HPP_ | ||
| 12 | |||
| 13 | #include <boost/make_shared.hpp> | ||
| 14 | #include <memory> | ||
| 15 | #include <stdexcept> | ||
| 16 | |||
| 17 | #include "crocoddyl/core/fwd.hpp" | ||
| 18 | #include "crocoddyl/core/state-base.hpp" | ||
| 19 | #include "crocoddyl/core/utils/math.hpp" | ||
| 20 | |||
| 21 | namespace crocoddyl { | ||
| 22 | |||
| 23 | /** | ||
| 24 | * @brief Abstract class for differential action model | ||
| 25 | * | ||
| 26 | * A differential action model combines dynamics, cost and constraints models. | ||
| 27 | * We can use it in each node of our optimal control problem thanks to dedicated | ||
| 28 | * integration rules (e.g., `IntegratedActionModelEulerTpl` or | ||
| 29 | * `IntegratedActionModelRK4Tpl`). These integrated action models produce action | ||
| 30 | * models (`ActionModelAbstractTpl`). Thus, every time that we want to describe | ||
| 31 | * a problem, we need to provide ways of computing the dynamics, cost, | ||
| 32 | * constraints functions and their derivatives. All these are described inside | ||
| 33 | * the differential action model. | ||
| 34 | * | ||
| 35 | * Concretely speaking, the differential action model is the time-continuous | ||
| 36 | * version of an action model, i.e., \f[ \begin{aligned} | ||
| 37 | * &\dot{\mathbf{v}} = \mathbf{f}(\mathbf{q}, \mathbf{v}, \mathbf{u}), | ||
| 38 | * &\textrm{(dynamics)}\\ | ||
| 39 | * &\ell(\mathbf{q}, \mathbf{v},\mathbf{u}) = \int_0^{\delta t} | ||
| 40 | * a(\mathbf{r}(\mathbf{q}, \mathbf{v},\mathbf{u}))\,dt, | ||
| 41 | * &\textrm{(cost)}\\ | ||
| 42 | * &\mathbf{g}(\mathbf{q}, \mathbf{v},\mathbf{u})<\mathbf{0}, | ||
| 43 | * &\textrm{(inequality constraint)}\\ | ||
| 44 | * &\mathbf{h}(\mathbf{q}, \mathbf{v},\mathbf{u})=\mathbf{0}, &\textrm{(equality | ||
| 45 | * constraint)} \end{aligned} \f] where | ||
| 46 | * - the configuration \f$\mathbf{q}\in\mathcal{Q}\f$ lies in the configuration | ||
| 47 | * manifold described with a `nq`-tuple, | ||
| 48 | * - the velocity \f$\mathbf{v}\in T_{\mathbf{q}}\mathcal{Q}\f$ is the tangent | ||
| 49 | * vector to the configuration manifold with `nv` dimension, | ||
| 50 | * - the control input \f$\mathbf{u}\in\mathbb{R}^{nu}\f$ is an Euclidean | ||
| 51 | * vector, | ||
| 52 | * - \f$\mathbf{r}(\cdot)\f$ and \f$a(\cdot)\f$ are the residual and activation | ||
| 53 | * functions (see `ResidualModelAbstractTpl` and `ActivationModelAbstractTpl`, | ||
| 54 | * respectively), | ||
| 55 | * - \f$\mathbf{g}(\cdot)\in\mathbb{R}^{ng}\f$ and | ||
| 56 | * \f$\mathbf{h}(\cdot)\in\mathbb{R}^{nh}\f$ are the inequality and equality | ||
| 57 | * vector functions, respectively. | ||
| 58 | * | ||
| 59 | * Both configuration and velocity describe the system space | ||
| 60 | * \f$\mathbf{x}=(\mathbf{q}, \mathbf{v})\in\mathcal{X}\f$ which lies in the | ||
| 61 | * state manifold. Note that the acceleration \f$\dot{\mathbf{v}}\in | ||
| 62 | * T_{\mathbf{q}}\mathcal{Q}\f$ lies also in the tangent space of the | ||
| 63 | * configuration manifold. The computation of these equations are carried out | ||
| 64 | * inside `calc()` function. In short, this function computes the system | ||
| 65 | * acceleration, cost and constraints values (also called constraints | ||
| 66 | * violations). This procedure is equivalent to running a forward pass of the | ||
| 67 | * action model. | ||
| 68 | * | ||
| 69 | * However, during numerical optimization, we also need to run backward passes | ||
| 70 | * of the differential action model. These calculations are performed by | ||
| 71 | * `calcDiff()`. In short, this function builds a linear-quadratic approximation | ||
| 72 | * of the differential action model, i.e., \f[ \begin{aligned} | ||
| 73 | * &\delta\dot{\mathbf{v}} = | ||
| 74 | * \mathbf{f_{q}}\delta\mathbf{q}+\mathbf{f_{v}}\delta\mathbf{v}+\mathbf{f_{u}}\delta\mathbf{u}, | ||
| 75 | * &\textrm{(dynamics)}\\ | ||
| 76 | * &\ell(\delta\mathbf{q},\delta\mathbf{v},\delta\mathbf{u}) = \begin{bmatrix}1 | ||
| 77 | * \\ \delta\mathbf{q} \\ \delta\mathbf{v} | ||
| 78 | * \\ \delta\mathbf{u}\end{bmatrix}^T \begin{bmatrix}0 & \mathbf{\ell_q}^T & | ||
| 79 | * \mathbf{\ell_v}^T & \mathbf{\ell_u}^T \\ \mathbf{\ell_q} & \mathbf{\ell_{qq}} | ||
| 80 | * & | ||
| 81 | * \mathbf{\ell_{qv}} & \mathbf{\ell_{uq}}^T \\ | ||
| 82 | * \mathbf{\ell_v} & \mathbf{\ell_{vq}} & \mathbf{\ell_{vv}} & | ||
| 83 | * \mathbf{\ell_{uv}}^T \\ | ||
| 84 | * \mathbf{\ell_u} & \mathbf{\ell_{uq}} & \mathbf{\ell_{uv}} & | ||
| 85 | * \mathbf{\ell_{uu}}\end{bmatrix} \begin{bmatrix}1 \\ \delta\mathbf{q} | ||
| 86 | * \\ \delta\mathbf{v} \\ | ||
| 87 | * \delta\mathbf{u}\end{bmatrix}, &\textrm{(cost)}\\ | ||
| 88 | * &\mathbf{g_q}\delta\mathbf{q}+\mathbf{g_v}\delta\mathbf{v}+\mathbf{g_u}\delta\mathbf{u}\leq\mathbf{0}, | ||
| 89 | * &\textrm{(inequality constraints)}\\ | ||
| 90 | * &\mathbf{h_q}\delta\mathbf{q}+\mathbf{h_v}\delta\mathbf{v}+\mathbf{h_u}\delta\mathbf{u}=\mathbf{0}, | ||
| 91 | * &\textrm{(equality constraints)} \end{aligned} \f] where | ||
| 92 | * - \f$\mathbf{f_x}=(\mathbf{f_q};\,\, \mathbf{f_v})\in\mathbb{R}^{nv\times | ||
| 93 | * ndx}\f$ and \f$\mathbf{f_u}\in\mathbb{R}^{nv\times nu}\f$ are the Jacobians | ||
| 94 | * of the dynamics, | ||
| 95 | * - \f$\mathbf{\ell_x}=(\mathbf{\ell_q};\,\, | ||
| 96 | * \mathbf{\ell_v})\in\mathbb{R}^{ndx}\f$ and | ||
| 97 | * \f$\mathbf{\ell_u}\in\mathbb{R}^{nu}\f$ are the Jacobians of the cost | ||
| 98 | * function, | ||
| 99 | * - \f$\mathbf{\ell_{xx}}=(\mathbf{\ell_{qq}}\,\, \mathbf{\ell_{qv}};\,\, | ||
| 100 | * \mathbf{\ell_{vq}}\, \mathbf{\ell_{vv}})\in\mathbb{R}^{ndx\times ndx}\f$, | ||
| 101 | * \f$\mathbf{\ell_{xu}}=(\mathbf{\ell_q};\,\, | ||
| 102 | * \mathbf{\ell_v})\in\mathbb{R}^{ndx\times nu}\f$ and | ||
| 103 | * \f$\mathbf{\ell_{uu}}\in\mathbb{R}^{nu\times nu}\f$ are the Hessians of the | ||
| 104 | * cost function, | ||
| 105 | * - \f$\mathbf{g_x}=(\mathbf{g_q};\,\, \mathbf{g_v})\in\mathbb{R}^{ng\times | ||
| 106 | * ndx}\f$ and \f$\mathbf{g_u}\in\mathbb{R}^{ng\times nu}\f$ are the Jacobians | ||
| 107 | * of the inequality constraints, and | ||
| 108 | * - \f$\mathbf{h_x}=(\mathbf{h_q};\,\, \mathbf{h_v})\in\mathbb{R}^{nh\times | ||
| 109 | * ndx}\f$ and \f$\mathbf{h_u}\in\mathbb{R}^{nh\times nu}\f$ are the Jacobians | ||
| 110 | * of the equality constraints. | ||
| 111 | * | ||
| 112 | * Additionally, it is important to note that `calcDiff()` computes the | ||
| 113 | * derivatives using the latest stored values by `calc()`. Thus, we need to | ||
| 114 | * first run `calc()`. | ||
| 115 | * | ||
| 116 | * \sa `ActionModelAbstractTpl`, `calc()`, `calcDiff()`, `createData()` | ||
| 117 | */ | ||
| 118 | template <typename _Scalar> | ||
| 119 | class DifferentialActionModelAbstractTpl { | ||
| 120 | public: | ||
| 121 | EIGEN_MAKE_ALIGNED_OPERATOR_NEW | ||
| 122 | |||
| 123 | typedef _Scalar Scalar; | ||
| 124 | typedef MathBaseTpl<Scalar> MathBase; | ||
| 125 | typedef DifferentialActionDataAbstractTpl<Scalar> | ||
| 126 | DifferentialActionDataAbstract; | ||
| 127 | typedef StateAbstractTpl<Scalar> StateAbstract; | ||
| 128 | typedef typename MathBase::VectorXs VectorXs; | ||
| 129 | typedef typename MathBase::MatrixXs MatrixXs; | ||
| 130 | |||
| 131 | /** | ||
| 132 | * @brief Initialize the differential action model | ||
| 133 | * | ||
| 134 | * @param[in] state State description | ||
| 135 | * @param[in] nu Dimension of control vector | ||
| 136 | * @param[in] nr Dimension of cost-residual vector | ||
| 137 | * @param[in] ng Number of inequality constraints (default 0) | ||
| 138 | * @param[in] nh Number of equality constraints (default 0) | ||
| 139 | * @param[in] ng_T Number of inequality terminal constraints (default 0) | ||
| 140 | * @param[in] nh_T Number of equality terminal constraints (default 0) | ||
| 141 | */ | ||
| 142 | DifferentialActionModelAbstractTpl(std::shared_ptr<StateAbstract> state, | ||
| 143 | const std::size_t nu, | ||
| 144 | const std::size_t nr = 0, | ||
| 145 | const std::size_t ng = 0, | ||
| 146 | const std::size_t nh = 0, | ||
| 147 | const std::size_t ng_T = 0, | ||
| 148 | const std::size_t nh_T = 0); | ||
| 149 | virtual ~DifferentialActionModelAbstractTpl(); | ||
| 150 | |||
| 151 | /** | ||
| 152 | * @brief Compute the system acceleration and cost value | ||
| 153 | * | ||
| 154 | * @param[in] data Differential action data | ||
| 155 | * @param[in] x State point \f$\mathbf{x}\in\mathbb{R}^{ndx}\f$ | ||
| 156 | * @param[in] u Control input \f$\mathbf{u}\in\mathbb{R}^{nu}\f$ | ||
| 157 | */ | ||
| 158 | virtual void calc(const std::shared_ptr<DifferentialActionDataAbstract>& data, | ||
| 159 | const Eigen::Ref<const VectorXs>& x, | ||
| 160 | const Eigen::Ref<const VectorXs>& u) = 0; | ||
| 161 | |||
| 162 | /** | ||
| 163 | * @brief Compute the total cost value for nodes that depends only on the | ||
| 164 | * state | ||
| 165 | * | ||
| 166 | * It updates the total cost and the system acceleration is not updated as the | ||
| 167 | * control input is undefined. This function is used in the terminal nodes of | ||
| 168 | * an optimal control problem. | ||
| 169 | * | ||
| 170 | * @param[in] data Differential action data | ||
| 171 | * @param[in] x State point \f$\mathbf{x}\in\mathbb{R}^{ndx}\f$ | ||
| 172 | */ | ||
| 173 | virtual void calc(const std::shared_ptr<DifferentialActionDataAbstract>& data, | ||
| 174 | const Eigen::Ref<const VectorXs>& x); | ||
| 175 | |||
| 176 | /** | ||
| 177 | * @brief Compute the derivatives of the dynamics and cost functions | ||
| 178 | * | ||
| 179 | * It computes the partial derivatives of the dynamical system and the cost | ||
| 180 | * function. It assumes that `calc()` has been run first. This function builds | ||
| 181 | * a quadratic approximation of the time-continuous action model (i.e. | ||
| 182 | * dynamical system and cost function). | ||
| 183 | * | ||
| 184 | * @param[in] data Differential action data | ||
| 185 | * @param[in] x State point \f$\mathbf{x}\in\mathbb{R}^{ndx}\f$ | ||
| 186 | * @param[in] u Control input \f$\mathbf{u}\in\mathbb{R}^{nu}\f$ | ||
| 187 | */ | ||
| 188 | virtual void calcDiff( | ||
| 189 | const std::shared_ptr<DifferentialActionDataAbstract>& data, | ||
| 190 | const Eigen::Ref<const VectorXs>& x, | ||
| 191 | const Eigen::Ref<const VectorXs>& u) = 0; | ||
| 192 | |||
| 193 | /** | ||
| 194 | * @brief Compute the derivatives of the cost functions with respect to the | ||
| 195 | * state only | ||
| 196 | * | ||
| 197 | * It updates the derivatives of the cost function with respect to the state | ||
| 198 | * only. This function is used in the terminal nodes of an optimal control | ||
| 199 | * problem. | ||
| 200 | * | ||
| 201 | * @param[in] data Differential action data | ||
| 202 | * @param[in] x State point \f$\mathbf{x}\in\mathbb{R}^{ndx}\f$ | ||
| 203 | */ | ||
| 204 | virtual void calcDiff( | ||
| 205 | const std::shared_ptr<DifferentialActionDataAbstract>& data, | ||
| 206 | const Eigen::Ref<const VectorXs>& x); | ||
| 207 | |||
| 208 | /** | ||
| 209 | * @brief Create the differential action data | ||
| 210 | * | ||
| 211 | * @return the differential action data | ||
| 212 | */ | ||
| 213 | virtual std::shared_ptr<DifferentialActionDataAbstract> createData(); | ||
| 214 | |||
| 215 | /** | ||
| 216 | * @brief Checks that a specific data belongs to this model | ||
| 217 | */ | ||
| 218 | virtual bool checkData( | ||
| 219 | const std::shared_ptr<DifferentialActionDataAbstract>& data); | ||
| 220 | |||
| 221 | /** | ||
| 222 | * @brief Computes the quasic static commands | ||
| 223 | * | ||
| 224 | * The quasic static commands are the ones produced for a the reference | ||
| 225 | * posture as an equilibrium point, i.e. for | ||
| 226 | * \f$\mathbf{f}(\mathbf{q},\mathbf{v}=\mathbf{0},\mathbf{u})=\mathbf{0}\f$ | ||
| 227 | * | ||
| 228 | * @param[in] data Differential action data | ||
| 229 | * @param[out] u Quasic static commands | ||
| 230 | * @param[in] x State point (velocity has to be zero) | ||
| 231 | * @param[in] maxiter Maximum allowed number of iterations | ||
| 232 | * @param[in] tol Tolerance | ||
| 233 | */ | ||
| 234 | virtual void quasiStatic( | ||
| 235 | const std::shared_ptr<DifferentialActionDataAbstract>& data, | ||
| 236 | Eigen::Ref<VectorXs> u, const Eigen::Ref<const VectorXs>& x, | ||
| 237 | const std::size_t maxiter = 100, const Scalar tol = Scalar(1e-9)); | ||
| 238 | |||
| 239 | /** | ||
| 240 | * @copybrief quasicStatic() | ||
| 241 | * | ||
| 242 | * @copydetails quasicStatic() | ||
| 243 | * | ||
| 244 | * @param[in] data Differential action data | ||
| 245 | * @param[in] x State point (velocity has to be zero) | ||
| 246 | * @param[in] maxiter Maximum allowed number of iterations | ||
| 247 | * @param[in] tol Tolerance | ||
| 248 | * @return Quasic static commands | ||
| 249 | */ | ||
| 250 | VectorXs quasiStatic_x( | ||
| 251 | const std::shared_ptr<DifferentialActionDataAbstract>& data, | ||
| 252 | const VectorXs& x, const std::size_t maxiter = 100, | ||
| 253 | const Scalar tol = Scalar(1e-9)); | ||
| 254 | |||
| 255 | /** | ||
| 256 | * @brief Return the dimension of the control input | ||
| 257 | */ | ||
| 258 | std::size_t get_nu() const; | ||
| 259 | |||
| 260 | /** | ||
| 261 | * @brief Return the dimension of the cost-residual vector | ||
| 262 | */ | ||
| 263 | std::size_t get_nr() const; | ||
| 264 | |||
| 265 | /** | ||
| 266 | * @brief Return the number of inequality constraints | ||
| 267 | */ | ||
| 268 | virtual std::size_t get_ng() const; | ||
| 269 | |||
| 270 | /** | ||
| 271 | * @brief Return the number of equality constraints | ||
| 272 | */ | ||
| 273 | virtual std::size_t get_nh() const; | ||
| 274 | |||
| 275 | /** | ||
| 276 | * @brief Return the number of inequality terminal constraints | ||
| 277 | */ | ||
| 278 | virtual std::size_t get_ng_T() const; | ||
| 279 | |||
| 280 | /** | ||
| 281 | * @brief Return the number of equality terminal constraints | ||
| 282 | */ | ||
| 283 | virtual std::size_t get_nh_T() const; | ||
| 284 | |||
| 285 | /** | ||
| 286 | * @brief Return the state | ||
| 287 | */ | ||
| 288 | const std::shared_ptr<StateAbstract>& get_state() const; | ||
| 289 | |||
| 290 | /** | ||
| 291 | * @brief Return the lower bound of the inequality constraints | ||
| 292 | */ | ||
| 293 | virtual const VectorXs& get_g_lb() const; | ||
| 294 | |||
| 295 | /** | ||
| 296 | * @brief Return the upper bound of the inequality constraints | ||
| 297 | */ | ||
| 298 | virtual const VectorXs& get_g_ub() const; | ||
| 299 | |||
| 300 | /** | ||
| 301 | * @brief Return the control lower bound | ||
| 302 | */ | ||
| 303 | const VectorXs& get_u_lb() const; | ||
| 304 | |||
| 305 | /** | ||
| 306 | * @brief Return the control upper bound | ||
| 307 | */ | ||
| 308 | const VectorXs& get_u_ub() const; | ||
| 309 | |||
| 310 | /** | ||
| 311 | * @brief Indicates if there are defined control limits | ||
| 312 | */ | ||
| 313 | bool get_has_control_limits() const; | ||
| 314 | |||
| 315 | /** | ||
| 316 | * @brief Modify the lower bound of the inequality constraints | ||
| 317 | */ | ||
| 318 | void set_g_lb(const VectorXs& g_lb); | ||
| 319 | |||
| 320 | /** | ||
| 321 | * @brief Modify the upper bound of the inequality constraints | ||
| 322 | */ | ||
| 323 | void set_g_ub(const VectorXs& g_ub); | ||
| 324 | |||
| 325 | /** | ||
| 326 | * @brief Modify the control lower bounds | ||
| 327 | */ | ||
| 328 | void set_u_lb(const VectorXs& u_lb); | ||
| 329 | |||
| 330 | /** | ||
| 331 | * @brief Modify the control upper bounds | ||
| 332 | */ | ||
| 333 | void set_u_ub(const VectorXs& u_ub); | ||
| 334 | |||
| 335 | /** | ||
| 336 | * @brief Print information on the differential action model | ||
| 337 | */ | ||
| 338 | template <class Scalar> | ||
| 339 | friend std::ostream& operator<<( | ||
| 340 | std::ostream& os, | ||
| 341 | const DifferentialActionModelAbstractTpl<Scalar>& model); | ||
| 342 | |||
| 343 | /** | ||
| 344 | * @brief Print relevant information of the differential action model | ||
| 345 | * | ||
| 346 | * @param[out] os Output stream object | ||
| 347 | */ | ||
| 348 | virtual void print(std::ostream& os) const; | ||
| 349 | |||
| 350 | private: | ||
| 351 | std::size_t ng_internal_; //!< Internal object for storing the number of | ||
| 352 | //!< inequality constraints | ||
| 353 | std::size_t nh_internal_; //!< Internal object for storing the number of | ||
| 354 | //!< equality constraints | ||
| 355 | |||
| 356 | protected: | ||
| 357 | std::size_t nu_; //!< Control dimension | ||
| 358 | std::size_t nr_; //!< Dimension of the cost residual | ||
| 359 | std::size_t ng_; //!< Number of inequality constraints | ||
| 360 | std::size_t nh_; //!< Number of equality constraints | ||
| 361 | std::size_t ng_T_; //!< Number of inequality terminal constraints | ||
| 362 | std::size_t nh_T_; //!< Number of equality terminal constraints | ||
| 363 | std::shared_ptr<StateAbstract> state_; //!< Model of the state | ||
| 364 | VectorXs unone_; //!< Neutral state | ||
| 365 | VectorXs g_lb_; //!< Lower bound of the inequality constraints | ||
| 366 | VectorXs g_ub_; //!< Lower bound of the inequality constraints | ||
| 367 | VectorXs u_lb_; //!< Lower control limits | ||
| 368 | VectorXs u_ub_; //!< Upper control limits | ||
| 369 | bool has_control_limits_; //!< Indicates whether any of the control limits is | ||
| 370 | //!< finite | ||
| 371 | |||
| 372 | /** | ||
| 373 | * @brief Update the status of the control limits (i.e. if there are defined | ||
| 374 | * limits) | ||
| 375 | */ | ||
| 376 | void update_has_control_limits(); | ||
| 377 | |||
| 378 | template <class Scalar> | ||
| 379 | friend class IntegratedActionModelAbstractTpl; | ||
| 380 | template <class Scalar> | ||
| 381 | friend class ConstraintModelManagerTpl; | ||
| 382 | }; | ||
| 383 | |||
| 384 | template <typename _Scalar> | ||
| 385 | struct DifferentialActionDataAbstractTpl { | ||
| 386 | EIGEN_MAKE_ALIGNED_OPERATOR_NEW | ||
| 387 | |||
| 388 | typedef _Scalar Scalar; | ||
| 389 | typedef MathBaseTpl<Scalar> MathBase; | ||
| 390 | typedef typename MathBase::VectorXs VectorXs; | ||
| 391 | typedef typename MathBase::MatrixXs MatrixXs; | ||
| 392 | |||
| 393 | template <template <typename Scalar> class Model> | ||
| 394 | 224102 | explicit DifferentialActionDataAbstractTpl(Model<Scalar>* const model) | |
| 395 | 224102 | : cost(Scalar(0.)), | |
| 396 |
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224102 | xout(model->get_state()->get_nv()), |
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224102 | Fx(model->get_state()->get_nv(), model->get_state()->get_ndx()), |
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224102 | Fu(model->get_state()->get_nv(), model->get_nu()), |
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224102 | r(model->get_nr()), |
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224102 | Lx(model->get_state()->get_ndx()), |
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224102 | Lu(model->get_nu()), |
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224102 | Lxx(model->get_state()->get_ndx(), model->get_state()->get_ndx()), |
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224102 | Lxu(model->get_state()->get_ndx(), model->get_nu()), |
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224102 | Luu(model->get_nu(), model->get_nu()), |
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448204 | g(model->get_ng() > model->get_ng_T() ? model->get_ng() |
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224102 | : model->get_ng_T()), |
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448204 | Gx(model->get_ng() > model->get_ng_T() ? model->get_ng() |
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224102 | : model->get_ng_T(), |
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224102 | model->get_state()->get_ndx()), |
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448204 | Gu(model->get_ng() > model->get_ng_T() ? model->get_ng() |
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224102 | : model->get_ng_T(), |
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224102 | model->get_nu()), |
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378870 | h(model->get_nh() > model->get_nh_T() ? model->get_nh() |
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154768 | : model->get_nh_T()), |
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378870 | Hx(model->get_nh() > model->get_nh_T() ? model->get_nh() |
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154768 | : model->get_nh_T(), |
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224102 | model->get_state()->get_ndx()), |
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448204 | model->get_nu()) { |
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224102 | xout.setZero(); |
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224102 | Fx.setZero(); |
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224102 | Fu.setZero(); |
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224102 | Lx.setZero(); |
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224102 | Hu.setZero(); |
| 436 | 224102 | } | |
| 437 | 112051 | virtual ~DifferentialActionDataAbstractTpl() {} | |
| 438 | |||
| 439 | Scalar cost; //!< cost value | ||
| 440 | VectorXs xout; //!< evolution state | ||
| 441 | MatrixXs Fx; //!< Jacobian of the dynamics w.r.t. the state \f$\mathbf{x}\f$ | ||
| 442 | MatrixXs | ||
| 443 | Fu; //!< Jacobian of the dynamics w.r.t. the control \f$\mathbf{u}\f$ | ||
| 444 | VectorXs r; //!< Cost residual | ||
| 445 | VectorXs Lx; //!< Jacobian of the cost w.r.t. the state \f$\mathbf{x}\f$ | ||
| 446 | VectorXs Lu; //!< Jacobian of the cost w.r.t. the control \f$\mathbf{u}\f$ | ||
| 447 | MatrixXs Lxx; //!< Hessian of the cost w.r.t. the state \f$\mathbf{x}\f$ | ||
| 448 | MatrixXs Lxu; //!< Hessian of the cost w.r.t. the state \f$\mathbf{x}\f$ and | ||
| 449 | //!< control u | ||
| 450 | MatrixXs Luu; //!< Hessian of the cost w.r.t. the control \f$\mathbf{u}\f$ | ||
| 451 | VectorXs g; //!< Inequality constraint values | ||
| 452 | MatrixXs Gx; //!< Jacobian of the inequality constraint w.r.t. the state | ||
| 453 | //!< \f$\mathbf{x}\f$ | ||
| 454 | MatrixXs Gu; //!< Jacobian of the inequality constraint w.r.t. the control | ||
| 455 | //!< \f$\mathbf{u}\f$ | ||
| 456 | VectorXs h; //!< Equality constraint values | ||
| 457 | MatrixXs Hx; //!< Jacobian of the equality constraint w.r.t. the state | ||
| 458 | //!< \f$\mathbf{x}\f$ | ||
| 459 | MatrixXs Hu; //!< Jacobian of the equality constraint w.r.t the control | ||
| 460 | //!< \f$\mathbf{u}\f$ | ||
| 461 | }; | ||
| 462 | |||
| 463 | } // namespace crocoddyl | ||
| 464 | |||
| 465 | /* --- Details -------------------------------------------------------------- */ | ||
| 466 | /* --- Details -------------------------------------------------------------- */ | ||
| 467 | /* --- Details -------------------------------------------------------------- */ | ||
| 468 | #include "crocoddyl/core/diff-action-base.hxx" | ||
| 469 | |||
| 470 | #endif // CROCODDYL_CORE_DIFF_ACTION_BASE_HPP_ | ||
| 471 |