/*! \file \brief Base classes for designers of control strategy \author Vaclav Smidl. ----------------------------------- BDM++ - C++ library for Bayesian Decision Making under Uncertainty Using IT++ for numerical operations ----------------------------------- */ #ifndef CTRL_BASE #define CTRL_BASE #include "../base/bdmbase.h" #include "../estim/kalman.h" namespace bdm { //! Base class for adaptive controllers //! The base class is, however, non-adaptive, method \c adapt() is empty. //! \note advanced Controllers will probably include estimator as their internal attribute (e.g. dual controllers) class Controller : public root { public: //! identifier of the designed action; RV rv; protected: //! identifier of the conditioning variables - data needed ; RV rvc; public: //! function processing new observations and adapting control strategy accordingly virtual void adapt ( const vec &data ) {}; //! function redesigning the control strategy virtual void redesign() {}; //! returns designed control action virtual vec ctrlaction ( const vec &cond ) { return vec ( 0 ); } void from_setting ( const Setting &set ) { shared_ptr rv_ptr = UI::build( set, "rv", UI::optional ); if( rv_ptr ) rv = *rv_ptr; shared_ptr rvc_ptr = UI::build( set, "rvc", UI::optional ); if( rvc_ptr ) rvc = *rvc_ptr; } //! access function const RV& _rv() { return rv; } //! access function const RV& _rvc() { return rvc; } //! register this controller with given datasource under name "name" virtual void log_register ( logger &L, const string &prefix ) { } //! write requested values into the logger virtual void log_write ( ) const { } }; //! Linear Quadratic Gaussian designer for constant penalizations and constant target //! Its internals are very close to Kalman estimator class LQG : public Controller { protected: //! StateSpace model from which we read data shared_ptr > S; //! required value of the output y at time t (assumed constant) vec y_req; //! required value of the output y at time t (assumed constant) vec u_req; //! Control horizon, set to maxint for infinite horizons int horizon; //! penalization matrix Qy chmat Qy; //! penalization matrix Qu chmat Qu; //! time of the design step - from horizon->0 int td; //! controller parameters mat L; //!@{ \name temporary storage for ricatti - use initialize //! convenience parameters int dimx; //! convenience parameters int dimy; //! convenience parameters int dimu; //! parameters mat pr; //! penalization mat qux; //! penalization mat qyx; //! internal quadratic form mat s; //! penalization mat qy; //! pre_qr part mat hqy; //! pre qr matrix mat pre_qr; //! post qr matrix mat post_qr; //!@} public: //! set system parameters from given matrices void set_system ( shared_ptr > S0 ); //! update internal whan system has changed void update_system(); //! set penalization matrices and control horizon void set_control_parameters ( const mat &Qy0, const mat &Qu0, const vec &y_req0, int horizon0 ); //! set penalization matrices and control horizon void set_control_Qy ( const mat &Qy0 ) { Qy = Qy0; } //! refresh temporary storage - inefficient can be improved void initial_belmann() { s = 1e-5 * eye ( dimx + dimu + dimy ); }; //! validation procedure void validate(); //! function for future use which is called at each time td; Should call initialize()! //! redesign one step of the void ricatti_step(); void redesign(); //! compute control action vec ctrlaction ( const vec &state, const vec &ukm ) { vec pom = concat ( state, ones ( dimy ), ukm ); return L*pom; } //! access function mat _L() const { return L; } } ; } // namespace #endif //CTRL_BASE