- 3.0.2 optimal control module.
LQR.cpp

Simple example how to linearize a system and design an LQR controller.

#include <ct/optcon/optcon.h> // also includes ct_core
#include "exampleDir.h"
int main(int argc, char** argv)
{
// get the state and control input dimension of the oscillator
// create an auto-differentiable instance of the oscillator dynamics
ct::core::ADCGScalar w_n(50.0);
std::shared_ptr<ct::core::ControlledSystem<state_dim, control_dim, ct::core::ADCGScalar>> oscillatorDynamics(
// create an Auto-Differentiation Linearizer with code generation on the quadrotor model
ct::core::ADCodegenLinearizer<state_dim, control_dim> adLinearizer(oscillatorDynamics);
// compile the linearized model just-in-time
adLinearizer.compileJIT();
// define the linearization point around steady state
x.setZero();
u.setZero();
double t = 0.0;
// compute the linearization around the nominal state using the Auto-Diff Linearizer
auto A = adLinearizer.getDerivativeState(x, u, t);
auto B = adLinearizer.getDerivativeControl(x, u, t);
// load the weighting matrices
quadraticCost.loadConfigFile(ct::optcon::exampleDir + "/lqrCost.info", "termLQR");
auto Q = quadraticCost.stateSecondDerivative(x, u, t); // x, u and t can be arbitrary here
auto R = quadraticCost.controlSecondDerivative(x, u, t); // x, u and t can be arbitrary here
// design the LQR controller
std::cout << "A: " << std::endl << A << std::endl << std::endl;
std::cout << "B: " << std::endl << B << std::endl << std::endl;
std::cout << "Q: " << std::endl << Q << std::endl << std::endl;
std::cout << "R: " << std::endl << R << std::endl << std::endl;
lqrSolver.compute(Q, R, A, B, K);
std::cout << "LQR gain matrix:" << std::endl << K << std::endl;
return 1;
}