// Copyright (C) 2015 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#undef DLIB_MPC_ABSTRACT_Hh_
#ifdef DLIB_MPC_ABSTRACT_Hh_
#include "../matrix.h"
namespace dlib
{
template <
long S_,
long I_,
unsigned long horizon_
>
class mpc
{
/*!
REQUIREMENTS ON horizon_
horizon_ > 0
REQUIREMENTS ON S_
S_ >= 0
REQUIREMENTS ON I_
I_ >= 0
WHAT THIS OBJECT REPRESENTS
This object implements a linear model predictive controller. To explain
what that means, suppose you have some process you want to control and the
process dynamics are described by the linear equation:
x_{i+1} = A*x_i + B*u_i + C
That is, the next state the system goes into is a linear function of its
current state (x_i) and the current control (u_i) plus some constant bias
or disturbance.
A model predictive controller can find the control (u) you should apply to
drive the state (x) to some reference value, or alternatively to make the
state track some reference time-varying sequence. It does this by
simulating the process for horizon_ time steps and selecting the control
that leads to the best performance over the next horizon_ steps.
To be precise, each time you ask this object for a control, it solves the
following quadratic program:
min sum_i trans(x_i-target_i)*Q*(x_i-target_i) + trans(u_i)*R*u_i
x_i,u_i
such that: x_0 == current_state
x_{i+1} == A*x_i + B*u_i + C
lower <= u_i <= upper
0 <= i < horizon_
and reports u_0 as the control you should take given that you are currently
in current_state. Q and R are user supplied matrices that define how we
penalize variations away from the target state as well as how much we want
to avoid generating large control signals.
Finally, the algorithm we use to solve this quadratic program is based
largely on the method described in:
A Fast Gradient method for embedded linear predictive control (2011)
by Markus Kogel and Rolf Findeisen
!*/
public:
const static long S = S_;
const static long I = I_;
const static unsigned long horizon = horizon_;
mpc(
);
/*!
ensures
- #get_max_iterations() == 0
- The A,B,C,Q,R,lower, and upper parameter matrices are filled with zeros.
Therefore, to use this object you must initialize it via the constructor
that supplies these parameters.
!*/
mpc (
const matrix<double,S,S>& A,
const matrix<double,S,I>& B,
const matrix<double,S,1>& C,
const matrix<double,S,1>& Q,
const matrix<double,I,1>& R,
const matrix<double,I,1>& lower,
const matrix<double,I,1>& upper
);
/*!
requires
- A.nr() > 0
- B.nc() > 0
- A.nr() == A.nc() == B.nr() == C.nr() == Q.nr()
- B.nc() == R.nr() == lower.nr() == upper.nr()
- min(Q) >= 0
- min(R) > 0
- min(upper-lower) >= 0
ensures
- #get_A() == A
- #get_B() == B
- #get_C() == C
- #get_Q() == Q
- #get_R() == R
- #get_lower_constraints() == lower
- #get_upper_constraints() == upper
- for all valid i:
- get_target(i) == a vector of all zeros
- get_target(i).size() == A.nr()
- #get_max_iterations() == 10000
- #get_epsilon() == 0.01
!*/
const matrix<double,S,S>& get_A (
) const;
/*!
ensures
- returns the A matrix from the quadratic program defined above.
!*/
const matrix<double,S,I>& get_B (
) const;
/*!
ensures
- returns the B matrix from the quadratic program defined above.
!*/
const matrix<double,S,1>& get_C (
) const;
/*!
ensures
- returns the C matrix from the quadratic program defined above.
!*/
const matrix<double,S,1>& get_Q (
) const;
/*!
ensures
- returns the diagonal of the Q matrix from the quadratic program defined
above.
!*/
const matrix<double,I,1>& get_R (
) const;
/*!
ensures
- returns the diagonal of the R matrix from the quadratic program defined
above.
!*/
const matrix<double,I,1>& get_lower_constraints (
) const;
/*!
ensures
- returns the lower matrix from the quadratic program defined above. All
controls generated by this object will have values no less than this
lower bound. That is, any control u will satisfy min(u-lower) >= 0.
!*/
const matrix<double,I,1>& get_upper_constraints (
) const;
/*!
ensures
- returns the upper matrix from the quadratic program defined above. All
controls generated by this object will have values no larger than this
upper bound. That is, any control u will satisfy min(upper-u) >= 0.
!*/
const matrix<double,S,1>& get_target (
const unsigned long time
) const;
/*!
requires
- time < horizon
ensures
- This object will try to find the control sequence that results in the
process obtaining get_target(time) state at the indicated time. Note
that the next time instant after "right now" is time 0.
!*/
void set_target (
const matrix<double,S,1>& val,
const unsigned long time
);
/*!
requires
- time < horizon
ensures
- #get_target(time) == val
!*/
void set_target (
const matrix<double,S,1>& val
);
/*!
ensures
- for all valid t:
- #get_target(t) == val
!*/
void set_last_target (
const matrix<double,S,1>& val
);
/*!
ensures
- performs: set_target(val, horizon-1)
!*/
unsigned long get_max_iterations (
) const;
/*!
ensures
- When operator() is called it solves an optimization problem to
get_epsilon() precision to determine the next control action. In
particular, we run the optimizer until the magnitude of each element of
the gradient vector is less than get_epsilon() or until
get_max_iterations() solver iterations have been executed.
!*/
void set_max_iterations (
unsigned long max_iter
);
/*!
ensures
- #get_max_iterations() == max_iter
!*/
void set_epsilon (
double eps
);
/*!
requires
- eps > 0
ensures
- #get_epsilon() == eps
!*/
double get_epsilon (
) const;
/*!
ensures
- When operator() is called it solves an optimization problem to
get_epsilon() precision to determine the next control action. In
particular, we run the optimizer until the magnitude of each element of
the gradient vector is less than get_epsilon() or until
get_max_iterations() solver iterations have been executed. This means
that smaller epsilon values will give more accurate outputs but may take
longer to compute.
!*/
matrix<double,I,1> operator() (
const matrix<double,S,1>& current_state
);
/*!
requires
- min(R) > 0
- A.nr() == current_state.size()
ensures
- Solves the model predictive control problem defined by the arguments to
this objects constructor, assuming that the starting state is given by
current_state. Then we return the control that should be taken in the
current state that best optimizes the quadratic objective function
defined above.
- We also shift over the target states so that you only need to update the
last one (if you are using non-zero target states) via a call to
set_last_target()). In particular, for all valid t, it will be the case
that:
- #get_target(t) == get_target(t+1)
- #get_target(horizon-1) == get_target(horizon-1)
!*/
};
}
#endif // DLIB_MPC_ABSTRACT_Hh_