diff --git a/mpc/README.md b/mpc/README.md
deleted file mode 100644
index 67d1112..0000000
--- a/mpc/README.md
+++ /dev/null
@@ -1,110 +0,0 @@
-# Model Predictive Control Tool
-This program is about template, generic function of linear model predictive control
-
-# Documentation of the MPC function
-Linear model predicitive control should have state equation.
-So if you want to use this function, you should model the plant as state equation.
-Therefore, the parameters of this class are as following
-
-**class MpcController()**
-
-Attributes :
-
-- A : numpy.ndarray
- - system matrix
-- B : numpy.ndarray
- - input matrix
-- Q : numpy.ndarray
- - evaluation function weight for states
-- Qs : numpy.ndarray
- - concatenated evaluation function weight for states
-- R : numpy.ndarray
- - evaluation function weight for inputs
-- Rs : numpy.ndarray
- - concatenated evaluation function weight for inputs
-- pre_step : int
- - prediction step
-- state_size : int
- - state size of the plant
-- input_size : int
- - input size of the plant
-- dt_input_upper : numpy.ndarray, shape(input_size, ), optional
- - constraints of input dt, default is None
-- dt_input_lower : numpy.ndarray, shape(input_size, ), optional
- - constraints of input dt, default is None
-- input_upper : numpy.ndarray, shape(input_size, ), optional
- - constraints of input, default is None
-- input_lower : numpy.ndarray, shape(input_size, ), optional
- - constraints of input, default is None
-
-Methods:
-
-- initialize_controller() initialize the controller
-- calc_input(states, references) calculating optimal input
-
-More details, please look the **mpc_func_with_scipy.py** and **mpc_func_with_cvxopt.py**
-
-We have two function, mpc_func_with_cvxopt.py and mpc_func_with_scipy.py
-Both functions have same variable and member function. However the solver is different.
-Plese choose the right method for your environment.
-
-- example of import
-
-```py
-from mpc_func_with_scipy import MpcController as MpcController_scipy
-from mpc_func_with_cvxopt import MpcController as MpcController_cvxopt
-```
-
-# Examples
-## Problem Formulation
-
-** updating soon !!
-
-- first order system
-
-
-- ACC (Adaptive cruise control)
-
-
-
-## Expected Results
-
-- first order system
-
-
-- ACC (Adaptive cruise control)
-
-
-# Usage
-
-- for example(first order system)
-
-```
-$ python main_example.py
-```
-
-- for example(ACC (Adaptive cruise control))
-
-```
-$ python main_ACC.py
-```
-
-- for comparing two methods of optimization solvers
-
-```
-$ python test_compare_methods.py
-```
-
-# Requirement
-
-- python3.5 or more
-- numpy
-- matplotlib
-- cvxopt
-- scipy1.2.0 or more
-- python-control
-
-# Reference
-I`m sorry that main references are written in Japanese
-
-- モデル予測制御―制約のもとでの最適制御 著：Jan M. Maciejowski 訳：足立修一 東京電機大学出版局
\ No newline at end of file
diff --git a/mpc/basic/README.md b/mpc/basic/README.md
new file mode 100644
index 0000000..d101024
--- /dev/null
+++ b/mpc/basic/README.md
@@ -0,0 +1,144 @@
+# Model Predictive Control Basic Tool
+This program is about template, generic function of linear model predictive control
+
+# Documentation of the MPC function
+Linear model predicitive control should have state equation.
+So if you want to use this function, you should model the plant as state equation.
+Therefore, the parameters of this class are as following
+
+**class MpcController()**
+
+Attributes :
+
+- A : numpy.ndarray
+ - system matrix
+- B : numpy.ndarray
+ - input matrix
+- Q : numpy.ndarray
+ - evaluation function weight for states
+- Qs : numpy.ndarray
+ - concatenated evaluation function weight for states
+- R : numpy.ndarray
+ - evaluation function weight for inputs
+- Rs : numpy.ndarray
+ - concatenated evaluation function weight for inputs
+- pre_step : int
+ - prediction step
+- state_size : int
+ - state size of the plant
+- input_size : int
+ - input size of the plant
+- dt_input_upper : numpy.ndarray, shape(input_size, ), optional
+ - constraints of input dt, default is None
+- dt_input_lower : numpy.ndarray, shape(input_size, ), optional
+ - constraints of input dt, default is None
+- input_upper : numpy.ndarray, shape(input_size, ), optional
+ - constraints of input, default is None
+- input_lower : numpy.ndarray, shape(input_size, ), optional
+ - constraints of input, default is None
+
+Methods:
+
+- initialize_controller() initialize the controller
+- calc_input(states, references) calculating optimal input
+
+More details, please look the **mpc_func_with_scipy.py** and **mpc_func_with_cvxopt.py**
+
+We have two function, mpc_func_with_cvxopt.py and mpc_func_with_scipy.py
+Both functions have same variable and member function. However the solver is different.
+Plese choose the right method for your environment.
+
+- example of import
+
+```py
+from mpc_func_with_scipy import MpcController as MpcController_scipy
+from mpc_func_with_cvxopt import MpcController as MpcController_cvxopt
+```
+
+# Examples
+## Problem Formulation
+
+- **first order system**
+
+
+
+- **ACC (Adaptive cruise control)**
+
+The two wheeled model are expressed the following equation.
+
+&space;&&space;0&space;\\&space;\sin(\theta)&space;&&space;0&space;\\&space;0&space;&&space;1&space;\\&space;\end{bmatrix}&space;\begin{bmatrix}&space;u_v&space;\\&space;u_\omega&space;\\&space;\end{bmatrix}&space;=&space;\boldsymbol{B}\boldsymbol{U} "\frac{d}{dt} \boldsymbol{X}= \frac{d}{dt} \begin{bmatrix} x \\ y \\ \theta \end{bmatrix} = \begin{bmatrix} \cos(\theta) & 0 \\ \sin(\theta) & 0 \\ 0 & 1 \\ \end{bmatrix} \begin{bmatrix} u_v \\ u_\omega \\ \end{bmatrix} = \boldsymbol{B}\boldsymbol{U}")
+
+However, if we assume the velocity are constant, we can approximate the equation as following,
+
+
+
+then we can apply this model to linear mpc, we should give the model reference V although.
+
+- **evaluation function**
+
+the both examples have same evaluation function form as following equation.
+
+-\boldsymbol{r}(k+i|k)&space;||^2_{{\boldsymbol{Q}}(i)}&space;+&space;||\boldsymbol{\Delta&space;{U}}(k+i|k)||^2_{{\boldsymbol{R}}(i)} "J = \sum_{i = 0}^{prestep}||\boldsymbol{\hat{X}}(k+i|k)-\boldsymbol{r}(k+i|k) ||^2_{{\boldsymbol{Q}}(i)} + ||\boldsymbol{\Delta {U}}(k+i|k)||^2_{{\boldsymbol{R}}(i)}")
+
+- 
is predicit state by using predict input
+
+- 
is reference state
+
+- 
is predict amount of change of input
+
+- 
are evaluation function weights
+
+## Expected Results
+
+- first order system
+
+- time history
+
+
+
+- input
+
+
+
+- ACC (Adaptive cruise control)
+
+
+
+
+- animation
+
+
+
+# Usage
+
+- for example(first order system)
+
+```
+$ python main_example.py
+```
+
+- for example(ACC (Adaptive cruise control))
+
+```
+$ python main_ACC.py
+```
+
+- for comparing two methods of optimization solvers
+
+```
+$ python test_compare_methods.py
+```
+
+# Requirement
+
+- python3.5 or more
+- numpy
+- matplotlib
+- cvxopt
+- scipy1.2.0 or more
+- python-control
+
+# Reference
+I`m sorry that main references are written in Japanese
+
+- モデル予測制御―制約のもとでの最適制御 著：Jan M. Maciejowski 訳：足立修一 東京電機大学出版局
\ No newline at end of file
diff --git a/mpc/animation.py b/mpc/basic/animation.py
similarity index 99%
rename from mpc/animation.py
rename to mpc/basic/animation.py
index 2d7cd99..6ece541 100755
--- a/mpc/animation.py
+++ b/mpc/basic/animation.py
@@ -127,7 +127,7 @@ class AnimDrawer():
self._set_axis()
self._set_img()
- self.skip_num = 3
+ self.skip_num = 1
frame_num = int((len(self.history_xs[0])-1) / self.skip_num)
animation = ani.FuncAnimation(self.anim_fig, self._update_anim, interval=interval, frames=frame_num)
diff --git a/mpc/main_ACC.py b/mpc/basic/main_ACC.py
similarity index 99%
rename from mpc/main_ACC.py
rename to mpc/basic/main_ACC.py
index ffab36f..a868559 100644
--- a/mpc/main_ACC.py
+++ b/mpc/basic/main_ACC.py
@@ -218,7 +218,6 @@ def main():
theta_fig.legend()
time_history_fig.tight_layout()
- time_history_fig.legend()
traj_fig.plot(lead_history_states[:, 0], lead_history_states[:, 1], label="lead")
traj_fig.plot(follow_history_states[:, 0], follow_history_states[:, 1], label="follow")
diff --git a/mpc/main_example.py b/mpc/basic/main_example.py
similarity index 97%
rename from mpc/main_example.py
rename to mpc/basic/main_example.py
index 538d912..082693a 100644
--- a/mpc/main_example.py
+++ b/mpc/basic/main_example.py
@@ -85,7 +85,7 @@ class FirstOrderSystem():
def main():
dt = 0.05
- simulation_time = 100 # in seconds
+ simulation_time = 30 # in seconds
iteration_num = int(simulation_time / dt)
# you must be care about this matrix
@@ -116,9 +116,9 @@ def main():
Bd = sysd.B
# evaluation function weight
- Q = np.diag([1., 1., 10., 10.])
- R = np.diag([0.01, 0.01])
- pre_step = 5
+ Q = np.diag([1., 1., 1., 1.])
+ R = np.diag([1., 1.])
+ pre_step = 10
# make controller with discreted matrix
# please check the solver, if you want to use the scipy, set the MpcController_scipy
diff --git a/mpc/mpc_func_with_cvxopt.py b/mpc/basic/mpc_func_with_cvxopt.py
similarity index 100%
rename from mpc/mpc_func_with_cvxopt.py
rename to mpc/basic/mpc_func_with_cvxopt.py
diff --git a/mpc/mpc_func_with_scipy.py b/mpc/basic/mpc_func_with_scipy.py
similarity index 100%
rename from mpc/mpc_func_with_scipy.py
rename to mpc/basic/mpc_func_with_scipy.py
diff --git a/mpc/test_compare_methods.py b/mpc/basic/test_compare_methods.py
similarity index 100%
rename from mpc/test_compare_methods.py
rename to mpc/basic/test_compare_methods.py