add newton method
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Shunichi09
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@ -8,6 +8,7 @@ Now you can see the examples of control theories as following
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- **Iterative Linear Quadratic Regulator (iLQR)**
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- **Nonlinear Model Predictive Control (NMPC) with CGMRES**
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- **Nonlinear Model Predictive Control (NMPC) with Newton method**
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- **Linear Model Predictive Control (MPC)**(as generic function such as matlab tool box)
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- **Extended Linear Model Predictive Control for vehicle model**
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@ -1,7 +1,8 @@
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# CGMRES method of Nonlinear Model Predictive Control
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This program is about Continuous gmres method for NMPC
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Although usually we have to calculate the partial differential of optimal matrix, it could be really complicated.
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By using CGMRES, we can pass the calculating step and get the optimal input quickly.
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# Newton method of Nonlinear Model Predictive Control
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This program is about NMPC with newton method.
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Usually we have to calculate the partial differential of optimal matrix.
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In this program, in stead of using any paticular methods to calculate the partial differential of optimal matrix, I used numerical differentiation.
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Therefore, I believe that it easy to understand and extend your model.
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# Problem Formulation
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@ -13,39 +14,26 @@ By using CGMRES, we can pass the calculating step and get the optimal input quic
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- evaluation function
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To consider the constraints of input u, I introduced dummy input.
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<a href="https://www.codecogs.com/eqnedit.php?latex=J&space;=&space;\frac{1}{2}(x_1^2(t+T)+x_2^2(t+T))+\int_{t}^{t+T}\frac{1}{2}(x_1^2+x_2^2+u^2)-0.01vd\tau" target="_blank"><img src="https://latex.codecogs.com/gif.latex?J&space;=&space;\frac{1}{2}(x_1^2(t+T)+x_2^2(t+T))+\int_{t}^{t+T}\frac{1}{2}(x_1^2+x_2^2+u^2)-0.01vd\tau" title="J = \frac{1}{2}(x_1^2(t+T)+x_2^2(t+T))+\int_{t}^{t+T}\frac{1}{2}(x_1^2+x_2^2+u^2)-0.01vd\tau" /></a>
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- **two wheeled model**
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- model
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<a href="https://www.codecogs.com/eqnedit.php?latex=\frac{d}{dt}&space;\boldsymbol{X}=&space;\frac{d}{dt}&space;\begin{bmatrix}&space;x&space;\\&space;y&space;\\&space;\theta&space;\end{bmatrix}&space;=&space;\begin{bmatrix}&space;\cos(\theta)&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}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\frac{d}{dt}&space;\boldsymbol{X}=&space;\frac{d}{dt}&space;\begin{bmatrix}&space;x&space;\\&space;y&space;\\&space;\theta&space;\end{bmatrix}&space;=&space;\begin{bmatrix}&space;\cos(\theta)&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}" title="\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}" /></a>
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- evaluation function
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<a href="https://www.codecogs.com/eqnedit.php?latex=J&space;=&space;\boldsymbol{X}(t_0+T)^2&space;+&space;\int_{t_0}^{t_0&space;+&space;T}&space;\boldsymbol{U}(t)^2&space;-&space;0.01&space;dummy_{u_v}&space;-&space;dummy_{u_\omega}&space;dt" target="_blank"><img src="https://latex.codecogs.com/gif.latex?J&space;=&space;\boldsymbol{X}(t_0+T)^2&space;+&space;\int_{t_0}^{t_0&space;+&space;T}&space;\boldsymbol{U}(t)^2&space;-&space;0.01&space;dummy_{u_v}&space;-&space;dummy_{u_\omega}&space;dt" title="J = \boldsymbol{X}(t_0+T)^2 + \int_{t_0}^{t_0 + T} \boldsymbol{U}(t)^2 - 0.01 dummy_{u_v} - dummy_{u_\omega} dt" /></a>
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if you want to see more detail about this methods, you should go https://qiita.com/MENDY/items/4108190a579395053924.
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However, it is written in Japanese
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coming soon !
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# Expected Results
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- example
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![Figure_1.png]()
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you can confirm that the my method could consider the constraints of input.
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- two wheeled model
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- trajectory
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- time history
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coming soon !
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# Usage
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@ -57,9 +45,7 @@ $ python main_example.py
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- for two wheeled
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```
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$ python main_two_wheeled.py
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```
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coming soon !
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# Requirement
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@ -407,19 +407,13 @@ class NMPCControllerWithNewton():
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"""
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Attributes
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------------
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zeta : float
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gain of optimal answer stability
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tf : float
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predict time
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alpha : float
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gain of predict time
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N : int
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predicte step, discritize value
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threshold : float
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cgmres's threshold value
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input_num : int
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newton's threshold value
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NUM_INPUT : int
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system input length, this should include dummy u and constraint variables
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max_iteration : int
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MAX_ITERATION : int
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decide by the solved matrix size
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simulator : NMPCSimulatorSystem class
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us : list of float
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@ -444,11 +438,10 @@ class NMPCControllerWithNewton():
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None
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"""
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# parameters
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self.tf = 1. # 最終時間
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self.N = 10 # 分割数
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self.threshold = 0.0001 # break値
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self.N = 10 # time step
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self.threshold = 0.0001 # break
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self.NUM_INPUT = 3 # dummy, 制約条件に対するrawにも合わせた入力の数
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self.NUM_INPUT = 3 # u with dummy, and 制約条件に対するrawにも合わせた入力の数
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self.Jacobian_size = self.NUM_INPUT * self.N
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# newton parameters
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@ -492,11 +485,6 @@ class NMPCControllerWithNewton():
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# Newton method
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for i in range(self.MAX_ITERATION):
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# check
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# print("all_us = {}".format(all_us))
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# print("newton iteration in {}".format(i))
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# input()
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# calc all state
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x_1s, x_2s, lam_1s, lam_2s = self.simulator.calc_predict_and_adjoint_state(x_1, x_2, self.us, self.N, dt)
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@ -504,8 +492,6 @@ class NMPCControllerWithNewton():
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F_hat = self._calc_f(x_1s, x_2s, lam_1s, lam_2s, all_us, self.N, dt)
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# judge
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# print("F_hat = {}".format(F_hat))
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# print(np.linalg.norm(F_hat))
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if np.linalg.norm(F_hat) < self.threshold:
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# print("break!!")
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break
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@ -531,9 +517,6 @@ class NMPCControllerWithNewton():
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F = self._calc_f(x_1s, x_2s, lam_1s, lam_2s, all_us, self.N, dt)
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# print("check val of F = {0}".format(np.linalg.norm(F)))
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# input()
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# for save
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self.history_f.append(np.linalg.norm(F))
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self.history_u.append(self.us[0])
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@ -641,7 +624,4 @@ def main():
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if __name__ == "__main__":
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main()
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main()
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