new_contents: #!/usr/bin/env python3
  """
  My favorite myopic (not MPC, DP, or RL) control-law when absolutely nothing is known about your system except
  that the control is additive and fully-actuated:
  ```
  dx/dt = f(x,t) + u         # drift f unknown, state x at time t known, choose control u to make x=r
  u = W.dot(x) + b           # policy is affine function of state
  dW/dt = outer(k*(r-x), x)  # parameters are adapted (learned online) to oppose the...
  db/dt = k*(r-x)            # ... gradient of the error-energy-rate d/dt((k/2)*(r-x)^2)
  ```
  Try this with any crazy f.
  It's basically PID but with the PD gains evolving according to the regression-like dW/dt I gave.
  PID with stationary PD gains fails when the f is reasonably nonlinear. This law still works.
  Of course, additive-control fully-actuated systems pretty much only model lame low-level problems, but still neat.
  
  """
  # Dependencies
  import numpy as np
  from matplotlib import pyplot
  
  ##################################################
  
  # Controller
  class C:
      def __init__(self, n, k):
          self.n = int(n)
          self.k = float(k)
          self.W = np.zeros((n, n), dtype=float)
          self.b = np.zeros(n, dtype=float)
      def u(self, r, x, dt):
          ked = self.k*(r - x)*dt
          self.W += np.outer(ked, x)
          self.b += ked
          return self.W.dot(x) + self.b
  
  ##################################################
  
  # Drift dynamic
  n = 3
  def f(x, t):
      return np.array([10.0*(x[1] - x[0]),
                       x[0]*(28.0 - x[2]) - x[1],
                       x[0]*x[1] - 2.6*x[2]])
  
  ##################################################
  
  # Time
  dt = 0.001
  T = np.arange(0.0, 10.0, dt)
  
  # State
  X = np.zeros((len(T), n), dtype=float)
  X[0] = [-1.0, 2.0, 3.0]
  
  # Control
  U = np.zeros((len(T), n), dtype=float)
  c = C(n, 5.0)
  
  # Reference
  R = np.array([[6.0, 7.0, -7.0]] * len(T))
  
  ##################################################
  
  # Simulation
  control = True
  for i, t in enumerate(T[1:]):
      if control: U[i] = c.u(R[i], X[i], dt)
      dxdt = f(X[i], t) + U[i]
      X[i+1] = X[i] + dxdt*dt
  
  ##################################################
  
  # Plot
  fig = pyplot.figure()
  if control: fig.suptitle("Controlled Response", fontsize=26)
  else: fig.suptitle("Natural Response", fontsize=26)
  ax = None
  for i in range(n):
      ax = fig.add_subplot(n, 1, i+1, sharex=ax)
      ax.plot(T, X[:, i], color='b', linewidth=2, label="state")
      ax.plot(T, R[:, i], color='g', linewidth=3, linestyle=':', label="desire")
      ax.plot(T[:-1], U[:-1, i], color='r', linewidth=0.5, label="action", scaley=False)
      ax.set_xlim([T[0], T[-1]])
      ax.set_ylabel("state "+str(i), fontsize=20)
      ax.grid(True)
  ax.set_xlabel("time", fontsize=20)
  ax.legend()
  pyplot.show()
  
subject: Add aadapt.py