controller.py 文件源码

def __init__(self, kp, kd, kv, kw, N, u_limit=[100, 100], nn_limit=[100, 100]):
        """
        kp:  Proportional feedback gains.
        kd:  Derivative feedback gains.
        kv:  Input-side learning gain (scalar).
        kw:  Output-side learning gain (scalar).
        N:   Number of neurons.
        u_limit: Limit on total output effort.
        nn_limit: Limit on NN component feedforward effort.

        The user must actively set self.learn = True to
        have the NN start learning.

        """
        self.nstates = 2 * len(kp)
        self.ncontrols = len(kp)
        self.nsigs = N

        self.sig = lambda x: np.concatenate(([1], np.tanh(x)))
        self.sigp = lambda x: np.tile(1/(np.cosh(x)**2), (self.nsigs+1, 1))

        self.set_gains(kp, kd, kv, kw)
        self.u_limit = np.array(u_limit, dtype=np.float32)
        self.nn_limit = np.array(nn_limit, dtype=np.float32)

        self.V = np.zeros((self.nstates+1, self.nsigs))
        self.W = np.zeros((self.nsigs+1, self.ncontrols))
        self.y = np.zeros(self.ncontrols)

        self.saturated = False
        self.learn = False

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