Algorithm 1 DDPG-ID Algorithm.

1:Randomly initialize online Q network with weights $w^Q$   2:Randomly initialize online policy network with weights $w^{\mu }$   3:Initialize the target Q network by $w^{Q^{\prime }}\leftarrow w^Q$   4:Initialize the target policy network by $w^{{\mu }^{\prime }}\leftarrow w^{\mu }$   5:Initialize the experience replay buffer $\Psi$   6:Load the simplified micropositioner dynamic model   7:for episode = 1, MaxEpisode do   8:    Initialize a noise process $\mathcal{N}$ for exploration   9:    Initialize ASMDO and ID compensator 10:    Randomly initialize micropositioner states 11:    Receive initial observation state $s_1$ 12:    for step = 1, T do 13:       Select action $a_t={\pi }_O\left(s_t\right)+{\widehat{D}}_t+{\mathcal{N}}_t$ 14:       Use $a_t$ to run micropositioner system model 15:       Process errors with integral differential compensator 16:       Receive reward $r_t$ and new state $s_{t+1}$ 17:       Store transition $(s_t,a_t,r_t,s_{t+1})$ in replay buffer $\Psi$ 18:       Randomly sample a minibatch of M transitions $(s_j,a_j,r_j,s_{j+1})$ from $\Psi$ 19:       Set $Q_T=r_j+\gamma Q_T^{\prime }(s_{j+1},{\pi }_T(s_{j+1},w^{{\mu }^{\prime }}),w^{Q^{\prime }})$ 20:       Minimize loss: $\mathcal{L}(w^Q)=\frac{1}{M}{\sum }_{j=1}^M{(Q_T-Q_O(s_j,a_j,w^Q))}^2$ to update online Q network 21:       Use the sampled policy gradient to update online policy network:        ${\nabla }_{w^{\mu }}J=\frac{1}{M}{\sum }_j^M({\nabla }_{a_j}{Q}_O(s_j,a_j,w^Q){\nabla }_{w^{\mu }}{\pi }_O\left(s_j,w^{\mu }\right))$ 22:       Update the target networks: $w^{Q^{\prime }}\leftarrow \tau w^Q+(1-\tau )w^{Q^{\prime }},w^{{\mu }^{\prime }}\leftarrow \tau w^{\mu }+(1-\tau )w^{{\mu }^{\prime }}$ 23:  end for 24:end for