The work is devoted to automatic solving the control object stabilization system synthesis problem at the point of state space. To solve this problem machine learning by symbolic regression is used. Main goal of stabilization system consists of direction the control object to given point in state space independent on its initial state in some given area. Usually at application of symbolic regression one terminal state, a set of initial states and a quality criterion are given, that includes sum values of the criterion estimations of achievement by control object of the terminal state from each initial state. The sum of criterion values hides the individual properties of each motion path from some initial state to the terminal state. Some trajectories don’t reach the terminal state or reaches the terminal state on complex path. Unlike the previous approach in this work, the shape of the trajectory of the control object to the terminal state is determined in advance. Initially, the optimal control problem is repeatedly solved for each given initial state according to speed criteria and/or minimum path length. Then, at the second stage, the symbolic regression method searches for one control function as a function of the object state vector that provides an approximation of all previously obtained optimal trajectories. This approach was named supervised machine learning, because the set of optimal trajectories is a training sample.