This paper presents online neural control approach for nonlinear mechanical systems with random friction nature. We construct neural auxiliary control to compensate a control error in online for overcoming friction effect which reduces control performance in real-time implementation. Friction dynamics is estimated by using online least square(LS) method, which is utilized for online learning of the neural network. We accomplish computer simulation for evaluating the proposed control approach comparing offline control method to demonstrate its superiority.