Reinforcement adaptive learning neural network based friction compensation for high speed and precision

There is an increasing number of applications in high precision motion control systems in manufacturing, i.e., ultra-precision machining, assembly of small components and micro devices. It is very difficult to assure such accuracy due to many factors affecting the precision of motion, such as friction and backlash in the drive system. The standard PID (proportional-integral-derivative) type servo control algorithms are not capable of delivering the desired precision under the influence of friction and backlash. In the paper, the friction and the disturbance are identified by a neural network. The weight adaptation rule, defined as a reinforcement adaptive-learning rule, is derived from the Lyapunov stability theory. Therefore the proposed scheme can be applicable to a wide class of mechanical systems. The simulation results on 1-DOF mechanical system verify the effectiveness of the proposed algorithm