Trajectory tracking control of a flexible micro-manipulator using neural networks

High precision positioning control of flexible micro-manipulators can be achieved by imitating aspects of human and animal behavior which involves the ability to learn and adapt to changes in environment. Artificial neural network modeling, which is a computational model for representing input/output relations, is an approach which can be applied in designing trained and self-learning motion control systems for micro-manipulators. A reference signal self-organizing control system using neural networks for flexible micro-manipulators is presented. The micro-manipulator is made of a bimorph piezo-electric high-polymer material (Poly Vinylidene Fluoride). This control system consists of both a plant with a feedback loop and a neural network with a feedforward loop. In this system, the neural network functions as the reference input filter and it organizes a new reference signal to the closed loop system. Numerical and experimental results show that the proposed control system is effective in tracking a reference signal.