Trajectory Control of Electro Hydraulic Excavator using Fuzzy Self Tuning Algorithm with Neural Network

This paper presents the trajectory control of a 2DOF mini electro-hydraulic excavator by using fuzzy self tuning with neural network algorithm. First, the mathematical model is derived for the 2DOF mini electro-hydraulic excavator. The fuzzy PID and fuzzy self tuning with neural network are designed for circle trajectory following. Its two links are driven by an electric motor controlled pump system. The experimental results demonstrated that the proposed controllers have better control performance than the conventional controller.

A dynamical neural network for hitting an approaching object

Besides making contact with an approaching ball at the proper place and time, hitting requires control of the effector velocity at contact. A dynamical neural network for the planning of hitting movements was derived in order to account for both these requirements. The model in question implements continuous required velocity control by extending the Vector Integration To Endpoint model while providing explicit control of effector velocity at interception. It was shown that the planned movement trajectories generated by the model agreed qualitatively with the kinematics of hitting movements as observed in two recent experiments. Outstanding features of this comparison concerned the timing and amplitude of the empirical backswing movements, which were largely consistent with the predictions from the model. Several theoretical implications as well as the informational basis and possible neural underpinnings of the model were discussed.