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.

The control of wave energy converters using active bipolar damping

A novel method for controlling wave energy converters using active bipolar damping is described and compared with current control methods. The performance of active bipolar damping is modelled numerically for two distinct types of wave energy converter and it is found that in both cases the power capture can be significantly increased relative to optimal linear damping. It is shown that this is because active bipolar damping has the potential for providing a quasi-spring or quasi-inertia, which improves the wave energy converter's tuning and amplitude of motion, resulting in the increase in power capture observed. The practical implementation of active bipolar damping is also discussed. It is noted that active bipolar damping does not require a reactive energy store and thereby reduces the cost and eliminates losses due to the cycling of reactive energy. It is also noted that active bipolar damping could be implemented using a single constant pressure double-acting hydraulic cylinder and so potentially represents a simple, efficient, robust and economic solution to the control of wave energy converters.