Development of A Linear Delta Robot with Three Horizontal-Axial Pneumatic Actuators for 3-DOF Trajectory Tracking

Abstract

This paper focuses on developing a pneumatic-driven and horizontal-structure linear delta robot (PH-LDR) and increasing its trajectory tracking performance on the three-degrees-of-freedom (3-DOF) space. With the investigation of inverse and forward kinematics, the parallel mechanism of PH-LDR is designed by using three high-power and low-cost rod-less pneumatic actuators (PAs) to track 3-DOF motion, and this has a horizontal structure to enlarge the workspace. Since the PH-LDR features nonlinear coupling among its three axes and is disturbed by the three high-nonlinear rod-less PAs, the tracking control performance is significantly decreased, subject to uncertain nonlinearity and parametric uncertainty. Therefore, a fuzzy-PID controller is used to achieve highly accurate 3-DOF trajectory tracking, and furthermore, this study exploits neural networks (NNs) to pre-compensate the impacts arising from the compressibility of air and temperature change. The control system for the PH-LDR also features an embedded controller that allows real-time control. Experimental demonstration verifies the developed PH-LDR with the proposed controller, and the dynamic tracking accuracy in 3-DOF trajectory can be achieved.

Smooth velocity tracking and kinematic-based fuzzy control of carrying mobile robots

“…The velocity graph computed using the method in [2] has 453.16% maximum overshoot from the reference velocity, while this overshoot is 3.69% using the presented method. On the other hand, if the errors derivatives are entered to the fuzzy logic [7][8][9][10][11], velocity is tracked at the price of a larger number of enforced fuzzy rules. Compared to 2*20 rules employed in Table 5, 3*49 rules are designed in [8] and [11].…”

“…However, for a velocity tracking observation, the rate changes of posture errors are also entered to the fuzzy logic [7][8][9]. The same combination of errors and errors rate are exploited as fuzzy inputs in [10], where a fuzzy ring network method is used to control a multi-motor coordinated system and in [11], in which the trajectory tracking of a pneumatic driven linear delta robot is studied. Naturally, the fuzzy system complication expands by increasing the number of inputs.…”