Analysing Various Control Techniques for Manipulator Robotic System (Robogymnast)

The Robogymnast is a highly complex,three-link system based on the triple-inverted pendulum and is modelled on the human example of a gymnast suspended by their hands from the high bar and executing larger and larger upswings to eventually rotate fully.The links of the Robogymnast correspond respectively to the arms,trunk,and lower limbs of the gymnast,and from its three joints,one is under passive operation,while the remaining two are powered.The passive top joint poses severe challenges in attaining the smooth movement control needed to operate the Robogymnast effectively.This study assesses four types of controllers used for systems operation and identifies how far response stabilisation is achieved with each.The system is simulated using MATLAB Simulink,with findings generated regarding rising and settling time,as well as overshoot.The research primarily seeks to exam-ine the application of a linear quadratic regulator controller,proportional-integral-derivative controller,fuzzy linear quadratic regulator controller and linear quadratic regulator-proportional-integral-derivative controller for this type of system and comparisons between the different controllers to demon-strate successful performance,which highlights the claimed advantages of the proposed system.

Multi-objective Invasive Weed Optimization of the LQR Controller

The Robogymnast is a triple link underactuated pendulum that mimics a human gymnast hanging from a horizontal bar.In this paper, two multi-objective optimization methods are developed using invasive weed optimization（IWO）. The first method is the weighted criteria method IWO（WCMIWO） and the second method is the fuzzy logic IWO hybrid（FLIWOH）. The two optimization methods were used to investigate the optimum diagonal values for the Q matrix of the linear quadratic regulator（LQR） controller that can balance the Robogymnast in an upright configuration. Two LQR controllers were first developed using the parameters obtained from the two optimization methods. The same process was then repeated, but this time with disturbance applied to the Robogymnast states to develop another set of two LQR controllers. The response of the controllers was then tested in different scenarios using simulation and their performance evaluated. The results show that all four controllers are able to balance the Robogymnast with varying accuracies. It has also been observed that the controllers trained with disturbance achieve faster settling time.