Deep regression of convolutional neural network applied to resolved acceleration control for a robot manipulator

This paper presents a modified resolved acceleration control scheme based on deep regression of the convolutional neural network. The resolved acceleration control scheme can achieve precise motion control of robot manipulators by regulating the accelerations of the end-effector, and the conventional scheme needs the position and orientation of the end-effector, which are obtained through the direct kinematics of the robot manipulator. This scheme increases the computational loads and might obtain inaccurate position and orientation due to mechanical errors. To overcome the drawbacks, a camera is used to capture the images of the robot manipulator, and then a deep regression of convolutional neural network is imposed into the resolved acceleration control to obtain the position and orientation of the end-effector. The proposed approach aims to enhance the positioning accuracy, to reduce the computational loads, and to facilitate the deep regression in real-time control. In this study, the proposed approach is applied to a 3-DOF planar parallel robot manipulator, and the results are compared with those by the conventional resolved acceleration control and a visual servo-based control. The results show that those objectives are achieved. Furthermore, the robustness of the proposed approach is tested through only the partial image of the end-effector available, and the proposed approach still works functionally and effectively.

Control Simulations for a Stewart Platform Compensator Mounted on Moving Base

In this paper, utilizing the analytical equations of motion for a base-moving Stewart platform, we design an active wave compensation system for a surgery table installed on the top plate of a Stewart platform in a ship. In our medical application, the base plate of a Stewart platform moves with the motion of the ship. For a base-moving Stewart platform, we presented analytical equations of motion in matrix form in the paper: IMECE2018-87253. The objective of the platform is to compensate the pitching, rolling, and heaving motions of the ship (with respect to an inertial coordinate system). As control methods for the nonlinear system, we employ a hybrid controller combining resolved acceleration control with H∞ control, and integral sliding mode control (ISMC). The ISMC with input time delay is also designed with a state predictor, which includes a ship motion predictor utilizing an autoregressive model. Finally, to assess the control performance and robustness for the system with uncertainties, numerical simulations are presented. In addition, the simulation results of the predictor based ISMC for the system with input time delay are illustrated showing the effectiveness of the controller.

A simulation and experimental study on wheeled mobile robot path control in road roundabout environment

A robust control algorithm for tracking a wheeled mobile robot navigating in a pre-planned path while passing through the road’s roundabout environment is presented in this article. The proposed control algorithm is derived from both the kinematic and dynamic modelling of a non-holonomic wheeled mobile robot that is driven by a differential drive system. The road’s roundabout is represented in a grid map and the path of the mobile robot is determined using a novel approach, the so-called laser simulator technique within the roundabout environment according to the respective road rules. The main control scheme is experimented in both simulation and experimental study using the resolved-acceleration control and active force control strategy to enable the robot to strictly follow the predefined path in the presence of disturbances. A fusion of the resolved-acceleration control–active force control controller with Kalman Filter has been used empirically in real time to control the wheeled mobile robot in the road’s roundabout setting with the specific purpose of eliminating the noises. Both the simulation and the experimental results show the capability of the proposed controller to track the robot in the predefined path robustly and cancel the effect of the disturbances.