No abstract

Flexible manipulators are associated with merits such as low power consumption, use of small actuators, high-speed, and their low cost due to fewer materials’ requirements than their rigid counterparts. However, they suffer from link vibration which hinder the aforementioned merits from being realized. The limitations of link vibrations are time wastage, poor precision, and the possibility of failure due to vibration fatigue. This paper extends the vibration control mathematical foundation from a single link manipulator to a three-dimensional, two links flexible manipulator. The vibration control theory developed earlier feeds back a fraction of the link root strain to increase the system damping, thereby reducing the strain. This extension is supported by experimental results. Further improvements are proposed by tuning the right proportion of root strain to feed back, and the timing using artificial neural networks. The algorithm was implemented online in matlab interfaced with dSPACE for practical experiments. From the practical experiment done in consideration of a variable load, neural network tuned gains exhibited a better performance over those obtained using fixed feedback gains in terms of damping of both torsional and bending vibrations and tracking of joint angles.

This article sought to address issues related to human-robot cooperation tasks focusing especially on robotic operation using bio-signals. In particular, we propose to develop a control scheme for a robot arm based on electromyography (EMG) signal that allows a cooperative task between humans and robots that would enable teleoperations. A basic framework for achieving the task and conducting EMG signals analysis of the motion of upper limb muscles for mapping the hand motion is presented. The objective of this work is to investigate the application of a wearable EMG device to control a robot arm in real-time. Three EMG sensors are attached to the brachioradialis, biceps brachii, and anterior deltoid muscles as targeted muscles. Three motions were conducted by moving the arm about the elbow joint, shoulder joint, and a combination of the two joints giving a two degree of freedom. Five subjects were used for the experiments. The results indicated that the performance of the system had an overall accuracy varying from 50% to 100% for the three motions for all subjects. This study has further shown that upper-limb motion discrimination can be used to control the robotic manipulator arm with its simplicity and low computational cost.

Flexible manipulators have numerous advantages such as lightweight, high operation speed, and low power consumption. However, they suffer from link vibrations, especially when operated at high speeds followed by sudden stops. This limitation has been addressed using techniques such as adaptive filters, adaptive strain feedback gain, state feedback control, etc. This article presents a filtered inverse controller for the mitigation of link vibrations in a multilink flexible manipulator. To this end, the plant model, developed and linearized in Maple/Maplesim was inverted in MATLAB. The internal dynamics of the inverse model were stabilized using the state feedback technique. For safe and high-speed operations, the inverse model was augmented with a low pass filter to form the filtered inverse which was used as feedforward controller. Practical experiments were carried out in the dSPACE environment. Results show that filtered inverse controller yield not only faster response but relatively minimal link vibration when compared with the manipulator without vibration controller.