Control Improvement of Low-Cost Cast Aluminium Robotic Arm Using Arduino Based Computed Torque Control

Gravity causes non-linearity in position control of an articulated industrial robotic arm. Especially for a joint position control of a robot’s shoulder and elbow that works parallel with the gravity direction. To overcome the problem, Computed Torque Control algorithm was implemented. This algorithm linearized the feedback, so a regular linear Proportional Derivative controller can be implemented. The contribution of this research is to find an effective controller to control a heavy weight low-cost robotic arm link/body using low-cost controller such as Arduino. A Computed Torque Control was implemented to control the shoulder joint of an articulated robotic arm. This joint is the most affected joint by the gravity. It works along the vertical plane, and loaded by the rest of the arm and the robot’s load. The proposed controller was compared to a Proportional Integral Derivative (PID) Controller and a Cascade PID Controller. The experiment showed that the Computed Torque Controller can control the position of the arm properly both in the direction along or against the gravity. A linear PID controller could not bring the arm to the set point when it moves against the gravity, but it works well when the arm moves in the opposite direction. A Cascade PID controller has an overshot when the arm moves along the gravity. But it works properly when it moves up against the gravity. A Computed Torque Control works well in both directions even in the presence of gravity force because it includes the gravity on its algorithm

An Improved DC Motor Position Control Using Differential Evolution Based Structure Specified H∞ Robust Controller

Traditional synthesis of an H∞ controller usually results in a very high order of controller that is not practical for a low-cost embedded system such as a microcontroller. This paper presents a synthesis method of a low-order H∞ robust controller to control the position of a dc motor. The synthesis employed Differential Evolution optimization to find a controller that guarantees robust stability performance and robust stability against system perturbation. A second-order PID structure was chosen for the synthesized controller because this structure is simple and very famous. The proposed controller performance under uncertainties was compared to some other controllers. The first was compared with a conventional PID controller that had been finely tuned using the trial and error method in the nominal transfer function of the plant. Secondly, the proposed controller was compared with a full-order H∞ robust controller generated from a traditional synthesis method. Thirdly, the proposed controller was compared with another structure specified H∞ robust controller generated differently from the proposed method. All of the controllers result in a stable response. However, the proposed controller gives a better response in terms of overshoot and response time

Controlling a knee CPM machine using PID and iterative learning control algorithm

A conventional continuous passive motion (CPM) machine uses simple controller such as On/Off controller. Some better CPMs use PID controller. These kind of CPMs can not distinguish load different due to the different size of the patient leg. This may cause the CPM no longer follow the trajectory or the angle commands. Meanwhile, each patient may have different scenario of therapy from the others. When progress on the patient exists, the range of the flexion may be increased step by step. Therefore, the treatment can be different in term of the range of flexion from time to time. This paper proposes CPM with hybrid proportional integral derivative (PID) and iterative learning controller (ILC). The system has capability in learning the trajectory tracking. Therefore, the CPM will be able to follow any load or trajectory changes applied to it. The more accurate CPM machine can follow the trajectory command, the better its performance for the treatment. The experiment showed that the system was stable due to the PID controller. The tracking performance also improved with the ILC even there exist some disturbances

Joint control of a robotic arm using particle swarm optimization based H2/H∞ robust control on arduino

This paper proposes a small structure of robust controller to control robotic arm’s joints where exist some uncertainties and unmodelled dynamics. Robotic arm is widely used now in the era of Industry 4.0. Nevertheless, the cost for an industry to migrate from a conventional automatic machine to industrial robot still very high. This become a significant challenge to middle or small size industry. Development of a low cost industrial robotic arm can be one of good solutions for them. However, a low-cost manipulator can bring more uncertainties. There might be exist more unmodelled dynamic in a low-cost system. A good controller to overcome such uncertainties and unmodelled dynamics is robust controller. A low-cost robotic arm might use small or medium size embedded controller such as Arduino. Therefore, the control algorithm should be a small order of controller. The synthesized controller was tested using MATLAB and then implemented on the real hardware to control a robotic manipulator. Both the simulation and the experiment showed that the proposed controller performed satisfactory results. It can control the joint position to the desired position even in the presence of uncertainties such as unmodelled dynamics and variation of loads or manipulator poses

Control of robot-assisted gait trainer using hybrid proportional integral derivative and iterative learning control

An inexpensive exoskeleton of the lower limb was designed and developed in this study. It can be used as a gait trainer for persons with lower limb problems. It plays an essential role in lower limb rehabilitation and aid for patients, and it can help them improve their physical condition. This paper proposes a hybrid controller for regulating the lower limb exoskeleton of a robot-assisted gait trainer that uses a proportional integral and derivative (PID) controller combined with an iterative learning controller (ILC). The direct current motors at the hip and knee joints are controlled by a microcontroller that uses a preset pattern for the trajectories. It can learn how to monitor a trajectory. If the trajectory or load is changed, it will be able to follow the change. The experiment showed that the PID controller had the smallest overshoot, and settling time, and was responsible for system stability. Even if there are occasional interruptions, the tracking performance improves with the ILC

Wearable Activity Monitoring System for Detection of Estrus in A Sheep Farm

We have carried out the design of estrus detection tool for the large-scale farm of sheep. Estrus is a regular period of sexual receptivity in female mammals. Basically there are some ways to determine the time of estrus in cattle, which is based on body heat, bioimpedance or motion (activity). These tools have been used in some large-scale farms in developed countries. While in developing countries they are less popular because the price is quite expensive. In this study, a low cost system is designed based on the method of activity measurement using IMU sensor. Activities of sheep sensed by the IMU sensor will be processed by a microcontroller, then to be sent wirelessly to a remote monitor computer.

Robotic arm joint position control using iterative learning and mixed sensitivity H? robust controller

This paper proposes an improved control strategy of a robotic arm joint using hybrid controller consist of H∞ robust controller and iterative learning controller. The main advantage of this controller is the simple structure that made it possible to be implemented on a small embedded system for frugal innovation in industrial robotic arm development. Although it has a simple structure, it is a robust H∞ controller that has robust stability and robust performance. The iterative learning controller makes the trajectory tracking even better. To test the effectiveness of the proposed method, computer simulations using Matlab and hardware experiments were conducted. Variation of load was applied to both of the processes to present the uncertainties. The superiority of the proposed controller over the proportional integral derivative (PID) controller that usually being used in a low-cost robotic arm development is confirmed that it has better trajectory tracking. The error tracking along the slope of sinusoidal trajectory input was suppressed to zero. The biggest error along the trajectory that happened on every peak of the sinusoidal input, or when the direction is changed has been improved from 15 degrees to 4 degrees. This can be conceived that the proposed controller can be applied to control a low-cost robotic arm joint position which is applicable for small industries or educational purpose