Active Disturbance Rejection Control for Robot Manipulator

Active Disturbance Rejection Control (ADRC) is a control methodology used in chemical processes, aircraft, motors, and other systems. This paper compares the results of an ADRC controller to a Proportional Integral Derivative controller (PID), applied to two degrees of freedom robots. A Linear Extended State Observer (LESO) is used to reconstruct the state variables and unknown parameters needed to control the position of each link. The ADRC can achieve the tracking position and estimate the velocity of each link. The results of the simulation program are shown

Improving the prediction accuracy of recurrent neural network by a PID controller.

International audienceIn maintenance field, prognostic is recognized as a key feature as the prediction of the remaining useful life of a system which allows avoiding inopportune maintenance spending. Assuming that it can be difficult to provide models for that purpose, artificial neural networks appear to be well suited. In this paper, an approach combining a Recurrent Radial Basis Function network (RRBF) and a proportional integral derivative controller (PID) is proposed in order to improve the accuracy of predictions. The PID controller attempts to correct the error between the real process variable and the neural network predictions

Combining a recurrent neural network and a PID controller for prognostic purpose.

International audienceIn maintenance field, prognostic is recognized as a key feature as the prediction of the remaining useful life of a system allows avoiding inopportune maintenance spending. Assuming that it can be difficult to provide models for that purpose, artificial neural networks appear to be well suited. In this paper, an approach combining a Recurrent Radial Basis Function network (RRBF) and a proportional integral derivative controller (PID) is proposed in order to improve the accuracy of predictions. The PID controller attempts to correct the error between the real process variable and the neural network predictions. The approach and its performances are illustrated by using two classical prediction benchmarks: the Mackey–Glass chaotic time series and the Box–Jenkins furnace data

Functions of fuzzy logic based controllers used in smart building

The main aim of this study is to support design and development processes of advanced fuzzy-logic-based controller for smart buildings e.g., heating, ventilation and air conditioning, heating, ventilation and air conditioning (HVAC) and indoor lighting control systems. Moreover, the proposed methodology can be used to assess systems energy and environmental performances, also compare energy usages of fuzzy control systems with the performances of conventional on/off and proportional integral derivative controller (PID). The main objective and purpose of using fuzzy-logic-based model and control is to precisely control indoor thermal comfort e.g., temperature, humidity, air quality, air velocity, thermal comfort, and energy balance. Moreover, this article present and highlight mathematical models of indoor temperature and humidity transfer matrix, uncertainties of users’ comfort preference set-points and a fuzzy algorithm

Pneumatic PID with Ultrasonic Distance Feedback

Indiana University Purdue University IndianapolisIndiana University–Purdue University Indianapolis (IUPUI) is initiating a new course to the Electrical & Computer Engineering Technology (ECET) Curriculum in the spring of 2019, this course is Advanced Process Controls. The lab curriculum for this course needed a functional application to demonstrate the use of a proportional–integral–derivative controller (PID). The lab location for this course has one important limitation, specifically no use of water; therefore, our design integrates the use of pneumatics. Using the lab’s existing Rockwell Automation PLC and software package, this design uses the PLC’s PID instruction to maintain an extension length on a pneumatic single acting cylinder. This closed control loop consists of the PLC and analog I/O card, an ultrasonic distance sensor, one pneumatic cylinder for the controlled variable, one pneumatic cylinder as a disturbance, and two Proportion-Air QB1X analog controlled pneumatic solenoids. The final design in summary, uses the ultrasonic sensor to provide feedback to the PID with the current extended length of the pneumatic cylinder. This establishes any error, and the properly tuned PID uses this feedback to respond accordingly to ensure the desired extension length of the cylinder is maintained.Electrical Engineering Technolog

Komparasi Performansi Antara Proportional Integral Derivative Controller (PID) Dan Fuzzy Logic Controller (FLC) Pada Penjejak Cahaya Dengan Tiga Sensor

The technology of light tracking monitors the solar panels to track the sun with full efficiency, and the solar panels can be upright to the sunlight in order to maximize the absorption of solar energy, so this system has a higher efficiency than non-tracking systems. This study aimed to obtain a controller that works accurately between the Proportional, Integral, and Derivative Controller (PID) and the Fuzzy Logic Controller (FLC) Algorithm by comparing the performance of the two algorithms in regulating the direction of the light tracker to detect the presence of sunlight. This solar prototype uses nine lamps as a simulation to determine the accuracy and precision of the angles of the two light trackers. The parameters compared in this test were the aspects of angular velocity and angle accuracy. The mean value of angular velocity obtained from the PID light tracking test results was 0.16 rad/s and the average linear velocity was 0.092 m/s whereas in the FLC light tracker, the average angular velocity value was 0.207 rad/s. Tests using a PID light tracker resulted in an X-axis accuracy of 45% and a Y-axis accuracy of 30%. The FLC light tracker, on the other hand, had an X-axis accuracy of 80% and a Y-axis accuracy of 30%.The precision value obtained by the PID light tracker on the X axis was 45% and the Y axis was 38%, while the precision value obtained by the FLC light tracker on the X axis was 71% and the Y axis was 33%. Based on the overall calculations, it can be concluded that the FLC light tracker has an increase in the speed value of 29% and an increase in the value of accuracy in the accuracy aspect by 35% and the precision aspect by 26% compared to the PID light tracker in previous studies

PID Controller Singularly Perturbing Impulsive Differential Equations and Optimal Control Problem

We study singular perturbation of impulsive system with a proportional-integral-derivative controller (PID controller) and solve an optimal control problem. The perturbation system comprises two important variables, a fast variable and a slow variable. Because of the complexity of the system, it is difficult to find its exact solution. This paper presents an approximation method for solving it. The aim of the approximation method is to reduce the complexity of the system by eliminating the fast variable. The solution of the method is expressed in an integral form, and it is called an approximated mild solution of the perturbed system. An example is provided to illustrate our result

Autonomous Energy Management system achieving piezoelectric energy harvesting in Wireless Sensors

International audienceWireless Sensor Networks (WSNs) are extensively used in monitoring applications such as humidity and temperature sensing in smart buildings, industrial automation, and predicting crop health. Sensor nodes are deployed in remote places to sense the data information from the environment and to transmit the sensing data to the Base Station (BS). When a sensor is drained of energy, it can no longer achieve its role without a substituted source of energy. However, limited energy in a sensor's battery prevents the long-term process in such applications. In addition, replacing the sensors' batteries and redeploying the sensors is very expensive in terms of time and budget. To overcome the energy limitation without changing the size of sensors, researchers have proposed the use of energy harvesting to reload the rechargeable battery by power. Therefore, efficient power management is required to increase the benefits of having additional environmental energy. This paper presents a new self-management of energy based on Proportional Integral Derivative controller (PID) to tune the energy harvesting and Microprocessor Controller Unit (MCU) to control the sensor modes

Using simple PID-inspired controllers for online resilient resource management of distributed scientific workflows

Scientific workflows have become mainstream for conducting large-scale scientific research. As a result, many workflow applications and Workflow Management Systems (WMSs) have been developed as part of the cyberinfrastructure to allow scientists to execute their applications seamlessly on a range of distributed platforms. Although the scientific community has addressed this challenge from both theoretical and practical approaches, failure prediction, detection, and recovery still raise many research questions. In this paper, we propose an approach inspired by the control theory developed as part of autonomic computing to predict failures before they happen, and mitigated them when possible. The proposed approach is inspired on the proportional–integral–derivative controller (PID controller) control loop mechanism, which is widely used in industrial control systems, where the controller will react to adjust its output to mitigate faults. PID controllers aim to detect the possibility of a non-steady state far enough in advance so that an action can be performed to prevent it from happening. To demonstrate the feasibility of the approach, we tackle two common execution faults of large scale data-intensive workflows—data storage overload and memory overflow. We developed a simulator, which implements and evaluates simple standalone PID-inspired controllers to autonomously manage data and memory usage of a data-intensive bioinformatics workflow that consumes/produces over 4.4 TB of data, and requires over 24 TB of memory to run all tasks concurrently. Experimental results obtained via simulation indicate that workflow executions may significantly benefit from the controller-inspired approach, in particular under online and unknown conditions. Simulation results show that nearly-optimal executions (slowdown of 1.01) can be attained when using our proposed method, and faults are detected and mitigated far in advance of their occurrence