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

On Vibration Suppression and Trajectory Tracking in Largely Uncertain Torsional System: An Error-based ADRC Approach

In this work, a practically relevant control problem of compensating harmonic uncertainties is tackled. The problem is formulated and solved here using an active disturbance rejection control (ADRC) methodology. A novel, custom ADRC structure is proposed that utilizes an innovative resonant extended state observer (RESO), dedicated to systems subjected to harmonic interferences. In order to make the introduced solution more industry-friendly, the entire observer-centered control topology is additionally restructured into one degree-of-freedom, compact, feedback error-based form (similar to ubiquitous in practice PID controller). Such reorganization enables a straightforward implementation and commission of the proposed technique in wide range of industrial control platforms, thus potentially increasing its outreach. In order to verify the efficiency of the introduced method, a multi-criteria experimental case study using a torsional plant is conducted in a trajectory tracking task, showing satisfactory performance in vibration suppression, without the often problem of noise amplification due to high observer/controller gains. Finally, a frequency analysis and a rigorous stability proof of the proposed control structure are given

A Nonlinear Extended State Observer for Rotor Position and Speed Estimation for Sensorless IPMSM Drives

© 1986-2012 IEEE. Sensorless machine drives in vehicle traction frequently experience rapidly-changing load disturbance and demand fast speed dynamics. Without gain-scheduling or compensation, conventional quadrature phase-locked-loop (Q-PLL) is unable to accurately estimate the rotor position and speed for these systems. In this paper, a third-order nonlinear extended state observer (TNESO) is proposed for position and speed estimation for sensorless interior permanent magnet synchronous motor drives. TNESO has the power of nonlinear feedback and takes the advantages of fast convergence and disturbance rejection. An optimized parameter configuration method is deployed to extend the disturbance observation bandwidth of the TNESO. Both steady state and transient performance of TNESO are verified through the experimental tests. In comparison with the performance of conventional Q-PLL scheme, the proposed observer is proved to be capable of delivering higher precision of position and speed estimation against rapidly varying disturbance in wide operating range

Semi-automatic liquid filling system using NodeMCU as an integrated Iot Learning tool

Computer programming and IoT are the key skills required in Industrial Revolution 4.0 (IR4.0). The industry demand is very high and therefore related students in this field should grasp adequate knowledge and skill in college or university prior to employment. However, learning technology related subject without applying it to an actual hardware can pose difficulty to relate the theoretical knowledge to problems in real application. It is proven that learning through hands-on activities is more effective and promotes deeper understanding of the subject matter (He et al. in Integrating Internet of Things (IoT) into STEM undergraduate education: Case study of a modern technology infused courseware for embedded system course. Erie, PA, USA, pp 1–9 (2016)). Thus, to fulfill the learning requirement, an integrated learning tool that combines learning of computer programming and IoT control for an industrial liquid filling system model is developed and tested. The integrated learning tool uses NodeMCU, Blynk app and smartphone to enable the IoT application. The system set-up is pre-designed for semi-automation liquid filling process to enhance hands-on learning experience but can be easily programmed for full automation. Overall, it is a user and cost friendly learning tool that can be developed by academic staff to aid learning of IoT and computer programming in related education levels and field