Design of H∞ Position Tracking Controllers for Current-Fed Induction Motors Used in Articulated Mechanical Loads

2002 ◽  
Vol 124 (3) ◽  
pp. 485-491
Author(s):  
Stefano Chiaverini ◽  
Giuseppe Fusco
Keyword(s):  
Induction Motors ◽  
Robot Manipulator ◽  
Tracking Errors ◽  
Rotor Resistance ◽  
Control Scheme ◽  
Tracking Controllers ◽  
Riccati Matrix Equation ◽  
Set Up ◽  
Riccati Matrix

This paper describes a systematic procedure to design H∞ position and flux-norm tracking controllers for current-fed induction motors. The designed controllers achieve convergence to zero of both position and flux-norm tracking errors while ensuring robustness with respect to unknown load torque disturbances. The proposed procedure offers the possibility of a simple development of the controllers’ design; in particular, it does not require a numerical solution of the Riccati matrix equation. A case study has been set up which considers the application of the proposed control scheme to a two-link robot manipulator system actuated by two induction motors. Numerical simulation results confirm the validity of the proposed design methodology, even in the presence of rotor resistance uncertainty.

Robust control of robot manipulators with torque saturation using fuzzy logic

Robotica ◽  
2001 ◽  
Vol 19 (6) ◽  
pp. 631-639 ◽  
Author(s):  
Hyeung-Sik Choi
Keyword(s):  
Fuzzy Logic ◽  
Robust Control ◽  
Trajectory Tracking ◽  
Robot Manipulator ◽  
Robot Manipulators ◽  
Uncertain System ◽  
Tracking Errors ◽  
Estimation Errors ◽  
System Parameters ◽  
Control Scheme

Robot manipulators, which are nonlinear structures and have uncertain system parameters, are complex dynamically when operated in an unknown environment. To compensate for estimate errors of the uncertain system parameters and to accomplish the desired trajectory tracking, nonlinear robust controllers are appropriate. However, when estimation errors or tracking errors are large, they require large input torques, which may not be satisfied due to torque limits of actuators such as driving motors. As a result, their stability cannot be guaranteed. In this paper, a new robust control scheme is presented to solve stability problems and to achieve fast trajectory tracking of uncertain robot manipulators in the presence of torque limits. By using fuzzy logic, new desired trajectories which can be reduced are generated based on the initial desired trajectory, and torques of the robust controller are regulated so as to not exceed torque limits. Numerical examples are shown to validate the proposed controller using an uncertain two degree-of-freedom underwater robot manipulator.

scholarly journals Visual Tracking Control of Cable-Driven Hyper-Redundant Snake-Like Manipulator

2021 ◽  
Vol 11 (13) ◽  
pp. 6224
Author(s):  
Qisong Zhou ◽  
Jianzhong Tang ◽  
Yong Nie ◽  
Zheng Chen ◽  
Long Qin
Keyword(s):  
Visual Tracking ◽  
Tracking Control ◽  
Control Method ◽  
Adaptive Optimization ◽  
Tracking Errors ◽  
Kinematics Model ◽  
Compound Control ◽  
Original Intention ◽  
Control Scheme ◽  
Specific Geometry

The cable-driven hyper-redundant snake-like manipulator (CHSM) inspired by the biomimetic structure of vertebrate muscles and tendons, which consists of numerous joint units connected adjacently driven by elastic materials with hyper-redundant DOF, performs flexible kinematic skills and competitive compound capability under complicated working circumstances. Nevertheless, the drawback of lacking the ability to perceive the environment to perform intelligently in complex scenarios leaves a lot to be improved, which is the original intention to introduce visual tracking feedback acting as an instructor. In this paper, a cable-driven snake-like robotic arm combined with a visual tracking technique is introduced. A visual tracking approach based on dual correlation filter is designed to guide the CHSM in detecting the target and tracing after its trajectory. Specifically, it contains an adaptive optimization for the scale variation of the tracking target via pyramid sampling. For the CHSM, an explicit kinematics model is derived from its specific geometry relationships and followed by a simplification for the inverse kinematics based on some assumption or limitation. A control scheme is brought up to combine the kinematics with visual tracking via the processing tracking errors. The experimental results with a practical prototype validate the availability of the proposed compound control method with the derived kinematics model.

Adaptive attitude-tracking control of spacecraft considering on-orbit refuelling

2020 ◽  
pp. 014233122097313
Author(s):  
Yiqi Xu
Keyword(s):  
Tracking Control ◽  
Closed Loop ◽  
Closed Loop System ◽  
Tracking Errors ◽  
Attitude Tracking ◽  
Control Scheme ◽  
Inertia Model ◽  
And Performance ◽  
Attitude Tracking Control ◽  
The Stability

This paper studies the attitude-tracking control problem of spacecraft considering on-orbit refuelling. A time-varying inertia model is developed for spacecraft on-orbit refuelling, which actually includes two processes: fuel in the transfer pipe and fuel in the tank. Based upon the inertia model, an adaptive attitude-tracking controller is derived to guarantee the stability of the resulted closed-loop system, as well as asymptotic convergence of the attitude-tracking errors, despite performing refuelling operations. Finally, numerical simulations illustrate the effectiveness and performance of the proposed control scheme.

Reducing Culture Reporting Errors in the Microbiology Laboratory

2019 ◽  
Vol 152 (Supplement_1) ◽  
pp. S131-S132
Author(s):  
Kathryn Hogan ◽  
Beena Umar ◽  
Mohamed Alhamar ◽  
Kathleen Callahan ◽  
Linoj Samuel
Keyword(s):  
Real Time ◽  
Error Rate ◽  
Reporting System ◽  
Error Rates ◽  
Tracking Errors ◽  
Corrective Measures ◽  
Safety Awareness ◽  
Before And After ◽  
Set Up ◽  
System Errors

Abstract Objectives There are few papers that characterize types of errors in microbiology laboratories and scant research demonstrating the effects of interventions on microbiology lab errors. This study aims to categorize types of culture reporting errors found in microbiology labs and to document the error rates before and after interventions designed to reduce errors and improve overall laboratory quality. Methods To improve documentation of error incidence, a self-reporting system was changed to an automatic reporting system. Errors were categorized into five types Gram stain (misinterpretations), identification (incorrect analysis), set up labeling (incorrect patient labels), procedures (not followed), and miscellaneous. Error rates were tracked according to technologist, and technologists were given real-time feedback by a manager. Error rates were also monitored in the daily quality meeting and frequently detected errors were discussed at staff meetings. Technologists attended a year-end review with a manager to improve their performance. To maintain these changes, policies were developed to monitor technologist error rate and to define corrective measures. If a certain number of errors per month was reached, technologists were required to undergo retraining by a manager. If a technologist failed to correct any error according to protocol, they were also potentially subject to corrective measures. Results In 2013, we recorded 0.5 errors per 1,000 tests. By 2018, we recorded only 0.1 errors per 1,000 tests, an 80% decrease. The yearly culture volume from 2013 to 2018 increased by 32%, while the yearly error rate went from 0.05% per year to 0.01% per year, a statistically significant decrease (P = .0007). Conclusion This study supports the effectiveness of the changes implemented to decrease errors in culture reporting. By tracking errors in real time and using a standardized process that involved timely follow-up, technologists were educated on error prevention. This practice increased safety awareness in our micro lab.

scholarly journals Towards Zero Emissions Noosa

2019 ◽  
Vol 11 (17) ◽  
pp. 4679
Author(s):  
Carina Anderson ◽  
Robert Passey ◽  
Jeremy De Valck ◽  
Rakibuzzaman Shah
Keyword(s):  
Community Engagement ◽  
Focal Point ◽  
Contextual Information ◽  
Renewable Electricity ◽  
Community Group ◽  
Course Of Action ◽  
Engagement Process ◽  
Set Up ◽  
External Consultant

This paper reports on a case study of the community group Zero Emissions Noosa, whose goal is for 100% renewable electricity in the Noosa Shire (Queensland, Australia) by 2026. Described within this paper are the processes used by Zero Emissions Noosa to set up their zero emissions plan, involving community engagement and the use of an external consultant. The external consultant was employed to produce a detailed report outlining how to successfully achieve zero emissions from electricity in the Noosa Shire by 2026. This paper explains how and why the community engagement process used to produce the report was just as important as the outcomes of the report itself. Modeling was undertaken, and both detailed and contextual information was provided. Inclusion of the community in developing the scenario parameters for the modeling had a number of benefits including establishing the context within which their actions would occur and focusing their efforts on options that were technically feasible, financially viable and within their capabilities to implement. This provided a focal point for the community in calling meetings and contacting stakeholders. Rather than prescribing a particular course of action, it also resulted in a toolbox of options, a range of possible solutions that is flexible enough to fit into whatever actions are preferred by the community. The approach and outcomes discussed in this paper should, therefore, be useful to other communities with similar carbon emission reduction goals.

NEURAL MODELING AND CONTROL OF DYNAMIC SYSTEMS WITH HYSTERESIS

2007 ◽  
Vol 31 (1) ◽  
pp. 127-141
Author(s):  
Yonghong Tan ◽  
Xinlong Zhao
Keyword(s):  
Dynamic Systems ◽  
Control Strategy ◽  
Neural Modeling ◽  
Modeling And Control ◽  
Control Scheme ◽  
The Neural Networks ◽  
Adaptive Control Strategy ◽  
Set Up ◽  
Modeling Strategy ◽  
And Control

A hysteretic operator is proposed to set up an expanded input space so as to transform the multi-valued mapping of hysteresis to a one-to-one mapping so that the neural networks can be applied to model of the behavior of hysteresis. Based on the proposed neural modeling strategy for hysteresis, a pseudo control scheme is developed to handle the control of nonlinear dynamic systems with hysteresis. A neural estimator is constructed to predict the system residual so that it avoids constructing the inverse model of hysteresis. Thus, the control strategy can be used for the case where the output of hysteresis is unmeasurable directly. Then, the corresponding adaptive control strategy is presented. The application of the novel modeling approach to hysteresis in a piezoelectric actuator is illustrated. Then a numerical example of using the proposed control strategy for a nonlinear system with hysteresis is presented.

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