Control of a Head Stabilization System for Use in Robotic Disaster Response

Author(s):  
Bijo Sebastian ◽  
Adam Williams ◽  
Pinhas Ben-Tzvi

This paper systematically describes the design and validation of a feasible control scheme for a robotic head stabilization system. Over the past few decades there has been a growing need for robotic systems to perform human rescue operations in the event of natural or manmade disasters. Before autonomous or remotely controlled robotic victim extraction can be realized, support systems with the capability to secure the head of a trauma victim in a manner that does not exacerbate existing spinal injuries needs to be developed. The paper starts with a brief description of one such previously developed robotic head stabilization system and examines the various functional requirements from a design and control standpoint. Detailed dynamic analysis of the system is done based on which a force control scheme involving Series Elastic Actuators (SEA) is proposed. The proposed control scheme is then tested on an ADAMS-MATLAB co-simulation where the dynamic head support system is modelled in ADAMS and the force controller in Simulink. Based on the results of the simulation, a physical prototype is integrated and the proposed control scheme is validated through experiments. The results of the simulation and experiment are analyzed, and improvements to the system are proposed for future experimentation. Based on the results of the simulation and experiments, the proposed control was found to successfully meet the desired control metrics in providing accurate force control for the head support device. The paper ends with a discussion on possible modifications to the overall system for it to be used in field robotic rescue.

2021 ◽  
pp. 106815
Author(s):  
Tao Zhang ◽  
Chengchao Li ◽  
Dongying Ma ◽  
Xiaodong Wang ◽  
Chaoyong Li

Author(s):  
Reyhane Mokhtarname ◽  
Ali Akbar Safavi ◽  
Leonhard Urbas ◽  
Fabienne Salimi ◽  
Mohammad M Zerafat ◽  
...  

Dynamic model development and control of an existing operating industrial continuous bulk free radical styrene polymerization process are carried out to evaluate the performance of auto-refrigerated CSTRs (continuous stirred tank reactors). One of the most difficult tasks in polymerization processes is to control the high viscosity reactor contents and heat removal. In this study, temperature control of an auto-refrigerated CSTR is carried out using an alternative control scheme which makes use of a vacuum system connected to the condenser and has not been addressed in the literature (i.e. to the best of our knowledge). The developed model is then verified using some experimental data of the real operating plant. To show the heat removal potential of this control scheme, a common control strategy used in some previous studies is also simulated. Simulation results show a faster dynamics and superior performance of the first control scheme which is already implemented in our operating plant. Besides, a nonlinear model predictive control (NMPC) is developed for the polymerization process under study to provide a better temperature control while satisfying the input/output and the heat exchanger capacity constraints on the heat removal. Then, a comparison has been also made with the conventional proportional-integral (PI) controller utilizing some common tuning rules. Some robustness and stability analyses of the control schemes investigated are also provided through some simulations. Simulation results clearly show the superiority of the NMPC strategy from all aspects.


2007 ◽  
Vol 31 (1) ◽  
pp. 127-141
Author(s):  
Yonghong Tan ◽  
Xinlong Zhao

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.


2021 ◽  
Vol 11 (12) ◽  
pp. 5330
Author(s):  
Gisela Pujol-Vázquez ◽  
Alessandro N. Vargas ◽  
Saleh Mobayen ◽  
Leonardo Acho

This paper describes how to construct a low-cost magnetic levitation system (MagLev). The MagLev has been intensively used in engineering education, allowing instructors and students to learn through hands-on experiences of essential concepts, such as electronics, electromagnetism, and control systems. Built from scratch, the MagLev depends only on simple, low-cost components readily available on the market. In addition to showing how to construct the MagLev, this paper presents a semi-active control strategy that seems novel when applied to the MagLev. Experiments performed in the laboratory provide comparisons of the proposed control scheme with the classical PID control. The corresponding real-time experiments illustrate both the effectiveness of the approach and the potential of the MagLev for education.


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