A Comparison of Control Strategies for Disruption Management in Engineering Design for Resilience

2018 ◽  
Author(s):  
Jiaxin Wu ◽  
Pingfeng Wang
Keyword(s):  
Dynamic System ◽  
Engineering Design ◽  
Control Strategies ◽  
System Modeling ◽  
Effective Control ◽  
Disruption Management ◽  
System Control ◽  
Dynamic System Modeling ◽  
Failure Restoration ◽  
Different Characteristics

Managing potential disruptive events at the operating phase of an engineered system therefore improving the system’s failure resilience is an importance yet challenging task in engineering design. The resilience of an engineered system can be improved by enhancing the failure restoration capability of the system with appropriate system control strategies. Therefore, control-guided failure restoration is an essential step in engineering design for resilience. Considering different characteristics of disruptive events and their impacts to the performance of a system, effective control strategies for the failure restoration must be selected correspondingly. However, the challenge is to develop generally applicable guiding principles for selecting effective control strategies thus implementing the control-guided failure restorations. In this paper, a comparison of three commonly used control strategies for dynamic system control is conducted with the focus on the effectiveness of restoring system performance after the system has undergone different major disruptive events. A case study of an electricity transmission system is used to demonstrate the dynamic system modeling and the comparison of three control strategies for disruption management.

A Comparison of Control Strategies for Disruption Management in Engineering Design for Resilience

2019 ◽  
Vol 5 (2) ◽  
Author(s):  
Jiaxin Wu ◽  
Pingfeng Wang
Keyword(s):  
Dynamic System ◽  
Engineering Design ◽  
Control Strategies ◽  
System Modeling ◽  
Effective Control ◽  
Disruption Management ◽  
System Control ◽  
Dynamic System Modeling ◽  
Failure Restoration ◽  
Different Characteristics

Managing potential disruptive events at the operating phase of an engineered system therefore improving the system's failure resilience is an importance yet challenging task in engineering design. The resilience of an engineered system can be improved by enhancing the failure restoration capability of the system with appropriate system control strategies. Therefore, control-guided failure restoration is an essential step in engineering design for resilience. Considering different characteristics of disruptive events and their impacts to the performance of a system, effective control strategies for the failure restoration must be selected correspondingly. However, the challenge is to develop generally applicable guiding principles for selecting effective control strategies, thus implementing the control-guided failure restorations. In this paper, a comparison of three commonly used control strategies for dynamic system control is conducted with the focus on the effectiveness of restoring system performance after the system has undergone different major disruptive events. A case study of an electricity transmission system is used to demonstrate the dynamic system modeling and the comparison of three control strategies for disruption management.

PERTURBATION PARAMETERS TUNING OF MULTI-OBJECTIVE OPTIMIZATION DIFFERENTIAL EVOLUTION AND ITS APPLICATION TO DYNAMIC SYSTEM MODELING

2015 ◽  
Vol 75 (11) ◽  
Author(s):  
Mohd Zakimi Zakaria ◽  
Hishamuddin Jamaluddin ◽  
Robiah Ahmad ◽  
Azmi Harun ◽  
Radhwan Hussin ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Dynamic System ◽  
Dynamic Systems ◽  
System Modeling ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Structure Selection ◽  
Dynamic System Modeling ◽  
Perturbation Parameters ◽  
Model Structure Selection

This paper presents perturbation parameters for tuning of multi-objective optimization differential evolution and its application to dynamic system modeling. The perturbation of the proposed algorithm was composed of crossover and mutation operators.  Initially, a set of parameter values was tuned vigorously by executing multiple runs of algorithm for each proposed parameter variation. A set of values for crossover and mutation rates were proposed in executing the algorithm for model structure selection in dynamic system modeling. The model structure selection was one of the procedures in the system identification technique. Most researchers focused on the problem in selecting the parsimony model as the best represented the dynamic systems. Therefore, this problem needed two objective functions to overcome it, i.e. minimum predictive error and model complexity.  One of the main problems in identification of dynamic systems is to select the minimal model from the huge possible models that need to be considered. Hence, the important concepts in selecting good and adequate model used in the proposed algorithm were elaborated, including the implementation of the algorithm for modeling dynamic systems. Besides, the results showed that multi-objective optimization differential evolution performed better with tuned perturbation parameters.

scholarly journals Dynamic system modeling of thermally-integrated concentrated PV-electrolysis

2021 ◽  
Vol 46 (18) ◽  
pp. 10666-10681
Author(s):  
Isaac Holmes-Gentle ◽  
Saurabh Tembhurne ◽  
Clemens Suter ◽  
Sophia Haussener
Keyword(s):  
Dynamic System ◽  
System Modeling ◽  
Dynamic System Modeling

Dynamic System Modeling Based on Recurrent Neural Network

2021 ◽  
Author(s):  
Wenjie Cao ◽  
Cheng Zhang ◽  
Zhenzhen Xiong ◽  
Ting Wang ◽  
Junchao Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Dynamic System ◽  
Recurrent Neural Network ◽  
System Modeling ◽  
Dynamic System Modeling

A localized adaptive soft sensor for dynamic system modeling

2014 ◽  
Vol 111 ◽  
pp. 350-363 ◽  
Author(s):  
Wangdong Ni ◽  
Steven D. Brown ◽  
Ruilin Man
Keyword(s):  
Dynamic System ◽  
System Modeling ◽  
Soft Sensor ◽  
Dynamic System Modeling

Post-Disruption Performance Recovery to Enhance Resilience of Interconnected Network Systems

2019 ◽  
Author(s):  
Jiaxin Wu ◽  
Pingfeng Wang
Keyword(s):  
Distribution System ◽  
Disruption Management ◽  
Network Systems ◽  
Analysis Process ◽  
Recovery Strategies ◽  
Performance Recovery ◽  
Different Types ◽  
Failure Restoration ◽  
Different Characteristics ◽  
Disruption Recovery

Abstract Mitigating the effect of potential disruptive events at the operating phase of an engineered system therefore improving the system’s failure resilience is an importance yet challenging task in system operation. For complex networked system, different stakeholders complicate the analysis process by introducing different characteristics, such as different types of material flow, storage, response time, and flexibility. With different types of systems, the resilience can be improved by enhancing the failure restoration capability of the systems with appropriate performance recovery strategies. These methods include but not limit to, rerouting paths, optimal repair sequence and distributed resource centers. Considering different characteristics of disruptive events, effective recovery strategies for the failure restoration must be selected correspondingly. However, the challenge is to develop a generally applicable framework to optimally coordinate different recovery strategies and thus lead to desirable failure restoration performances. This paper presents a post-disruption recovery decision-making framework for networked systems, to help decision-makers optimize recovery strategies, in which the overall recovery task is formulated as an optimization problem to achieve maximum resilience. A case study of an electricity distribution system is used to demonstrate the feasibility of the developed framework and the comparison of several recovery strategies for disruption management.

Wave-Based Control of a Nonlinear Flexible System

2006 ◽  
Author(s):  
William J. O’Connor ◽  
Francisco Ramos ◽  
Vicente Feliu
Keyword(s):  
Control Strategies ◽  
System Modeling ◽  
Nonlinear Behavior ◽  
Effective Control ◽  
Space Structures ◽  
Large Space ◽  
Flexible System ◽  
Strongly Nonlinear ◽  
Mechanical Waves ◽  
Actuator System

The motivation for this work is the control of flexible mechanical systems, such as long, light robot arms, gantry cranes, and large space structures, with an actuator at one end and a free boundary at the other. Very effective control strategies have recently been developed which are based on interpreting the actuator motion as launching mechanical “waves” (propagating motion) into the flexible system while absorbing returning “waves”. These control systems are robust to system changes and to actuator limitations. They are generic, require very little system modeling, need only local sensing, and are computationally light and easy to implement. In a flexible arm, when elastic deflections are large, frequently there is strongly nonlinear behavior. This paper investigates how such nonlinearities affect the wave-based control strategy. In summary, the news is good. It is found that errors arise only when trajectories are very demanding, and even then the errors are small. Some strategies for correcting these errors are explained: addition of a linear element at the actuator-system interface, error correction by second manoeuver, and redefinition of the waves in a less-than-optimal way. The paper presents these ideas and illustrates them with numerical simulations.

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