Soft computing based signal processing approaches for supporting modeling and control of engineering systems - a case study

Author(s):  
Terez A. Varkonyi
2011 ◽  
Vol 403-408 ◽  
pp. 3758-3762
Author(s):  
Subhajit Patra ◽  
Prabirkumar Saha

In this paper, two efficient control algorithms are discussed viz., Linear Quadratic Regulator (LQR) and Dynamic Matrix Controller (DMC) and their applicability has been demonstrated through case study with a complex interacting process viz., a laboratory based four tank liquid storage system. The process has Two Input Two Output (TITO) structure and is available for experimental study. A mathematical model of the process has been developed using first principles. Model parameters have been estimated through the experimentation results. The performance of the controllers (LQR and DMC) has been compared to that of industrially more accepted PID controller.


2015 ◽  
Vol 137 (1) ◽  
Author(s):  
Renato Galluzzi ◽  
Andrea Tonoli ◽  
Nicola Amati

The implementation of variable damping systems to increase the adaptability of mechanical structures to their working environment has been gaining increasing scientific interest, and numerous attempts have been devoted to address vibration control by means of active and semi-active devices. Although research results seem promising in some cases, the proposed solutions are often not able to fulfill requirements in terms of compactness and simplicity on one hand, and dynamic performance on the other. In this context, the present paper discusses the modeling and control of an electrohydrostatic actuation (EHA) system for its implementation as a damping device. A model of the device is proposed and analyzed for design purposes. Subsequently, a damping control strategy is presented. Finally, a case study introduces and validates an EHA prototype for helicopter rotor blade lead–lag damping.


1987 ◽  
Vol 20 (5) ◽  
pp. 223-229
Author(s):  
P.B. Luh ◽  
Yu-Chi Ho ◽  
Ying-Ping Zheng ◽  
Jong-Ming Wu ◽  
Qing-Yu Wang ◽  
...  

2019 ◽  
Vol 141 (10) ◽  
Author(s):  
Daniel R. Herber ◽  
James T. Allison

Here we describe a problem class with combined architecture, plant, and control design for dynamic engineering systems. The design problem class is characterized by architectures comprised of linear physical elements and nested co-design optimization problems employing linear-quadratic dynamic optimization. The select problem class leverages a number of existing theory and tools and is particularly effective due to the symbiosis between labeled graph representations of architectures, dynamic models constructed from linear physical elements, linear-quadratic dynamic optimization, and the nested co-design solution strategy. A vehicle suspension case study is investigated and a specifically constructed architecture, plant, and control design problem is described. The result was the automated generation and co-design problem evaluation of 4374 unique suspension architectures. The results demonstrate that changes to the vehicle suspension architecture can result in improved performance, but at the cost of increased mechanical complexity. Furthermore, the case study highlights a number of challenges associated with finding solutions to the considered class of design problems. One such challenge is the requirement to use simplified design problem elements/models; thus, the goal of these early-stage studies are to identify new architectures that are worth investigating more deeply. The results of higher-fidelity studies on a subset of high-performance architectures can then be used to select a final system architecture. In many aspects, the described problem class is the simplest case applicable to graph-representable, dynamic engineering systems.


Author(s):  
Sulaiman F. Alyaqout ◽  
Panos Y. Papalambros ◽  
A. Galip Ulsoy

System performance can significantly benefit from optimally integrating the design and control of engineering systems. To improve the robustness properties of systems, the present article introduces an approach that combines robust design with robust control and investigates the coupling between them. However, the computational cost of improving this robustness can often be high due to the need to solve a resulting minimax design and control optimization problem. To reduce this cost, sequential and iterative strategies are proposed and compared to an all-in-one strategy for solving the minimax problem. These strategies are then illustrated for a case-study: Robust design and robust control of a DC motor. Results show that the resulting strategies can improve the robustness properties of the DC motor. In addition, the coupling strength between robust design and robust control tends to increase as the applied level of uncertainty increases.


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