Actuator and Sensor Placement for Multiobjective Control of Structures

Brown, Aaron S.; Ankireddi, Seshasayee; Yang, Henry T. Y.

doi:10.1061/(asce)0733-9445(1999)125:7(757)

Cited by 24 publications

(6 citation statements)

References 19 publications

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“…Brown et al . took a multi‐objective approach to define Pareto‐optimal curves for varying placements of active dampers and sensors on a five‐story frame using the results of many individual trials to obtain each trade‐off curve. Moita et al .…”

Section: Background Literature On Optimization Of Device and Sensor Lmentioning

confidence: 99%

Optimal placement of active control devices and sensors in frame structures using multi-objective genetic algorithms

Cha

Raich

Barroso

et al. 2011

Struct. Control Health Monit.

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Active control efficiency is highly dependent on the control algorithm and device types as well as the locations of the devices and sensors in a building. A gene manipulation, multi-objective genetic algorithm is proposed to optimize the placement of active devices and sensors in frame structures to reduce active control cost and increase the structural control strategy's effectiveness. Gene manipulation uses engineering judgment to modify the encoded variable information defining the number of devices and sensors per floor in selected Pareto-optimal front individuals. The proposed methodology evolves Pareto-optimal layouts that minimize the number of devices/sensors used while also minimizing the building interstory drift for a 20story steel-frame building under earthquake loading. The results indicate that the number and location of the devices and sensors in the layouts obtained strongly depends on the desired maximum drift. Also, the location of the sensors significantly impacts the efficiency of the active controller in reducing interstory drifts. In simulation trials, the proposed gene manipulation method obtained layouts that distributed devices and sensors more evenly over the building height than layouts obtained using standard multi-objective methods, resulting in greater control efficiency. The primary benefit of implementing the proposed gene manipulation was in reducing the number of multi-objective genetic algorithm generations required by up to 40% without negatively impacting the quality of Pareto-optimal device/sensor layout solutions obtained. rotational behavior of the structural system must be tackled. This section extends the developed GMGA to obtain optimal placements of control devices and sensors to a 3D problem and to compare the performance of the NSGA-II and SPEA2 approaches on this problem domain. In 2D, the majority of possible architectures are controllable and stable. When considering 3D control Figure 13. The average number of devices at each 3D building story direction using GMGA linked with NSGA-II (redundancy ratio = 0.3, GMR = 0.3).

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Section: Background Literature On Optimization Of Device and Sensor Lmentioning

confidence: 99%

Optimal placement of active control devices and sensors in frame structures using multi-objective genetic algorithms

Cha

Raich

Barroso

et al. 2011

Struct. Control Health Monit.

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“…In order to demonstrate the applicability and assess the effectiveness of the proposed control method, two shear beam models with three ͑Yang et al 1995; Ma andYang 2004͒ andfive stories ͑Yang et al 1993;Brown et al 1999͒ were chosen as numerical examples. For both models, the cost function was chosen to be the same as in Ma and Yang ͑2004͒.…”

Section: Illustrative Examplesmentioning

confidence: 99%

Sampled Data Feedback-Feedforward Control of Structures with Time Delays

Yang

2006

J. Struct. Eng.

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“…As an alternative, a guided random search method, the simulated annealing (SA) method, was employed to optimally place control devices in a structure (Liu et al, 1997). Brown et al (1999) used linear quadratic Gaussian (LQG) optimal control theory-based Pareto-optimal trade-off curves for various placements of actuators and sensors within civil structures. Xu and Teng (2002) developed an incremental algorithm for placement of active/passive control devices within the structure.…”

Section: Introductionmentioning

confidence: 99%

Cost-effective multi-objective optimal positioning of magnetorheological dampers and active actuators in large nonlinear structures

Askari

Samali

2016

Journal of Intelligent Material Systems and Structures

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The optimal number and location of control devices not only play a major role in an effective structural control system but also lead to a cost-effective design. This article presents a multi-objective optimization method based on a new genetic algorithm for simultaneous finding of the optimal number and placement of actuators and magnetorheological dampers, in active and semi-active vibration control of structures. The proposed strategy considers three objective functions to be minimized through optimization, including peak inter-storey drift ratio, peak acceleration and peak base shear force to make sure both human comfort and safety of the structure are guaranteed. Also, by choosing a pre-defined level of performance on dynamic responses of a structure, the designer can decide on decreasing or increasing the number of control devices in a systematic way and minimize the control cost. The approach is then validated through a nonlinear 20-storey benchmark problem. The results from active control system show how a problem that was initially solved with 25 actuators can be solved with less than a quarter of those actuators, having similar results in terms of aforementioned indices. The optimal distribution of different numbers of magnetorheological dampers in the same benchmark building is also studied in this article and compared to those obtained from actuators. Due to highly nonlinear behaviour of these devices, and also the complexity of the under-study benchmark structure, few reported researches have been conducted in this area. Also, the comparison between optimal places of active and semi-active control devices in the same structure has hitherto not been reported in the open literature.

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Actuator and Sensor Placement for Multiobjective Control of Structures

Cited by 24 publications

References 19 publications

Optimal placement of active control devices and sensors in frame structures using multi-objective genetic algorithms

Optimal placement of active control devices and sensors in frame structures using multi-objective genetic algorithms

Sampled Data Feedback-Feedforward Control of Structures with Time Delays

Cost-effective multi-objective optimal positioning of magnetorheological dampers and active actuators in large nonlinear structures

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