Generalized Linear Quadratic Gaussian Techniques for the Wind Benchmark Problem

Pham, Khanh; Jin, Gang; Sain, M.K.; Spencer, B. F.; Liberty, S.R.

doi:10.1061/(asce)0733-9399(2004)130:4(466)

Cited by 13 publications

(9 citation statements)

References 4 publications

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“…The EBP algorithm is implemented on an SATMD with variable damping device, and its efficacy is evaluated by implementing the SATMD for the response control of a 76‐story wind‐excited benchmark building. The performances of various other control strategies involving SATMDs and ATMDs have previously been investigated and reported by researchers for the benchmark building (Battaini, Casciati, & Faravelli, ; Deshmukh & Chandiramani, ; Kang et al., ; Kim, Yun, & Spencer, ; Mei, Kareem, & Kantor, ; Peng, Gu, & Niemann, ; Pham, Jin, Sain, Spencer, & Liberty, ; Varadarajan & Nagarajaiah, ; Wang, ; Wang & Adeli, ; Wu & Yang, ; Yang et al., ; Yang, Lin, & Jabbari, ). The fundamental time period of the uncontrolled 76‐story building is 6.25 s. The mass of the SATMD used in this investigation is 500 t, which is similar to what is used by previously proposed strategies.…”

Section: Numerical Studymentioning

confidence: 99%

“…(); balanced LQG (BLQG) controller and the extension of BLQG using second‐cost‐cumulant (2CC) generalization of the popular LQG proposed by Pham et al. (); linear quadratic Gaussian‐alpha (LQG‐ α ) control strategy developed by Wang (); modified sliding mode control (MSMC) scheme proposed by Wu and Yang (); energy‐to‐peak based (E‐PB) control and peak‐to‐peak based (P‐PB) strategies proposed by Yang et al (); and filtered sliding mode control (Filtered SMC) proposed by Wang and Adeli ().…”

Section: Numerical Studymentioning

confidence: 99%

“…Notable among the previously proposed methods, the linear quadratic Gaussian (LQG) strategy is presented by Yang et al (2004a) as a sample strategy for the response control of the benchmark building using ATMD. The other active control strategies discussed here include the general fuzzy controller (FC)-based strategy proposed by Battaini et al (2004); sliding mode fuzzy control (SMFC) proposed by Kim et al (2004); model predictive control scheme, without constraints (MPC 1 ) and with constraints (MPC 2 ), proposed by Mei et al (2004); sinusoidal reference strategy (SRS)-based adaptive feed-forward vibration control strategy proposed by Peng et al (2004); balanced LQG (BLQG) controller and the extension of BLQG using secondcost-cumulant (2CC) generalization of the popular LQG proposed by Pham et al (2004); linear quadratic Gaussianalpha (LQG-) control strategy developed by Wang (2004); modified sliding mode control (MSMC) scheme proposed by Wu and Yang (2004); energy-to-peak based (E-PB) control and peak-to-peak based (P-PB) strategies proposed by Yang et al (2004b); and filtered sliding mode control (Filtered SMC) proposed by Wang and Adeli (2015).…”

Section: The Effectiveness Of the Ebp Algorithm For Wind Response Conmentioning

confidence: 99%

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Semi‐active algorithm for energy‐based predictive structural control using tuned mass dampers

Zelleke

Matsagar

2019

Computer aided Civil Eng

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A novel semi‐active control algorithm is developed and numerically evaluated for the suppression of undesirable structural vibrations. The mechanical energy of the vibrating structure is considered as the primary variable influencing the control action. This intuitive strategy is proposed to realize improved control of structural vibrations. The numerical study conducted reveals that the proposed energy‐based predictive (EBP) algorithm can be implemented on vibration control applications. The energy imparted to the structure is also reduced due to the proposed algorithm. The influence of the parameters of the proposed semi‐active tuned mass damper is studied. Further, the application of the proposed strategy on a realistic structure is numerically demonstrated by implementing the algorithm for the wind response control of a 76‐story benchmark building. The results show that the EBP algorithm is a competitive semi‐active strategy. The robustness of the strategy is also evaluated considering uncertainties in the properties of the benchmark building.

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Section: Numerical Studymentioning

confidence: 99%

Section: Numerical Studymentioning

confidence: 99%

Section: The Effectiveness Of the Ebp Algorithm For Wind Response Conmentioning

confidence: 99%

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Semi‐active algorithm for energy‐based predictive structural control using tuned mass dampers

Zelleke

Matsagar

2019

Computer aided Civil Eng

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“…Control of buildings subject to seismic and wind disturbances has been a useful application for cumulant control methods. These methods have been used on several structural benchmarks, for example see, [3,4,[7][8][9][10][11][12] with some success. The structures are lightly damped, which helps make them interesting applications when a stochastic-like disturbance, such as an earthquake, enters the system.…”

Section: Application To Linear Quadratic Systemsmentioning

confidence: 99%

Multi-cumulant, multi-objective structural control: a circuit analog

Diersing

Sain

2008

Analog Integr Circ Sig Process

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Cost cumulant control has been gaining momentum in recent years and has proven to be competitive with other controllers when applied to various structural control problems. These control methods have generalized the well-known LQG control. Similarly, in this paper cost cumulants are used to generalize the H 2 =H 1 control problem. Initially the development will be done for a class of nonlinear systems. The treatment will then consider the case when the system is linear and the costs are quadratic. The multi-cumulant, multiobjective control strategy, as well as the worst case disturbance, will be found through a Nash game approach. Uncertainties in the stiffness and damping coefficients will be taken into account and this control strategy will be applied to a four story building subject to a seismic disturbance. Also, a connection of the building problem to circuits will be given.

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“…They have mainly been applied to control techniques with encouraging results [5], [6], [8]. Recently, cumulants have been applied to solve stochastic games [2], [7].…”

Section: Introductionmentioning

confidence: 99%

Nash and Minimax Bi-Cumulant Games

Diersing

Sain

2006

Proceedings of the 45th IEEE Conference on Decision and Control

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Cumulants have been gaining interest in control lately. This paper applies cumulants to the area of game theory and gives sufficient conditions for nonzero and zero sum games to a class of nonlinear systems. With a linear system and quadratic costs, Nash and minimax equilibrium solutions are found. The games consider the finite time horizon, state feedback problem. Special cases of these methods reduce to the H 2/H∞ and H∞ control problems, so that a generalization is achieved.

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Generalized Linear Quadratic Gaussian Techniques for the Wind Benchmark Problem

Cited by 13 publications

References 4 publications

Semi‐active algorithm for energy‐based predictive structural control using tuned mass dampers

Semi‐active algorithm for energy‐based predictive structural control using tuned mass dampers

Multi-cumulant, multi-objective structural control: a circuit analog

Nash and Minimax Bi-Cumulant Games

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