Research Article  Open Access
Francisco PalaciosQuiñonero, Josep RubióMassegú, Josep M. Rossell, Hamid Reza Karimi, "DiscreteTime Multioverlapping Controller Design for Structural Vibration Control of Tall Buildings under Seismic Excitation", Mathematical Problems in Engineering, vol. 2012, Article ID 636878, 20 pages, 2012. https://doi.org/10.1155/2012/636878
DiscreteTime Multioverlapping Controller Design for Structural Vibration Control of Tall Buildings under Seismic Excitation
Abstract
In this paper, a computationally effective strategy to obtain multioverlapping controllers via the Inclusion Principle is applied to design discretetime statefeedback multioverlapping LQR controllers for seismic protection of tall buildings. To compute the corresponding control actions, the proposed semidecentralized controllers only require state information from neighboring stories. This particular configuration of information exchange allows introducing a dramatic reduction in the transmission range required for a wireless implementation of the communication system. To investigate the behavior of the proposed semidecentralized multioverlapping controllers, a proper simulation model has been designed. This model includes semiactive actuation devices with limited force capacity, control sampling times consistent with the communication latency, timedelayed state information, and communication failures. The performance of the proposed multioverlapping controllers has been assessed through numerical simulations of the seismic response of a 20story building with positive results.
1. Introduction
Over the last decades, problems of ever increasing complexity have been considered in the field of Structural Vibration Control (SVC). Current SVC systems for seismic protection of tall buildings can involve a large number of sensors and actuation devices and a wide and sophisticated communication network [1–3]. Semidecentralized control strategies, which can operate using only state information from neighboring stories, are especially relevant for wireless implementations of the communication system. Semidecentralized statefeedback LQR controllers were proposed by Wang and Lynch in [4], and the study was extended to statefeedback controllers by Wang et al. in [5]. The numerical and experimental results obtained in these works clearly indicate that the proposed semidecentralized control strategies are specially suitable for SVC of tall buildings with wireless communications. It should be highlighted, however, that important computational difficulties can arise when applying these control design strategies to large buildings. The LQR controller design presented in [4] uses a variant of the heuristic iterative procedure proposed by Lunze in [6], and the controller design in [5] is based on a Linear Matrix Inequality formulation (LMI). For largedimensional problems, a great computational effort is required by the iterative procedure used in the LQR design. Analogously, solving largedimensional convex optimization problems with LMI constraints is also a costly computational task.
In this context, the design of semidecentralized controllers using multioverlapping decompositions based on the Inclusion Principle (IP) is a very interesting option [7–12]. Broadly speaking, the IP allows decomposing the original largedimensional problem into a set of lowdimensional decoupled problems. This decomposition takes advantage of the particular structure of the original system and can help to significantly reduce the computational effort. Examples of successful applications of the IP to SVC can be found in [13–15]. Recently, an effective computational strategy to design semidecentralized multioverlapping controllers based on a sequential application of the IP was presented by PalaciosQuiñonero et al. in [16]. In that work, semidecentralized multioverlapping LQR controllers are designed for seismic protection of a fourstory building with positive results. However, it has to be noted that all these applications of the IP to SVC have been conducted using small buildings, continuoustime models, and assuming highly idealized conditions, such as active force actuators with unrestricted force capacity and communication systems with no failures nor delays.
The main contribution of the present paper is to present a largescale application of the IP to the design of semidecentralized controllers for SVC, paying special attention to some aspects of practical relevance. More specifically, the computational strategy proposed in [16] is applied to design discretetime statefeedback multioverlapping LQR controllers to mitigate the seismic response of a 20story building. Moreover, to gain a meaningful insight into the behavior of the proposed multioverlapping controllers, the models used in the numerical simulations include some factors of practical relevance such as control sampling rates, realistic implementation of the control actions, timedelayed state information, and communication latency and failures. One of the main difficulties encountered when applying the IP to discretetime controller design is that the natural structure of the continuoustime model is lost in the discretization process. To overcome this difficulty, the discretization process has been carried out on the expanded decoupled subsystems. The results obtained in the numerical simulations confirm the excellent characteristics of the proposed semidecentralized multioverlapping controllers for SVC of large buildings.
The organization of the paper is as follows: In Section 2, a detailed derivation of the continuoustime statespace model for an story building is presented. Section 3 begins with a summary discussion on the design of discretetime statefeedback centralized LQR controllers. Next, the main ideas involved in the design of discretetime statefeedback multioverlapping LQR controllers are briefly presented. In Section 4, three mathematical models used to conduct the numerical simulations of the building seismic response are presented: (i) Basic building model, which consists in a discretetime approximation of the continuoustime statespace model with a small sampling time and no control action. (ii) Centralized control model, which implements the discretetime centralized LQR controller with perfect state knowledge, small sampling time, and ideal semiactive force actuators with limited force capacity. (iii) Multioverlapping control model. This case implements a discretetime multioverlapping controller considering semiactive actuation devices with limited force capacity, a control sampling time consistent with the communication latency, timedelayed state information, and communication failures. Finally, in Section 5, the control design methodology presented in Section 3 and the simulation models introduced in Section 4 are applied to a particular 20story building to assess the performance of the proposed multioverlapping controllers.
2. ContinuousTime Building Model
Let us consider the story building schematically displayed in Figure 1, which is modeled as a lumpedmass planar system with displacements in the direction of the ground motion. The building motion can be described by the secondorder differential equation: where is the vector of story displacements with respect to the ground, and represents the displacement of the th story. , , are the mass, damping, and stiffness matrices, respectively. The vector of control actions is where represents the control force exerted by the actuation device (see Figure 2), and is the control location matrix. The seismic ground acceleration is , and denotes the disturbance input matrix. The mass and stiffness matrices have the following structures: where and represent, respectively, the mass and stiffness of the th story. When the values of the story damping coefficients , , are known, a damping matrix with the same structure as can be obtained by replacing by in (2.5). Alternatively, a tridiagonal damping matrix in the form can be computed following the Rayleigh damping approach by setting the damping ratio values for two selected natural frequencies [17]. For the actuation system schematically depicted in Figure 2, the control location matrix has dimensions and the following structure: where denotes the element in the th row and th column of . Finally, the disturbance input matrix is where is a column vector of dimension with all its entries equal to 1.
Now, we take the state vector and derive a firstorder statespace model The state, control, and disturbance input matrices are, respectively, where represents a zero matrix of dimensions , and is the identity matrix of dimension . Next, we consider a new state vector which groups together the interstory drifts and interstory velocities in increasing order Using the change of basis defined by the matrix we obtain the new state space model: where In this work, we restrict our attention to the interstory drifts as output variables. The output vector can then be obtained as where is a matrix of dimensions with the following structure:
3. Control Design
To design a discretetime centralized statefeedback LQR controller for the story building model presented in the previous section, we begin by considering the continuoustime system: obtained from (2.16) by removing the disturbance term . The discretetime system corresponding to the zerohold approximation of (3.1) with sampling time is where Next, we consider the discretetime statefeedback controller: and the quadratic index: where is a symmetric positive semidefinite matrix, and is a symmetric positive definite matrix. The control gain matrix that minimizes (3.5) under constraints (3.2) and (3.4) can be computed as where is the solution of the discretetime Riccati equation:
To design a multioverlapping controller that is able to compute the control actions using only state information corresponding to neighboring stories, we consider the story building decomposed into a sequence of twostory overlapped subsystems where represents the th story. This overlapping decomposition is schematically depicted in Figure 3. Following the sequential multioverlapping decomposition strategy proposed in [16], the initial continuoustime system (3.1) can be conveniently expanded to form a new continuoustime system: where the state matrix and the control input matrix are block diagonal. The expanded system can then be decomposed into a sequence of decoupled continuoustime subsystems: For the continuoustime subsystems , we compute discretetime zerohold approximations with sampling time : where and consider the local quadratic indexes: to compute local discretetime LQR controllers which minimize the indexes (3.13) under constraints (3.11) and (3.14). Finally, the sequence of expanded local control matrices is contracted back to a control gain matrix in order to define a discretetime multioverlapping controller: for the original discretetime system (3.2).
Remark 3.1. The expansioncontraction procedure associated to the design of multioverlapping controllers for large buildings is only outlined in this section. For clarity and simplicity, a detailed account of this procedure has not been included in the paper. However, a complete presentation of this background material together with some practical applications to SVC of small buildings can be found in [15, 16].
Remark 3.2. The expanded blockdiagonal system (3.9) can only be computed when the matrices of the initial statespace system have a suitable zerononzero block structure. For the building model (2.1), the initial statespace system (3.1) has a proper structure. However, this structure is lost in the discretization process and the expansiondecoupling process can no longer be applied to the discretetime statespace system (3.2). To overcome this difficulty, the expansiondecoupling process is first completed for the continuoustime system and, after that, the discretization process is carried out on the continuoustime expanded decoupled subsystems (3.10) to obtain the discretetime expanded decoupled subsystems (3.11).
Remark 3.3. It should be noted that the control gain matrix given in (3.6) is a full matrix of size , and the full state is required to compute the control action for the actuation device : In contrast, the multioverlapping control matrix has a blocktridiagonal structure and only requires a reduced number of 4–6 states to compute the control action for each actuation device. More specifically, we have
Remark 3.4. For clarity and simplicity, the controllers presented in this section have been computed following an LQR approach. However, it has to be highlighted that other control strategies are also possible. For example, an application of the IP to the design of semidecentralized static outputfeedback controllers for SVC can be found in [14].
4. Simulation Models
One of the main objectives of the present work is to gain a meaningful insight into the behavior of semidecentralized multioverlapping controllers through numerical simulations. To this end, the simulation models have to include some relevant factors such as sampling rates, realistic implementation of the control actions, timedelayed state information, and communication latency and failures. Trying to achieve a proper balance between simplicity and accuracy, we have considered a simulation framework formed by three different models: (i) Basic building model, which consists in a discretetime approximation of the continuoustime statespace model (3.1) with a small basic sampling time and no control action. (ii) Centralized control model, which implements the discretetime LQR controller given in (3.4) with perfect state knowledge and the basic sampling time . The actuation devices, however, are assumed to be ideal semiactive force actuators with limited force capacity. (iii) Multioverlapping control model. This case implements the discretetime multioverlapping controller given in (3.15) considering semiactive actuation devices with limited force capacity, a control sampling time consistent with the communication latency, timedelayed state information, and communication failures. In all the cases, the interstory drifts are taken as output variables.
The basic building model is: where is the discretetime state matrix in (3.3); is the discretetime disturbance input matrix, which can be computed as with and representing, respectively, the state and disturbance input continuoustime matrices; and is the sampled disturbance. The vector of interstory drifts is computed with the output matrix given in (2.19). A good approximation of the uncontrolled seismic response of the building can be obtained using the basic building model with a small sampling time .
The centralized control model is: where the sampling time , the matrices , , and , and the sampled disturbance are the same as those used in (4.1); and is the discretetime inputcontrol matrix defined in (3.3). The vector of control actions is computed using the discretetime centralized control gain matrix given in (3.6), and the vector of control forces is In this section, the actuation devices are modeled as ideal semiactive force actuators with maximum actuation force . For a given control action , the actual control force exerted by the actuation device is where is the corresponding interstory velocity, and is the signum function. In the centralized control model, we assume an ideal communication system, which can provide a perfect knowledge of the full state vector . This model is used as a reference in the performance assessment of the multioverlapping controller.
In the multioverlapping control model, we consider the actuationcommunication system schematically depicted in Figure 4, consisting in an actuation device , a sensor unit , a local control unit , and a wireless communication unit . The actuation device produces the semiactive implementation of the control actions defined in (4.6). The sensor unit is an ideal sensor that provides an exact measurement of the local state where and denote the local interstory drift and velocity, respectively. To model the operation of the local control unit, we introduce the controller sampling time where is the basic sampling time used in (4.1) and (4.3), and can be understood as the maximum number of sampling steps that can be spent by to collect state information from neighboring stories through the communication unit . We also consider the updatecontrol times and define the interval of stateinformation gathering and the interval of controlaction holding which are schematically represented in Figure 5. The operation of the local control unit has been modeled in accordance with the following set of basic principles: The local control action is updated at the sampling times , . The local controller unit has direct access to the sensing unit ; consequently the local state is assumed to be always available. The state information of neighboring stories obtained through the wireless communication unit has the form , where and the time delay satisfies .For a given time interval with length , the events obtains the neighboring state in the time interval of length are independent random events with common probability If , are nonoverlapping time intervals with respective lengths and , then and are independent random events. Through the time interval , the local control unit tries to collect the state information required to compute the control action. If this state information is successfully acquired, the flag variable is set to 1 and the new control action is computed; otherwise, the flag variable and the control action are both set to 0. The control action computed at the sampling time is held through the time interval . According to the previous principles, the vector of control actions can be computed by setting the initial value and, for , using the expression where represents the integer remainder after division, is the diagonal matrix is the blockdiagonal matrix formed by the diagonal blocks of and the matrix contains the outofdiagonal blocks of the blocktridiagonal multioverlapping control matrix . The notation represents the delayed state where is the delay in the local state .
The multioverlapping control model can now be obtained by completing (4.14) and (4.15) with the state and output equations It should be noted that expressions (4.16) and (4.20) are only evaluated at the updatecontrol times . Moreover, according to (P. 1), (P. 4) and (P. 6), the informationstate flag variables are independent random variables with Bernoulli distributions . In particular and have a Bernoulli distribution , and has distribution for . Finally, it should also be noted that the probability of gathering the neighboring state information by the controller unit in a time interval of length is This formula can be easily obtained by writing the time interval as union of two nonoverlapping intervals of length . According to (P. 5), and are independent random events, and the probability of failing to acquire the state in the whole interval is . Analogously, it can be shown that the corresponding probability for a time interval of length is
5. Numerical Simulations
In this section, the behavior of discretetime multioverlapping LQR controllers is investigated through numerical simulations of the seismic response of a 20story building. The parameter values for this particular building are collected in Table 1 and are similar to those used in [5]. The damping matrix has been computed as a Rayleigh damping matrix by setting a 5% of damping ratio for the 1st and 18th natural frequencies. The actuation system implemented between the th and th stories (see Figure 2) is assumed to be formed by a number of identical actuation devices that work coordinately as a single device. The force saturation level of a single actuation device has been taken as N. The total number of actuation devices and the maximum actuation force for the actuation systems , , is also presented in Table 1.

For this 20story building, three different discretetime LQR controllers are designed: a centralized controller , which has been obtained using the basic sampling time s; and two multioverlapping controllers and , computed with sampling times , and , respectively. The particular values of the weighting matrices in (3.5) used to design the centralized controller are and . For the multioverlapping controllers, the weighting matrices in (3.13) used to compute the local expanded LQR controllers and , , have been taken as and , for .
In the numerical simulations, the maximum absolute interstory drifts have been computed for different control configurations. The basic building model given in (4.1) with sampling time s has been used to compute the uncontrolled seismic response. The controlled response corresponding to the centralized controller has been obtained with the centralized control model presented in (4.3). Finally, the multioverlapping control model defined in (4.14), (4.15), and (4.21) has been used to compute the controlled response for the multioverlapping controllers and . In all the cases, the full scale 1995 Kobe NorthSouth seismic record has been taken as ground acceleration (see Figure 6). This seismic record, obtained at the Kobe Japanese Meteorological Agency station during the HyogokenNanbu earthquake of January 17, 1995, is a nearfield record that presents large acceleration peaks which are extremely destructive to tall structures [18, 19]. In the multioverlapping control model, the reference value of has been set for the probability of obtaining state information from neighboring stories in a time interval of length ms. According to (4.23), for the multioverlapping controller with control sampling time ms, the probability of successfully gathering state information from neighboring stories can be taken as .
In Figure 7(a), the red line with asterisks (Overlap. fail in the legend) presents the maximum absolute interstory drifts corresponding to the multioverlapping controller with controller sampling time ms, probability of successful communication , and state delay ms. The blue line with circles (Overlap. in the legend), displays the values obtained with no communication failures, that is, with . The interstory drifts peak values corresponding to the multioverlapping controller with controller sampling time ms, and state delay ms are presented in Figure 7(b). Here, the red line with asterisks corresponds to the probability , and the blue line with circles presents again the results for . In both cases, the graphics corresponding to the uncontrolled response (black line with triangles), and the controlled response for the centralized controller , with controller sampling time ms, with no communication failures nor delays (black line with squares) have been included as reference. In Figure 8, the red line with asterisks (Over. delay in the legend) displays the maximum absolute interstory drifts corresponding to the multioverlapping controller with ms, , and state delay ms, while the green line with circles (Over. no delay in the legend) presents the response obtained with null state delay.
(a) Controller sampling time 40 ms
(b) Controller sampling time 20 ms
The graphics in Figure 7(a) show the excellent performance of the proposed multioverlapping controller for controller sampling times compatible with moderate values of communication latency and also compatible with moderate rates of communication failures. Moreover, the graphics in Figure 7(b) clearly illustrate the tradeoff between the controller sampling time and the probability of successful communication . Certainly, taking a smaller sampling time allows a more accurate implementation of the control actions; however, this also implies a reduction of the probability which, in the end, may result in an overall loss of performance.
Finally, the graphics in Figure 8 show the moderate influence of the state delay in the multioverlapping controller performance for reasonable values of the controller sampling time .
Remark 5.1. It is worth to be mentioned that the behavior of the ideal discretetime centralized controller is very similar to the behavior exhibited by an ideal continuoustime centralized LQR controller. As mentioned in Remark 3.3, full state information is required by centralized controllers and this fact makes them unsuitable for SVC of large buildings with wireless communication systems. A detailed discussion of this point can be found in [5].
Remark 5.2. The proposed semidecentralized controllers can operate using only state information from neighboring stories. This fact makes it possible for them to successfully collect the required state information in a relatively small time interval. As a side effect, state delays are also small and have no significant impact on the controller performance.
Remark 5.3. Force saturation is an important issue in SVC. For large seismic excitations, the required control actions frequently exceed the force capacity of the actuation devices. Consequently, force actuation constraints should be considered when studying the controllers behavior. All the numerical simulations of the controlled responses presented in this paper have been conducted using the force saturation values displayed in Table 1.
6. Conclusions and Future Directions
In this paper, a computationally effective strategy has been used to design discretetime statefeedback multioverlapping LQR controllers for seismic protection of tall buildings. This strategy, based on a sequential application of the Inclusion Principle, produces a blocktridiagonal control gain matrix that allows computing the corresponding control actions using only state information from neighboring stories. Due to this particular information exchange configuration of the multioverlapping controllers, the transmission range and the control sampling frequency in wireless implementations of the communication system can be dramatically improved. To investigate the behavior of the proposed semidecentralized multioverlapping controllers, a proper simulation model has been designed, which allows including semiactive actuation devices with limited force capacity, control sampling times consistent with the communication latency, timedelayed state information, and communication failures. To assess the performance of the proposed multioverlapping controllers, numerical simulations of the seismic response for a 20story building model have been conducted with positive results.
For clarity and simplicity, the controllers presented in this paper have been designed following an LQR approach. In future works, further research effort should be addressed at exploring the effectiveness of the proposed control design strategy in more complex scenarios, which can involve issues of practical interest such as structural information constraints [20], actuator saturation [21], actuation and sensor failures [22], and limited frequency domain [23]. Other natural extensions of the present work should include a deeper treatment of some important practical aspects related to the communication system such as missing measurements [24–28], stochastic uncertainties [29], and stochastic nonlinearities [30–33].
Acknowledgments
This work has been partially supported by the Spanish Ministry of Economy and Competitiveness through the Grant DPI201125567C02 and by the Norwegian Center of Offshore Wind Energy (NORCOWE) under Grant 193821/S60 from the Research Council of Norway (RCN).
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Copyright © 2012 Francisco PalaciosQuiñonero et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.