EACS 2012 – 5th European Conference on Structural Control
Genoa, Italy – 18-20 June 2012 Paper No. #147
An Active Mass Damper Based on Wireless Sensors and Digital Control
Sara CASCIATI1, ZhiCong Chen2*, Lucia FARAVELLI2, Umut YILDIRIM2 1
Department DARC, University of Catania Piazza Federico di Svevia, 96100 Siracusa, Italy
[email protected] 2 Department of Structural Mechanics, University of Pavia Via Ferrata 1, 27100 Pavia, Italy.
[email protected],
[email protected],
[email protected]
ABSTRACT With the increasing demand on structural safety, the concept of structural control is conceived to protect the controlled structure by reducing the response due to external excitation, such as earthquake and strong wind. An active mass damper (AMD), i.e., the active form of the passive device known as tuned mass damper (TMD), is a typical way to implement structural control. An existing laboratory three-storey steel frame was used to validate different control schemes. The frame is mounted on a single-axis shaking table simulating the ground excitation. Four single-axis wired accelerometers were mounted on each level (ground and floors of the frame). A mass cart driven by a DC motor, a DC motor position analog controller, and a controller board complete the early bed-test realization. Recently wireless sensors and a digital position controller were introduced to update the AMD performance. In this paper the frame specimen is simulated by a numerical model allowing the authors to design and test the control laws without any risk of damaging the physical model. Such a modeling is pursued by an approach different from the one adopted in early studies. Keywords:, Active mass damper, Digital control, Shaking table tests, Wireless sensors
1 INTRODUCTION During the last three decades many structural control strategies have been proposed for the mitigation of the dynamic structural response induced by environmental and man-made excitations in [1-5]. A control system for the vibration mitigation consists of several sensors, a controller(s), a control algorithm(s), and a control force generator(s): these components act as an integrated system [6]. The integrated system can be used to verify the effectiveness of any proposed control algorithm. To quantitatively validate the actual potential of control algorithms, the dynamic response of a reduced scale, three-story steel frame, with an active mass damper (AMD) installed on the top floor, was studied under sinusoidal input excitation, white-noise realizations and recorded acceleration time histories [7]. The control strategy was implemented inside a dedicated digital computer, with the function of data acquisition being played through the communication with an Analog to Digital Converter (ADC) and a Digital to Analog Converter (DAC). The analog/digital control hardware features a custom-designed signal interface unit which is equipped with the *
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Corresponding author
modules of signal conditioning, filtering, monitoring, fail-safe limit detection, signal communication, and remote activation subsystems. The adoption of a wireless technology eliminates the mechanical vulnerability of cables, and thus it is regarded as a step forward toward the promotion and spread of such control systems [8-11]. It represents a first step to move toward a fully digital technology aiming to replace all the analog components. Using the new digital test-bed, the comparison of the performance achieved by different control laws is evaluated. For this purpose, the following system architecture is considered: 1) Frame and sensors replaced by a numerical model sending, at each time step, the sensor readings in a wireless manner (i.e., in a digital form) 2) A controller board whose microcontroller elaborates the control action to be sent ,wireless (i.e., in a digital form); 3) The digital controller of the active control device, whose force is accounted in the next time step of the whole simulation. This paper is devoted to both the components validation and to the assemblage of the components into an integrated system. 2 THE EXPERIMENTAL SPECIMEN AND THE TEST ENVIRONMENT A reduced scale three-story, single-bay steel frame is mounted on a uniaxial shaking table able to infer ground accelerations to the specimen. A DC-motor is implemented on the top of the structure to activate a mass damper. The driver signal of the motor corresponds to the input control force applied to the structure. The main characteristics of the laboratory facilities used in the test are given as follows. The shaking table has a 92x92cm aluminium plate that is driven by a 10kN hydraulic actuator. It operates in the frequency range from 0 to 25 Hz. The test structure is the three-story steel frame shown in Figure 1. Each floor consists of a 60x30x3cm steel plate of weight 42kg and is supported by four columns of rectangular crosssection. The height of each floor is 38 cm. The test structure can be set into several configurations (varying from one to three degrees of freedom) by a suitable mounting of the bracing system. In this contribution no braces are installed. The active-mass damper (AMD) is mounted on a cart which is driven by the DC-motor through a gear. The position of the moving mass is controlled by a proportional derivative (PD) controller that is fed on a displacement measurement from the potentiometer installed on the moving cart. The maximum stroke is ±15cm with a maximum nominal acceleration of ±5g. The moving mass is 1.7 kg and it is approximately equal to 1.2% of the total mass of the structure. The control system was initially implemented by using the Multi-Q board developed by Quanser Consulting Inc [11]. The board has eight analog I/O channels and each of them features A/D and D/A converters of 12 bit resolution. The early data acquisition system used for the system identification and control strategy was a National Instruments AT-MIO-16DE with 16 analog input channels and 12 bit A/D converters. It is driven by the Labview software. In addition, eight poles, elliptic antialiasing filters with programmable gains and cut off frequencies in the range of 0-10000Hz are used to reduce the high frequency components of the signals. The filters are analog and provide a sharp roll off of 135db/octave. The measurements of the absolute accelerations of the three floors and ground accelerations are acquired by four Kinemetrics FBA-11 accelerometers. The sensors are accurate from 0.5Hz to 50Hz, with a signal noise lower than 2 mV.
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Figure 1 - The experimental mock-up
3 MODELLING THE STRUCTURE The structural specimen is excited by two inputs: ag(t) and u(t), representing the time histories of the ground acceleration and the top floor force, respectively. The observed variables are the floor accelerations, whose time histories are denoted as a1(t), a2(t), a3(t), as proceding from the bottom to the top. Data were recorded in different sets. The datasets obtained from the four Kinemetric FBA11 accelerometers that are mounted on the shaking table and on each of the three floors of the structural system, will be denoted as Ch0, Ch1, Ch2, and Ch3, respectively. A further channel is dedicated to record the control command and is denoted as Ch-engine. The differential equations that identify the structural behaviour of the three degrees of freedom (d.o.f ) shear type structure by describing its motion under external forces are given by (1) with [M] = the mass matrix; [C] = the damping matrix and [K] = the stiffness matrix, provided the material properties are known. Moreover, are the horizontal displacement, velocity and acceleration vectors (their coordinates represent the corresponding kinematic quantities at each floor with reference to the ground) and {F(t)} accounts for the forces (including both excitation and control).. Alternatively, a state-space representation of the shear type structure can be used. The statespace equation is written as (2)
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with {x} the state variable vector, {u} the input vector, and {y} the output vector of the structural system. 4 SYSTEM IDENTIFICATION PROCEDURE The preliminary system identification procedure was carried out in the frequency domain [7]. The structural response measurements, acquired in time domain were transformed in the frequency domain by Fast Fourier Transform (FFT). The experimental transfer functions were evaluated by polynomials to obtain the system model. The structural system transfer function matrix was represented corresponding to two input signals; namely, the AMD driving signal, u, and the ground acceleration, ag. For the purpose of this specific paper, the identification of Eq. (1) was pursued in a parametric form, by assuming as target the full agreement of the numerically simulated time histories with the ones recorded during the experiment. Figure 2 provides details of the fitting for a sine-wave excitation. 3rd floor acc.
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Figure 2 –Comparison of the numerical and experimental response of the uncontrolled frame under a sinusoidal excitation at 1.15Hz, with an amplitude of 2mm 5 CONTROL BOARD The controller (see Figure 3, except for the dotted block) was implemented mainly by the microcontroller C8051F007 which integrates with 4 analog to digital (ADC) input channels and 2 DAC (digital to analog) output channels. Two control algorithms, linear control and fuzzy control, were implemented [8]. In this paper, the linear control is selected to support the necessary steps. The firmware has been designed with the easiness of use as the main concern. It has been implemented as a terminal shell. On the computer, the software HyperTerminal is used to access the embedded shell interface exposed by the board.
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In order to introduce wireless sensor links [11], the controller is slightly modified. As shown by the dotted block in Figure 3, wireless transceivers connected to the microcontroller via SPI interface are added. The wireless transceivers are used to command the wireless sensors and collect the data from them as well. The user can choose to acquire the wireless or the wired signal input through the computer.
Figure 3 - Block Diagram of the controller With the goal of preserving the safety of the laboratory specimen and wanting to achieve an easy repeatability of the fully numerical tests, three verification approaches are used in designing and optimizing the controller. The first approach is to apply the control board directly on the frame; the second approach is to apply the control board on the model of the frame; the third approach is to use the simulator of both the control board and the model. (1) Control board and real frame Using this approach, the AMD will directly act on the frame. If the controller does not perform as expected, the AMD might produce a negative effect on the frame, and eventually destroy the specimen. Therefore, it is only recommended when the controller has been validated in simulation. (2) Control board and frame model Using this approach, the control board is connected to the Matlab model of the system formed by the frame and the AMD, which is created by applying the methods described in the previous two sections. For this close loop simulation, a modification to the controller board is required. The modification consists of adding a simulation mode to the firmware, in which the control algorithm receives the acceleration data from the model through a serial port and then computes the control output, which is sent back to the Matlab model again through the serial port. Therefore, in each time step, the model sends four acceleration data to the controller and waits for the control output data. Once the controller receives the set of 4 data, it computes and outputs the control data, and then waits for the next set of 4 data. (3) Simulator of the control board and frame model 5
The function of the control board is to sample the acceleration signals from the frame by ADC, then execute the control algorithm, and output the control signal by DAC. Its control algorithm could be implemented in Matlab. The difference between the Matlab control algorithm and the one implemented in the control board is mainly due to the computation precision. In order to facilitate the design of the control algorithm and to check the impact of the computation precision, a simulator of the control algorithm is also implemented. 6 EXPERIMENTAL VALIDATION In order to validate both the AMD and the model of the controlled frame, a shaking table excitation represented by a sinusoidal wave with a frequency of 1.25 Hz and an amplitude of 2 mm, is applied to the controlled frame. Since the model of the uncontrolled frame has been validated, a simulated response of the frame without control under the excitation is obtained. The numerical response of the uncontrolled frame is plotted together with the experimental result of the controlled frame, as shown in Figure 4 where the effectiveness of the AMD in reducing the structural response is evident. Finally, the numerical and experimental responses of the controlled frame are plotted together in Figure 5 and the two responses are verified to match well. 3rd floor acc.
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Figure 5 – Comparison of the experimental and simulated response of the controlled frame under a sinusoidal ground excitation of 1.25Hz and 2mm 7 CONCLUSIONS To reduce the high-costs of cables installation and maintenance, a wireless communication system can serve as an alternative real-time communication link between the nodes of a control system. In this paper, feedback control algorithms are embedded between the wireless sensors and the actuator devices. To validate the embedment of such control algorithms, a prototype of a wireless structural sensing and control system is implemented and its performance is verified by analyzing the results from shaking table tests, full numerical simulation and hybrid testing of a three-story, reduced scale steel structure, with an active mass damper AMD installed on the top floor. The results show that the control solutions can be conveniently designed taking advantage of numerical and hybrid simulators. ACKNOWLEDGEMENTS The authors acknowledge the support from Catania and Pavia Research Athenaeum funds. The last author is supported by the Marie Curie European project SMARTEN.
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