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Time-delay of magnetorheological elastomer bearing (MRB) can bring structural response menace to bridges. This paper investigates a bridge pier-bearing semi-active-coupling control method based on model predictive control (MPC). The presented strategy takes the structure prediction model to predict the state responses of the controlled plant in a period of future time. Then, the control law can be determined by solving a finite horizon optimization problem. The peak shearing force of pier top, the displacement and the acceleration of beam are chosen as control goals, and the vibration isolation rate is applied to characterize the vibration isolation effect. It is noted that MPC method naturally takes the time-delay and uncertain interference into consideration, and significantly improves the control performance of the system. Finally, the numerical example is described and the seismic response of isolated bridge based on MRB is analyzed. The simulation results show that predictive control can be used to control the time-delay of bridge system in different degrees. The best control performance is at 0.4 s. Even if the time-delay reaches 2 s, it is still good. Therefore, the control method significantly reduces the adverse effects of time-delay on the system, and has a good vibration isolation performance.

In this paper, an observability analysis of sensor bias based on the track-independent estimation model is presented. Observability analysis is a prerequisite for developing a systematic error estimation model. Therefore, an observability matrix is adopted to study the observability and observability degree of sensor bias. Initially, based on the track-independent model, both state transformation and measurement models of sensor bias are built in two dimensions to construct the observability matrix. Then, the observability of the sensor bias is studied by judging the singularity of the observability matrix. Further, the mathematical expression for the degree of observability is presented. Finally, several typical simulations to demonstrate the effect of degree of observability on the estimation accuracy of sensor bias and the effects of sensor location and target motion on the observability and observability degree of sensor bias are provided.

Systematic error of radar cannot be accurately determined solely with the use of an automatic dependent surveillance broadcasting (ADS-B) device. This is largely, because of the device's inability to obtain the exact transmitting time of ADS-B data packets. In order to study and establish an efficient and accurate error registration algorithm for radars based on the ADS-B system, a joint systematic error estimation model of the radar and ADS-B was built. The authors showed that when receiving time is known, the time difference between radar measuring time and ADS-B data transmitting time is consistently biased, a feature that can be used to calculate the systematic error of the ADS-B receiving system. Then, they established an error estimation model based on the joint systematic error of the radar and ADS-B. The generalised least squares estimation method was adopted to solve the model. Finally, data samples verified the accuracy and effectiveness of the error registration algorithm.