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An accelerometer-based gyro drift compensation approach in a dual-axial stabilization platform is introduced in this paper. The stabilization platform consists of platform framework, drive motor, gyro and accelerometer module and contorl board. Gyro is an angular rate detecting element to achieve angular rate and rotation angle of the dynamic platform system. However, the platform system has an unstable factor because of the drift of gyro. The main contribution of this paper is to implement a convenient gyro drift compensation approach by using the accelerometer. In contrast to a kalman filtering method, this approach is simpler and practical due to the high-precision characteristic of the accelerometer. Data filtering algorithm and limit of threshold setting of total acceleration values are applied in this approach. The validity and feasibility of the proposed approach are evaluated by four tests under various conditions.

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The single-component adaptive Metropolis (SCAM) algorithm, as one of the Markov chain Monte Carlo (MCMC) sampling methods, is very effective at solving high-dimensional posterior joint probability distributions. However, studies show that the SCAM algorithm is prone to generating duplicate samples, thus lowering the sampling efficiency and causing large calculation errors. To solve this problem, a modified SCAM algorithm is proposed in the present study. In the modified algorithm, the expression of the variance of the proposal distribution is redefined so that the Markov chain formed by the sample sequence is relatively stable. With the obtained structural response, the posterior joint probability distribution of the physical parameters of the structure is achieved using Bayesian theory, which is then combined with the modified SCAM algorithm to solve the posterior marginal probability distribution and the optimal estimates. Hence, the structural damage is identified. The validity, reliability, and practicability of the modified SCAM algorithm are verified through theoretical analysis, a numerical simulation of the structural example, and a shaking table test. The results show that the modified SCAM algorithm not only enhances the accuracy of the calculation results but also increases the sampling efficiency. This method can be used as a reference for physical parameter identification and damage assessment of the engineering structures.

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