One of the challenges with model-based control of stochastic dynamical systems is that the state transition dynamics are involved, making it difficult and inefficient to make good-quality predictions of the states. Moreover, there are not many representational models for the majority of autonomous systems, as it is not easy to build a compact model that captures all the subtleties and uncertainties in the system dynamics. In this work, we present a hierarchical Bayesian linear regression model with local features to learn the dynamics of such systems. The model is hierarchical since we consider non-stationary priors for the model parameters which increases its flexibility. To solve the maximum likelihood (ML) estimation problem for this hierarchical model, we use the variational expectation maximization (EM) algorithm, and enhance the procedure by introducing hidden target variables. The algorithm is guaranteed to converge to the optimal log-likelihood values under certain reasonable assumptions. It also yields parsimonious model structures, and consistently provides fast and accurate predictions for all our examples, including two illustrative systems and a challenging micro-robotic system, involving large training and test sets. These results demonstrate the effectiveness of the method in approximating stochastic dynamics, which make it suitable for future use in a paradigm, such as model-based reinforcement learning, to compute optimal control policies in real time.

The deformation of lithographic planar gold nanostructures under cyclic thermal loading and its influence on surface plasmon resonance sensing are investigated.

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We report high-speed, large dynamic range spectral domain interrogation of fiber-optic Fabry–Perot (FP) interferometric sensors. An optical interrogation system employing a piezoelectric FP tunable filter and an array of fiber-Bragg gratings for wavelength referencing is developed to acquire the reflection spectrum of FP sensors at a high interrogation speed with a wide wavelength range. A 98 nm wavelength interrogation range was obtained at the resonance frequency of ∼ 110 k H z of the FP tunable filter. At this frequency, the resolution of the FP cavity length measurement was 1.8 nm. To examine the performance of the proposed high-speed spectral domain interrogation scheme, two diaphragm-based fiber-tip FP sensors (a pressure sensor and acoustic sensor) were interrogated. The pressure measurement results show that the high-speed spectral domain interrogation method has the advantages of being robust to light intensity fluctuations and having a much larger dynamic range compared with the conventional intensity-based interrogation method. Moreover, owing to its capability of measuring the absolute FP cavity length, the proposed interrogation system mitigates the sensitivity drift that intensity-based interrogation often suffers from. The acoustic measurement results demonstrate that the high-speed spectral domain interrogation method is capable of high-frequency acoustic measurements of up to 20 kHz. This work will benefit many applications that require high-speed interrogation of fiber-optic FP interferometric sensors.

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