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Digital microfluidics systems require advanced controllers to operate accurately since their parameters are subjected to change in environment and over time. Due to imperfect manufacturing processes, their fabricated system parameters may become different from the destined values. Hence, estimation based controllers are required to identify the system parameters. The electrowetting on dielectric can be precisely controlled to dispense desirable and repeatable droplets. However, the system parameters and their variation over time makes the control system challenging. This paper describes the application of an indirect adaptive trajectory controller for digital Pico-Droplet dispensing system. Forgetting factor recursive least square estimator is used to estimate the system parameters including capacitance and resistance of the occupying droplet between electrodes. Indirect adaptive technique is used to measure and control the droplet volume on the dispensing electrodes. Simulations of the estimator, tracking performance of dispensed droplet volume and the controller's control effort are provided to demonstrate an accurate and high performance control approach.

Nonlinear model of hydraulic wind power system operates on a wide spectrum of operating points such as random wind speed disturbances and applied control commands. Thus, one way to linearize this model is to use multiple linear models representing the whole range of operating points. This paper introduces a minimal number of fixed linear models in a multiple model adaptive estimation (MMAE) framework to reduce the state estimation error. System parameters such as pressures of the pump and motors can be estimated while the overall error in entire operating points is reduced. The algorithm is composed of a bank of Kalman filters, each of which is modeled to match particular real world operating condition. Simulation results demonstrate that the adaptive approach can optimally estimate the state variables in a wide range of operating points.

This paper presents a new inverter technology that is designed based on SEPIC converter. Small signal model and transfer functions of the inverter are obtained. The system demonstrates the existence of a non-minimum phase zero in positive peak and shift of that zero to left half plane to make the negative peak transfer function a minimum phase behavior. Simulations demonstrate that at various circuit parameter values, as the duty cycle increased in boost mode of operation, the transfer function zeros migrated towards the origin. Increasing the inductance values resulted in similar behavior. However, increasing the capacitance values resulted in a reduction in real part of the complex poles and zeros.

In this paper a battery fault detection unit is developed using multiple model adaptive estimation technique. Impedance spectroscopy data from Li-ion cell is used along with the equivalent circuit methodology to construct the battery models. Battery faults such as over charge and over discharge cause significant model parameter variation and can be considered as separate models. Kalman filters are used to estimate the parameters of each model and to generate the residual signal. These residuals are used in the multiple model adaptive estimation technique to detect battery faults. Simulation results show that using this method the stated battery faults can be detected in real-time, thus providing an effective way of diagnosing Li-Ion battery failure.

This paper illustrates the application of model predictive control for trajectory control of micro electro mechanical lateral comb resonators (MEMS LCRs). A recursive least square estimator with dynamic data weighting method is used to estimate the parameters of MEMS in real-time. Accordingly, the control law and the gains are derived to achieve high performance trajectory control. Simulation results demonstrate a close trajectory profile tracking and accurate parameter estimation performance.