Process Control Applications in Microbial Fuel Cells(MFC)

abstract: Microbial fuel cells(MFC) use micro-organisms called anode-respiring bacteria(ARB) to convert chemical energy into electrical energy. This process can not only treat wastewater but can also produce useful byproduct hydrogen peroxide(H2O2). Process variables like anode potential and pH play important role in the MFC operation and the focus of this dissertation are pH and potential control problems. Most of the adaptive pH control solutions use signal-based-norms as cost functions, but their strong dependency on excitation signal properties makes them sensitive to noise, disturbances, and modeling errors. System-based-norm( H-infinity) cost functions provide a viable alternative for the adaptation as they are less susceptible to the signal properties. Two variants of adaptive pH control algorithms that use approximate H-infinity frequency loop-shaping (FLS) cost metrics are proposed in this dissertation. A pH neutralization process with high retention time is studied using lab scale experiments and the experimental setup is used as a basis to develop a first-principles model. The analysis of such a model shows that only the gain of the process varies significantly with operating conditions and with buffering capacity. Consequently, the adaptation of the controller gain (single parameter) is sufficient to compensate for the variation in process gain and the focus of the proposed algorithms is the adaptation of the PI controller gain. Computer simulations and lab-scale experiments are used to study tracking, disturbance rejection and adaptation performance of these algorithms under different excitation conditions. Results show the proposed algorithm produces optimum that is less dependent on the excitation as compared to a commonly used L2 cost function based algorithm and tracks set-points reasonably well under practical conditions. The proposed direct pH control algorithm is integrated with the combined activated sludge anaerobic digestion model (CASADM) of an MFC and it is shown pH control improves its performance. Analytical grade potentiostats are commonly used in MFC potential control, but, their high cost (>$6000) and large size, make them nonviable for the field usage. This dissertation proposes an alternate low-cost($200) portable potentiostat solution. This potentiostat is tested using a ferricyanide reactor and results show it produces performance close to an analytical grade potentiostat.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Flexible structure control laboratory development and technology demonstration

An experimental structure is described which was constructed to demonstrate and validate recent emerging technologies in the active control and identification of large flexible space structures. The configuration consists of a large, 20 foot diameter antenna-like flexible structure in the horizontal plane with a gimballed central hub, a flexible feed-boom assembly hanging from the hub, and 12 flexible ribs radiating outward. Fourteen electrodynamic force actuators mounted to the hub and to the individual ribs provide the means to excite the structure and exert control forces. Thirty permanently mounted sensors, including optical encoders and analog induction devices provide measurements of structural response at widely distributed points. An experimental remote optical sensor provides sixteen additional sensing channels. A computer samples the sensors, computes the control updates and sends commands to the actuators in real time, while simultaneously displaying selected outputs on a graphics terminal and saving them in memory. Several control experiments were conducted thus far and are documented. These include implementation of distributed parameter system control, model reference adaptive control, and static shape control. These experiments have demonstrated the successful implementation of state-of-the-art control approaches using actual hardware

Subspace-Based Blind Channel Identification for Cyclic Prefix Systems Using Few Received Blocks

In this paper, a novel generalization of subspace-based blind channel identification methods in cyclic prefix (CP) systems is proposed. For the generalization, a new system parameter called repetition index is introduced whose value is unity for previously reported special cases. By choosing a repetition index larger than unity, the number of received blocks needed for blind identification is significantly reduced compared to all previously reported methods. This feature makes the method more realistic especially in wireless environments where the channel state is usually fast-varying. Given the number of received blocks available, the minimum value of repetition index is derived. Theoretical limit allows the proposed method to perform blind identification using only three received blocks in absence of noise. In practice, the number of received blocks needed to yield a satisfactory bit-error-rate (BER) performance is usually on the order of half the block size. Simulation results not only demonstrate the capability of the algorithm to perform blind identification using fewer received blocks, but also show that in some cases system performance can be improved by choosing a repetition index larger than needed. Simulation of the proposed method over time-varying channels clearly demonstrates the improvement over previously reported methods

Approximate Bayesian Computation by Subset Simulation

A new Approximate Bayesian Computation (ABC) algorithm for Bayesian updating of model parameters is proposed in this paper, which combines the ABC principles with the technique of Subset Simulation for efficient rare-event simulation, first developed in S.K. Au and J.L. Beck [1]. It has been named ABC- SubSim. The idea is to choose the nested decreasing sequence of regions in Subset Simulation as the regions that correspond to increasingly closer approximations of the actual data vector in observation space. The efficiency of the algorithm is demonstrated in two examples that illustrate some of the challenges faced in real-world applications of ABC. We show that the proposed algorithm outperforms other recent sequential ABC algorithms in terms of computational efficiency while achieving the same, or better, measure of ac- curacy in the posterior distribution. We also show that ABC-SubSim readily provides an estimate of the evidence (marginal likelihood) for posterior model class assessment, as a by-product

Real-time flutter identification

The techniques and a FORTRAN 77 MOdal Parameter IDentification (MOPID) computer program developed for identification of the frequencies and damping ratios of multiple flutter modes in real time are documented. Physically meaningful model parameterization was combined with state of the art recursive identification techniques and applied to the problem of real time flutter mode monitoring. The performance of the algorithm in terms of convergence speed and parameter estimation error is demonstrated for several simulated data cases, and the results of actual flight data analysis from two different vehicles are presented. It is indicated that the algorithm is capable of real time monitoring of aircraft flutter characteristics with a high degree of reliability

Real-time parameter estimation of DC-DC converters using a self-tuned kalman filter

To achieve high-performance control of modern dc-dc converters, using direct digital design techniques, an accurate discrete model of the converter is necessary. In this paper, a new parametric system identification method, based on a Kalman filter (KF) approach is introduced to estimate the discrete model of a synchronous dc-dc buck converter. To improve the tracking performance of the proposed KF, an adaptive tuning technique is proposed. Unlike many other published schemes, this approach offers the unique advantage of updating the parameter vector coefficients at different rates. The proposed KF estimation technique is experimentally verified using a Texas Instruments TMS320F28335 microcontroller platform and synchronous step-down dc-dc converter. Results demonstrate a robust and reliable real-time estimator. The proposed method can accurately identify the discrete coefficients of the dc-dc converter. This paper also validates the performance of the identification algorithm with time-varying parameters, such as an abrupt load change. The proposed method demonstrates robust estimation with and without an excitation signal, which makes it very well suited for real-time power electronic control applications. Furthermore, the estimator convergence time is significantly shorter compared to many other schemes, such as the classical exponentially weighted recursive least-squares method

Adaptive Control By Regulation-Triggered Batch Least-Squares Estimation of Non-Observable Parameters

The paper extends a recently proposed indirect, certainty-equivalence, event-triggered adaptive control scheme to the case of non-observable parameters. The extension is achieved by using a novel Batch Least-Squares Identifier (BaLSI), which is activated at the times of the events. The BaLSI guarantees the finite-time asymptotic constancy of the parameter estimates and the fact that the trajectories of the closed-loop system follow the trajectories of the nominal closed-loop system ("nominal" in the sense of the asymptotic parameter estimate, not in the sense of the true unknown parameter). Thus, if the nominal feedback guarantees global asymptotic stability and local exponential stability, then unlike conventional adaptive control, the newly proposed event-triggered adaptive scheme guarantees global asymptotic regulation with a uniform exponential convergence rate. The developed adaptive scheme is tested to a well-known control problem: the state regulation of the wing-rock model. Comparisons with other adaptive schemes are provided for this particular problem.Comment: 29 pages, 12 figure