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Master of Science / Department of Mechanical and Nuclear Engineering / Guoqiang Hu / In some types of renewable energy systems such as wind turbines or solar power plants, the optimal operating conditions are influenced by the intermittent nature of these energies. This fact, along with the modeling difficulties of such systems, provides incentive to look for non-model based adaptive techniques to address the maximum power point tracking (MPPT) problem. In this thesis, a novel extremum seeking algorithm is proposed for systems where the optimal point and the optimal value of the cost function are allowed to be time varying. A sinusoidal perturbation based technique is used to estimate the gradient of the cost function. Afterwards, a robust optimization method is developed to drive the system to its optimal point. Since this method does not require any knowledge about the dynamic system or the structure of the input-to-output mapping, it is considered to be a non-model based adaptive technique. The proposed method is then employed for maximizing the energy capture from the wind in a variable speed wind turbine. It is shown that without any measurements of wind velocity or power, the proposed method can drive the wind turbine to the optimal operating point. The generated power is observed to be very close to the maximum possible values.

In this work, we propose three control strategies from different perspectives to solve the alignment problem for different optical wireless communication (OWC) systems. • Experimental modeling based strategy: we model and analyze the vibration effects on the stationary OWC system (e.g. urban free-space optical (FSO) communication system in our case). The proposed Bifurcated-Gaussian (B-G) distribution model of the receiver optical power is derived under different vibra- tion levels and link distances using the nonlinear iteration method. Besides, the UFSO channel under the effects of both vibration and atmospheric turbulence is also explored under three atmospheric turbulence conditions. Our proposed B-G distribution model helps to easily evaluate the link performance of UFSO systems and paves the way for constructing completed auxiliary control subsys- tems for robust UFSO links. • Extremum seeking control based strategy: we propose an extremum seeking control (ESC) based strategy for the mobile OWC system. Our proposed ap- proach consists of coarse alignment and fine alignment. The coarse alignment using feedback proportional-derivative (PD) control is responsible for tracking and following the receiver. For fine alignment, the perturbation-based extremum seeking control (ESC) is adopted for a continuous search for the optimal posi- tion, where the received optical power is maximum in the presence of distur- bance. The proposed approach is simple, effective, and easy to implement. • Time scale theory based strategy: we design a time scale based Kalman filter for the intermittent OWC system. First, the algorithm of Kalman filter on time scales is presented, followed by several numerical examples for interpretation and analysis. The design of Kalman filter on time scales for our simulated vibrating OWC system is then discussed, whose results are analyzed thoroughly and further validated by a reference system. The proposed strategy has great potential for solving the problem of observer design in the case of intermittent received signals (non-uniform measurements) and paves the way for further controller design. The three proposed control strategies directly or indirectly solve the beam align- ment problem for optical communication systems, supporting the development of robust optical communication link.

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Extremum seeking control (ESC) is a classical adaptive control method aimed at locating and tracking optimal operating conditions in complex non-linear plants. Early results on ESC were restricted to plants that could bedescribed by Wiener or Hammerstein models. However, recent results haveshown that ESC will possess a stationary solution close to the optimum also for more general dynamical systems, provided the gradient estimation and feedback is sufficiently slow relative to the process dynamics. This thesis addresses the uniqueness of this solution and the achievable rate of convergence.The motivation for the work stems from the need to optimize a complex biofilm reactor, the CANON process, which if operated near a narrow optimum may significantly lower the cost of ammonium removal in wastewater treatment. Simulations of ESC applied to the CANON process reveal that, depending on initial conditions and tuning parameters, the ESC loop may converge to stationary solutions far removed from the optimum and that multiple stationary solutions may exist. Analysis of a general model for the ESC loop shows that the stationary solutions are characterized either by a gain condition or a phase lag condition on the locally linearized system, the latter indicating that the ESC loop can act as a phase-lock loop. The phase lag condition is shown to be satisfied close to the optimum, but can be fulfilled also at operating points with no relation to the optimality criterion whatsoever and this serves to explain the observed solution multiplicity. Bifurcation theory is employed to further analyze the stationary solutions of the ESC loop and conditions for existence of saddle-node bifurcations are derived. A saddle node bifurcation implies a hard loss of stability and the existence of multiple stationary solutions. It is also demonstrated, using examples, that the ESC loop may undergo other types of bifurcations, including period doubling bifurcations into chaos. For the considered example, the resulting chaotic solution is significantly closer to optimum than the underlying nominal limit cycle. Previous results on ESC applied to general dynamic systems have relied on the use of asymptotic methods, such as singular perturbations and averaging. This has resulted in a three time-scale separation of the problem, in which the gradient estimation and control have been forced to be significantly slower than the open-loop process dynamics. For most processes, including the CANON process studied in this thesis, this renders ESC of little practical use and we therefore consider relaxing some of the restrictive assumptions. Inparticular, we allow for any gradient estimation rate and significantly faster gradient feedback as compared to previous studies. Using a linear parameter varying (LPV) description of the plant, quantitative expressions for the convergence rate in terms of the ESC tuning parameters and plant properties are derived. / <p>QC 20141106</p>

<p>Data networks are an important part in an increasing number of applications with real-time and reliability requirements. To meet these demands a variety of approaches have been proposed. Forward error correction, which adds redundancy to the communicated data, is one of them. However, the redundancy occupies communication bandwidth, so it is desirable to control the amount of redundancy in order to achieve high reliability without adding excessive communication delay. The main contribution of the thesis is to formulate the problem of adjusting the redundancy in a control framework, which enables the dynamic properties of error correction control to be analyzed using control theory. The trade-off between application quality and resource usage is captured by introducing an optimal control problem. Its dependence on the knowledge of the network state at the transmission side is discussed. An error correction controller that optimizes the amount of redundancy without relying on network state information is presented. This is achieved by utilizing an extremum seeking control algorithm to optimize the cost function. Models with varying complexity of the resulting feedback system are presented and analyzed. Conditions for convergence are given. Multiple-input describing function analysis is used to examine periodic solutions. The results are illustrated through computer simulations and experiments on a wireless sensor network.</p>

Indiana University-Purdue University Indianapolis (IUPUI) / With the increasing demand of economy and environmental pollutions, more and more renewable energy systems with clean sources appear and have attracted attention of systems involving solar power, wind power and hybrid new energy powers[1]. However, there are some difficulties associated with combined utilization of solar and wind, such as their intermittent behavior and their peak hours mismatch in generation and consumption[1]. For this purpose, advanced network of a variety of renewable energy systems along with controllable load and storage units have been introduced[1-3]. This thesis proposes some configurations of hybrid energy harvesting systems, including wind-wind-storage DC power system with BOOST converters, solar-solar-storage DC power system with cascade BOOST converters, wind-solar-storage DC power system with BOOST converter and cascade BOOST converter, and wind-solar DC power system with SEPIC converter and BOOST converter. The models of all kinds of systems are built in Matlab/Simulink and the mathematical state-space models of combined renewable energy systems are also established. Several MPPT control strategies are introduced and designed to maximize the simultaneous power capturing from wind and solar, such as Perturb & Observe (P&O) algorithm for solar and wind, Tip Speed Ratio (TSR) control and Power Signal Feedback (PSF) control for wind, and Sliding Mode Extremum Seeking Control (SM-ESC) for wind and solar systems[4]. The control effects of some of these MPPT methods are also compared and analyzed. The supervisory control strategies corresponding to each configurations are also discussed and implemented to maximize the simultaneous energy harvesting from both renewable sources and balance the energy between the sources, battery and the load[2]. Different contingencies are considered and categorized according to the power generation available at each renewable source and the state of charge in the battery[2]. Applying the system architectures and control methods in the proposed hybrid new energy systems is a novel and significant attempt, which can be more general in the practical applications. Simulation results demonstrate accurate operation of the supervisory controller and functionality of the maximum power point tracking algorithm in each operating condition both for solar and for wind power[3]

Data networks are an important part in an increasing number of applications with real-time and reliability requirements. To meet these demands a variety of approaches have been proposed. Forward error correction, which adds redundancy to the communicated data, is one of them. However, the redundancy occupies communication bandwidth, so it is desirable to control the amount of redundancy in order to achieve high reliability without adding excessive communication delay. The main contribution of the thesis is to formulate the problem of adjusting the redundancy in a control framework, which enables the dynamic properties of error correction control to be analyzed using control theory. The trade-off between application quality and resource usage is captured by introducing an optimal control problem. Its dependence on the knowledge of the network state at the transmission side is discussed. An error correction controller that optimizes the amount of redundancy without relying on network state information is presented. This is achieved by utilizing an extremum seeking control algorithm to optimize the cost function. Models with varying complexity of the resulting feedback system are presented and analyzed. Conditions for convergence are given. Multiple-input describing function analysis is used to examine periodic solutions. The results are illustrated through computer simulations and experiments on a wireless sensor network. / QC 20101105