This paper considers the problem of enhancing the performance of buck-boost DC-DC converters that are already regulated in voltage-mode by a typical PID type-III primary controller. This need may arise in situations where a commercial power converter is not easily replaceable. In these cases improved performance may be obtained with the addition of a secondary controller/prefilter in the form of a reference governor. Recent results in the literature have justified this policy for buck or boost converters. It has been shown that such a complementary scheme, which adjusts the imposed voltage reference signal dynamically, can offer significant performance enhancement in a wide operating range, and without imposing extra requirements or limitations. This work has applied similar concepts to the case of a buck-boost converter topology. An optimal design using a closed-form model predictive control (MPC) methodology is adopted, which is computationally tractable for digital microprocessor implementation. Moreover, contrary to the buck or boost case, the MPC reference governor policy has been found more flexible when applied to buck-boost converters, i.e. offering a better constraints handling ability. These findings are supported by extensive simulation results.

The design of simple linear control laws via linear mathematical models is a standard part of every control systems laboratory in a standard undergraduate curriculum. A typical example is the design of PI control laws for the speed control problem of DC motors. Embedded control is taught nowadays using cheap take-home equipment based on low-cost microprocessors like Arduino and dc micro-motors. Although offering ease for the demonstration of simple PI control laws, these motors exhibit nonlinear characteristics, which can be dealt with the application of more advanced control laws. The paper's purpose is twofold: first, to explore the possibilities offered by low-cost equipment, as a test bed for the “proof of concept” of more sophisticated switching control laws; and secondly to unfold the possibilities offered by this new environment for teaching, demonstration, project work, as well as research purposes, in the current laboratory practice.

In this paper we investigate the nonlinear dynamics of DC-DC boost converters under state feedback controllers. We demonstrate that it is possible to have multiple equilibria, which can further bifurcate and result in a torus or even chaos. More specifically, by changing the feedback gains we show that it is possible to have a saddle node and then a period 2 bifurcation. These are then followed by a slow scale bifurcation that results in a 2T torus. Also, the nominal or desired equilibrium hits a border in the state space that forces the steady state duty cycle to become 0. Obviously the occurrence of saturation in the duty cycle or the existence of instabilities can greatly decrease the converter's performance. In this paper we study this behavior: we use semi-analytical methods to locate the stable and unstable period 1 and period 2 orbits, the unstable manifold of the period 1 orbit; and by using the theory of the monodromy matrix enhanced with the saltation matrix we determine the stability of the converter. The latter can be used for design purposes in order to guarantee a stable and satisfactory performance.

This work investigates a particular instance of the problem of designing efficient adaptive systems, under the condition that each adaptation decision incurs some nonnegligible cost when enacted. More specifically, we deal with the problem of dynamic, intraquery load balancing in parallel database queries across heterogeneous nodes in a way that takes into account the inherent cost of adaptations and thus avoids both overreacting and deciding when to adapt in a completely heuristic manner. The latter may lead to serious performance degradation in several cases, such as periodic and random imbalances. We follow a control theoretical approach to this problem; more specifically, we propose a multiple-input multiple-output feedback linear quadratic regulation (LQR) controller, which captures the tradeoff between reaching a balanced state and the cost inherent in such adaptations. Our approach, apart from benefitting from and being characterized by a solid theoretical foundation, exhibits better performance than state-of-the-art heuristics in realistic situations, as verified by thorough evaluation.

Self-managing solutions have recently attracted a lot of interest from the database community. The need for self-* properties is more evident in distributed applications comprising heterogeneous and autonomous databases and functionality providers. Such resources are typically exposed as Web Services (WSs), which encapsulate remote DBMSs and functions called from within database queries. In this setting, database queries are over WSs, and the data transfer cost becomes the main bottleneck. To reduce this cost, data is shipped to and from WSs in chunks; however the optimum chunk size is volatile, depending on both the resources' runtime properties and the query. In this paper we propose a robust control theoretical solution to the problem of optimizing the data transfer in queries over WSs, by continuously tuning at runtime the block size and thus tracking the optimum point. Also, we develop online system identification mechanisms that are capable of estimating the optimum block size analytically. Both contributions are evaluated via both empirical experimentation in a real environment and simulations, and have been proved to be more effective and efficient than static solutions.

Nowadays, Web Services (WS) play an important role in the dissemination and distributed processing of large amounts of data that become available on the Web. In many cases, it is essential to retrieve and process such data in blocks, in order to benefit from pipelined parallelism and reduced communication costs. This article deals with the problem of minimizing at runtime, in a self-managing way, the total response time of a call to a database exposed to a volatile environment, like the Grid, as a WS. Typically, in this scenario, response time exhibits a concave, nonlinear behavior depending on the client-controlled size of the individual requests comprising a fixed size task. In addition, no accurate profiling or internal state information is available, and the optimum point is volatile. This situation is encountered in several systems, such as WS Management Systems (WSMS) for DBMS-like data management over wide area service-based networks, and the widely spread OGSA-DAI WS for accessing and integrating traditional DBMS. The main challenges in this problem apart from the unavailability of a model, include the presence of noise, which incurs local minima, the volatility of the environment, which results in moving optimum operating point, and the requirements for fast convergence to the optimal size of the request from the side of the client rather than of the server, and for low overshooting. Two solutions are presented in this work, which fall into the broader areas of runtime optimization and switching extremum control. They incorporate heuristics to avoid local optimal points, and address all the aforementioned challenges. The effectiveness of the solutions is verified via both empirical evaluation in real cases and simulations, which show that significant performance benefits can be provided rendering obsolete the need for detailed profiling of the WS.