Leonardo A. B. Tôrres

Comparison of Three Single-Phase PLL Algorithms for UPS Applications

In this paper, the performance assessment of three software single-phase phase-locked loop (PLL) algorithms is carried out by means of dynamic analysis and experimental results. Several line disturbances such as phase-angle jump, voltage sag, frequency step, and harmonics are generated by a DSP together with a D/A converter and applied to each PLL. The actual minus the estimated phase-angle values are displayed, providing a refined method for performance evaluation and comparison. Guidelines for parameters adjustments are also presented. In addition, practical implementation issues such as computational delay effects, ride-through, and computational load are addressed. The developed models proved to accurately represent the PLLs under real test conditions.

State estimation for linear and non-linear equality-constrained systems

This article addresses the state-estimation problem for linear and non-linear systems for the case in which prior knowledge is available in the form of an equality constraint. The equality-constrained Kalman filter (KF) is derived as the maximum-a-posteriori solution to the equality-constrained state-estimation problem for linear and Gaussian systems and is compared to alternative algorithms. Then, four novel algorithms for non-linear equality-constrained state estimation based on the unscented KF are presented, namely, the equality-constrained unscented KF, the projected unscented KF, the measurement-augmentation unscented KF, and the constrained unscented KF. Finally, these methods are compared on linear and non-linear examples.

New Stability Conditions Based on Piecewise Fuzzy Lyapunov Functions and Tensor Product Transformations

Improvements of recent stability conditions for continuous-time Takagi-Sugeno (T-S) fuzzy systems are proposed. The key idea is to bring together the so-called local transformations of membership functions and new piecewise fuzzy Lyapunov functions. By relying on these special local transformations, the associated linear matrix inequalities that are used to prove the systems stability can be relaxed without increasing the number of conditions. In addition, to enhance the usefulness of the proposed methodology, one can choose between two different sets of conditions characterized by independence or dependence on known bounds of the membership functions time derivatives. A standard example is presented to illustrate that the proposed method is able to provide substantial improvements in some cases.

Gain-Constrained Kalman Filtering for Linear and Nonlinear Systems

This paper considers the state-estimation problem with a constraint on the data-injection gain. Special cases of this problem include the enforcing of a linear equality constraint in the state vector, the enforcing of unbiased estimation for systems with unknown inputs, and simplification of the estimator structure for large-scale systems. Both the one-step gain-constrained Kalman predictor and the two-step gain-constrained Kalman filter are presented. The latter is extended to the nonlinear case, yielding the gain-constrained unscented Kalman filter. Two illustrative examples are presented.

State estimation for equality-constrained linear systems

We address the state-estimation problem for linear systems in a context where prior knowledge, in addition to the model and the measurements, is available in the form of an equality constraint. First, we investigate from where an equality constraint arises in a dynamic system. Then, the equality-constrained Kalman filter (ECKF) is derived as the solution to the equality-constrained state-estimation problem and compared to alternative algorithms. These methods are investigated in an example. In addition to exactly satisfying an equality constraint on the system, ECKF produce more accurate and more informative estimates than the unconstrained estimates.

Development of a Hand-Launched Small UAV for Ground Reconnaissance

The engineering design of a hand-launched, small unmanned aerial vehicle (SUAV), including guidance strategy and control design, together with real data from practical flight tests, is presented. The main goal in this work is the implementation of a low cost, portable, and reliable aerial platform for ground reconnaissance. The vehicle was specially designed so that the number of necessary sensors and actuators was reduced, without precluding the feasibility of the assigned mission.

UPS Parallel Balanced Operation Without Explicit Estimation of Reactive Power—A Simpler Scheme

A novel scheme of the well-known technique for parallel operation of uninterruptible power supply systems, namely frequency and voltage droop control method, is presented. The innovation relies on active power estimation, together with the use of a piecewise-continuous nonlinear function, in order to eliminate the need for costly cascaded low-pass filters usually employed to estimate the reactive power. Moreover, the whole scheme is derived in the time domain on a state-space approach. As a consequence, models that are lower order and more amenable to nonlinear analysis are obtained. Simulated results for two UPS parallel operation connected to a resistive load are presented. The results indicate that the novel technique leads to a proper load sharing.

Unscented filtering for interval-constrained nonlinear systems

This paper addresses the state-estimation problem for nonlinear systems with an interval constraint on the state vector. Approximate solutions to this problem are reviewed and compared with new algorithms, which are based on the unscented Kalman filter. An illustrative example is discussed.

Unscented filtering for equality-constrained nonlinear systems

This paper addresses the state-estimation problem for nonlinear systems in a context where prior knowledge, in addition to the model and the measurement data, is available in the form of an equality constraint. Three novel suboptimal algorithms based on the unscented Kalman filter are developed, namely, the equality-constrained unscented Kalman filter, the projected unscented Kalman filter, and the measurement-augmented unscented Kalman filter. These methods are compared on two examples: a quaternion-based attitude estimation problem and an idealized flow model involving conserved quantities.

Performance Evaluation of Attitude Estimation Algorithms in the Design of an AHRS for Fixed Wing UAVs

Three recently published attitude estimation algorithms are compared, and another one is proposed, aiming the performance evaluation when used in an attitude and heading reference system (AHRS), using low cost MEMS sensors, for fixed wing Unmanned Aerial Vehicles. The comparison is based on simulation results associated with typical aerial maneuvers of fixed wing UAVs, characterized by low frequency acceleration signals that are hard to be distinguished from intrinsic sensors biases, as it is usual during coordinated turns. The sensors models are also parameterized from information obtained in the respective datasheets, or obtained from experimental procedures, in order to better represent the imperfections present in practice. Three algorithms are based on the EKF (Extended Kalman Filter) and one is based on nonlinear complementary filtering. The results have revealed that some form of compensation for the effect of low frequency accelerations seems to be crucial to achieve good results.