Tohid Alizadeh

Cluster-head based feedback for simplified time reversal prefiltering in ultra-wideband systems

Abstract Time-reversal prefiltering (TRP) technique for impulse radio (IR) ultra wide-band (UWB) systems requires a large amount of feedback to transmit the channel impulse response from the receiver to the transmitter. In this paper, we propose a new feedback design based on vector quantization. We use a machine learning algorithm to cluster the estimated channels into several groups and to select the channel cluster heads (CCHs) for feedback. In particular, CCHs and their labels are recorded at both side of the UWB transceivers and the label of the most similar CCH to the estimated channel is fed back to the transmitter. Finally, the TRP is applied using the feedback CCH. The proposed digital feedback provides three main advantages: (1) it significantly reduces the dedicated bandwidth required for feedback; (2) it considerably improves the speed of transceivers; and, (3) it is robust to noise in the feedback channel since few bytes are required to send the codes that can be heavily error protected. Numerical results on standard UWB channel models are discussed, showing the advantage of the proposed solution.

Simultaneous total harmonic distortion minimization and selective harmonic elimination: Combining the best of both worlds

Power electronic converters for medium and high power / voltage applications employ low frequency fundamental switching that is dictated by the necessity to minimize switching losses. There are two major approaches to switching angles determination - Total Harmonic Distortion (THD) minimization and Selective Harmonic Elimination (SHE). For the THD minimization, the switching angles are found so as to assure minimum THD without taking any care of frequency spectrum. For the SHE approach, all degrees of freedom due to available amount of switching angles are spent on elimination of certain low order harmonics. The basic idea of the suggested combined approach consists in spending some degrees of freedom on certain harmonics elimination and using the rest for minimizing THD. This generalized problem formulation includes classic minimal THD and SHE problems as special cases.

Robot programming by demonstration of multiple tasks within a common environment

Most of the available robot programming by demonstration (PbD) approaches focus on learning a single task, in a given environmental situation. In this paper, we propose to learn multiple tasks together, within a common environment, using one of the available PbD approaches. Task-parameterized Gaussian mixture model (TP-GMM) is used at the core of the proposed approach. A database of TP-GMMs will be constructed for the tasks, and it will be used to provide the reproduction when needed. The environment will be shared between different tasks, in other words, all the available objects will be considered as external task parameters (TPs), as they may modulate the task. During the learning part, the relevance of the task parameters will be extracted for each task, and the information will be stored together with the parameters of the corresponding updated TP-GMM. For reproduction, the end user will specify the task and the robot will be able to pick the relevant TP-GMM and the relevant task parameters and reproduce the movement. The proposed approach is tested both in simulation and using a robotic experiment.

Identifying the relevant frames of reference in programming by demonstration using task-parameterized Gaussian mixture regression

Automatic identification of the relevant frames of references (or external task parameters) in programming by demonstration using the task-parameterized Gaussian mixture regression (TP-GMM) is addressed in this paper. While performing a given task, there may be several external task parameters, some of which are relevant to the specific task, while some others are not relevant. Identifying the irrelevant task parameters could help to automatize the selection of task parameters, construct a more compact model of the task and achieve better performances in the reproduction phase. At first, all the potential candidate frames of references will be taken into account, then, after identifying the irrelevant ones, they will be removed from the model, and only the relevant frames will be used for the reproduction. The performance of the approach is testified through an experiment, where the reproduction with only the relevant frames of references provides much better results compared to the case of including all candidate frames of references.

Simultaneous Selective Harmonic Elimination and THD Minimization for a Single-Phase Multilevel Inverter With Staircase Modulation

Power electronic converters for medium- and high-power/voltage applications employ low-frequency fundamental switching frequencies and, therefore, minimize switching losses. There are two main control techniques of switching angle estimation; total harmonic distortion (THD) minimization and selective harmonic elimination (SHE). In case of the first control technique, the switching angles are found based on THD minimization without being specifically focused on eliminating some exact harmonics. For the SHE control technique, all degrees of freedom, due to available number of switching angles, is used for the elimination of certain low-order harmonics. The basic idea of combining those two control techniques consists of using some degrees of freedom for eliminating certain harmonics and using the others for minimizing the remaining THD content. This generalized problem formulation includes classic minimal THD and SHE problems as special cases. Theoretical developments are verified by the set of experimental cases for the voltage and current THDs selecting characteristic working points over the modulation index range.

Learning from demonstration with partially observable task parameters using dynamic movement primitives and Gaussian process regression

The problem of learning from demonstration in the case of partially observable external task parameters is addressed in this paper. Such a situation could be present in the daily life scenarios, where information regarding some task parameters are missing or partially available. In the first step, one dynamic movement primitives (DMP) model is learned for each demonstration trajectory. The parameters of the learned DMP model are recorded together with the corresponding external task parameters (query points), to create a database. Then Gaussian process regression (GPR) is used to create a model for the external task parameters and the DMP parameters. During reproduction, DMP parameters are retrieved by providing the new external task parameters and are used to regenerate the trajectory. It is shown how the learning approach could be adapted to deal with the partially observable external task parameters and regenerate the proper trajectory. The proposed methodology is applied to learn a via-point passing experiment with a lightweight robot manipulator (KUKA robot) to illustrate the efficacy of the proposed approach.

A sensorless MPPT-based solar tracking control approach for mobile autonomous systems

This paper presents a new approach to the solar tracking control. Today several methods are used to minimize the angle of incidence between the incoming sunlight and the photovoltaic (PV) panel. Most of them require optical solar sensor(s) to maximize energy output. There are approaches where geolocation data and the timetable of the Suns position support a solar tracking mechanism. This paper proposes a novel sensorless (no optical or geo positioning sensors required) solar tracking mechanism based on a maximum power point tracking (MPPT) control for a mobile autonomous PV system. Such a system would provide an automatic PV panel position adjustment towards the Sun using a sensorless 3-DOF solar tracker system with a MPPT based control.

Reverse engineering of RLC circuits using Matlab: An experiment for electrical circuits course

Identifying the contents of a black-box electrical circuit is a challenging experiment. In this paper, we present an approach for identifying the topological structure of the circuit and estimating the values of the internal components, by applying input signals and measuring available signals. The black-box model is provided as a Simulink model, whose contents are not accessible to the students. The overall procedure is performed in Matlab/Simulink environment and the results are obtained for a given circuit and compared with the actual values. The experiment is performed by a group of undergraduate students and the assessment results show its effectiveness in challenging their knowledge.

Fault tolerant control of electromagnetic suspension system with simultaneous sensor and actuator faults

In electromagnetic suspension (EMS) systems, faults in the sensors and actuators might result in instability in the system, and could even lead to catastrophic system failures. Thus, it is essential to improve the system reliability and safety. However, since EMS systems are highly nonlinear and open-loop unstable, such systems require further investigation for fault tolerant performance. Therefore, in order to preserve the stability of EMS system after occurring sensor and actuator fault, a fault tolerant controller using virtual sensor/actuator approach is investigated in this work. Control scheme has been verified by simulating a single-axis two-magnet suspension system subjected to failures of the sensor and actuator.