Nonlinear Control of a Flexible Rotor Magnetic Bearing System: Robustness and the Indefinate Model

Previously published control strategies for magnetic bearings primarily focus on linear optimal control techniques. While these methods afford many advantages, conspicuously absent from the literature are detailed attempts at nonlinear control. Here, we obtain the equations of motion of an overhung flexible rotor supported in magnetic bearings with two different levels of model sophistication. We derive a generic nonlinear controller in the manner of feedback linearization, and compare the eigenanalysis and transient response of the two rotor models under the action of this >perfect model> controller. We then proceed to obtain a robust nonlinear controller through the sliding mode technique and demonstrate that robustness by implementing it on an uncertain model.Center for Electromechanic

Technical report on Optimization-Based Bearing-Only Visual Homing with Applications to a 2-D Unicycle Model

We consider the problem of bearing-based visual homing: Given a mobile robot which can measure bearing directions with respect to known landmarks, the goal is to guide the robot toward a desired "home" location. We propose a control law based on the gradient field of a Lyapunov function, and give sufficient conditions for global convergence. We show that the well-known Average Landmark Vector method (for which no convergence proof was known) can be obtained as a particular case of our framework. We then derive a sliding mode control law for a unicycle model which follows this gradient field. Both controllers do not depend on range information. Finally, we also show how our framework can be used to characterize the sensitivity of a home location with respect to noise in the specified bearings. This is an extended version of the conference paper [1].Comment: This is an extender version of R. Tron and K. Daniilidis, "An optimization approach to bearing-only visual homing with applications to a 2-D unicycle model," in IEEE International Conference on Robotics and Automation, 2014, containing additional proof

Challenges in the Development of Micro Gas Turbines for Concentrated Solar Power Systems

Parabolic solar dish systems have gained more interest recently as a reliable way for harnessing the solar power in form of electricity. Micro gas turbines can be usedas engines in such system to convert the heat available from the solar collector o electricity. In this paper the technical challenges related to using micro gas turbines for utilising concentrated solar power will be addressed based on the experience gained from the EU funded project OMSoP (Optimised Microturbine Solar Power system) which aims todevelop and demonstrate a micro gas turbine coupled to a parabolic dish for the power range of 5–10 kW. The technical challenges related to the turbomachinery design, rotordynamics and dynamic stability, control system, power electronics and thermal storage will be briefly reviewed. Techno economic considerations of the system will also be discussed

On-line multiobjective automatic control system generation by evolutionary algorithms

Evolutionary algorithms are applied to the on- line generation of servo-motor control systems. In this paper, the evolving population of controllers is evaluated at run-time via hardware in the loop, rather than on a simulated model. Disturbances are also introduced at run-time in order to pro- duce robust performance. Multiobjective optimisation of both PI and Fuzzy Logic controllers is considered. Finally an on-line implementation of Genetic Programming is presented based around the Simulink standard blockset. The on-line designed controllers are shown to be robust to both system noise and ex- ternal disturbances while still demonstrating excellent steady- state and dvnamic characteristics

Application of probabilistic PCR5 Fusion Rule for Multisensor Target Tracking

This paper defines and implements a non-Bayesian fusion rule for combining densities of probabilities estimated by local (non-linear) filters for tracking a moving target by passive sensors. This rule is the restriction to a strict probabilistic paradigm of the recent and efficient Proportional Conflict Redistribution rule no 5 (PCR5) developed in the DSmT framework for fusing basic belief assignments. A sampling method for probabilistic PCR5 (p-PCR5) is defined. It is shown that p-PCR5 is more robust to an erroneous modeling and allows to keep the modes of local densities and preserve as much as possible the whole information inherent to each densities to combine. In particular, p-PCR5 is able of maintaining multiple hypotheses/modes after fusion, when the hypotheses are too distant in regards to their deviations. This new p-PCR5 rule has been tested on a simple example of distributed non-linear filtering application to show the interest of such approach for future developments. The non-linear distributed filter is implemented through a basic particles filtering technique. The results obtained in our simulations show the ability of this p-PCR5-based filter to track the target even when the models are not well consistent in regards to the initialization and real cinematic

Design and application of electromechanical actuators for deep space missions

During the period 8/16/92 through 2/15/93, work has been focused on three major topics: (1) screw modeling and testing; (2) motor selection; and (3) health monitoring and fault diagnosis. Detailed theoretical analysis has been performed to specify a full dynamic model for the roller screw. A test stand has been designed for model parameter estimation and screw testing. In addition, the test stand is expected to be used to perform a study on transverse screw loading

Adaptive Multiscale Weighted Permutation Entropy for Rolling Bearing Fault Diagnosis

© 2020 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.Bearing vibration signals contain non-linear and non-stationary features due to instantaneous variations in the operation of rotating machinery. It is important to characterize and analyze the complexity change of the bearing vibration signals so that bearing health conditions can be accurately identified. Entropy measures are non-linear indicators that are applicable to the time series complexity analysis for machine fault diagnosis. In this paper, an improved entropy measure, termed Adaptive Multiscale Weighted Permutation Entropy (AMWPE), is proposed. Then, a new rolling bearing fault diagnosis method is developed based on the AMWPE and multi-class SVM. For comparison, experimental bearing data are analyzed using the AMWPE, compared with the conventional entropy measures, where a multi-class SVM is adopted for fault type classification. Moreover, the robustness of different entropy measures is further studied for the analysis of noisy signals with various Signal-to-Noise Ratios (SNRs). The experimental results have demonstrated the effectiveness of the proposed method in fault diagnosis of rolling bearing under different fault types, severity degrees, and SNR levels.Peer reviewedFinal Accepted Versio

An improved robot for bridge inspection

This paper presents a significant improvement from the previous submission from the same authors at ISARC 2016. The robot is now equipped with low-cost cameras and a 2D laser scanner which is used to monitor and survey a bridge bearing. The robot is capable of localising by combining a data from a pre-surveyed 3D model of the space with real-time data collection in-situ. Autonomous navigation is also performed using the 2D laser scanner in a mapped environment. The Robot Operating System (ROS) framework is used to integrate data collection and communication for navigation