Beshah Ayalew

Design and Development of

This paper presents the development of H∞ and μ-synthesis robust controllers for nanorobotic manipulation and grasping applications. Here a 3 DOF (Degrees Of Freedom) nanomanipulator with RRP (Revolute Revolute Prismatic) actuator arrangement is considered for nanomanipulation purposes. Due to the sophisticated complexity, and expected high level of accuracy and precision (of the order of 10−7 rad in revolute actuators and 0.25 nm in the prismatic actuator) of the nanomanipulator, there is a need to design a suitable controller to guarantee an accurate manipulation process. However, structure of the nanomanipulator employed here, namely MM3A, is such that the dynamic equations of motion of the nanomanipulator are highly nonlinear and complicated. Linearizing these dynamic equations of the nanomanipulator simplifies the controller design process significantly. However, linearization could suppress some critical information about the system dynamics. In order to achieve the precise motion of the nanomanipulator utilizing the simple linearized model, H∞ and μ-synthesis robust controller design approaches are proposed. Following the development of the controllers, numerical simulations of the proposed controllers on the nanomanipulator are used to verify the positioning performance.Copyright

Nonlinear Robust Observers for State-of-Charge Estimation of Lithium-Ion Cells Based on a Reduced Electrochemical Model

Advanced battery management systems rely on accurate cell- or module-level state-of-charge (SOC) information for effective control, monitoring, and diagnostics. Electrochemical models provide arguably the most accurate and detailed information about the SOC of lithium-ion cells. In this brief, two nonlinear observer designs are presented based on a reduced order electrochemical model. Both observers consist of a Luenberger term acting on nominal errors and a variable structure term for handling model uncertainty. Using Lyapunovs direct method, the design of the Luenberger term in each observer is formulated as a linear matrix inequality problem, whereas the variable structure term is designed assuming uncertainty bounds. Simulation and experimental studies are included to demonstrate the performance of the proposed observers.

Robust Fault Diagnosis for a Horizontal Axis Wind Turbine

Abstract This paper presents an H∞ optimization-based approach for the detection and isolation of faults in a horizontal axis wind turbine. The primary residuals are generated from separate parity equations for each of the blade pitch and drivetrain subsystems. Then, a robust secondary residual filtering scheme is developed to remove undesirable cross coupling in fault-residual pairings while suppressing the effects of the strong nonlinearity of the aerodynamic rotor torque-speed relationships, the effects of unmodelled dynamics and noise. Solutions are obtained using H∞ and μ-synthesis tools. Sensor, actuator and system/parameter faults are diagnosed using specified information about the nature of the faults as well as sensor redundancy. Results are included demonstrating the diagnosis of individual faults specified with the benchmark model for wind turbine fault detection and isolation.

Application of Topology, Size and Shape Optimization Methods in Polymer Metal Hybrid Structural Lightweight Engineering

Application of the engineering design optimization methods and tools to the design of automotive body‐in‐white (BIW) structural components made of polymer metal hybrid (PMH) materials is considered. Specifically, the use of topology optimization in identifying the optimal initial designs and the use of size and shape optimization techniques in defining the final designs is discussed. The optimization analyses employed were required to account for the fact that the BIW structural PMH component in question may be subjected to different in‐service loads be designed for stiffness, strength or buckling resistance and that it must be manufacturable using conventional injection over‐molding. The paper demonstrates the use of various engineering tools, i.e. a CAD program to create the solid model of the PMH component, a meshing program to ensure mesh matching across the polymer/metal interfaces, a linear‐static analysis based topology optimization tool to generate an initial design, a nonlinear statics‐based size and shape optimization program to obtained the final design and a mold‐filling simulation tool to validate manufacturability of the PMH component.

A Comparative Study of Three Fault Diagnosis Schemes for Wind Turbines

In wind turbine systems, early diagnosis and accommodation of faults are crucial for the reliable and cost effective operation of wind turbines and their success as viable renewable energy conversion solutions. This paper proposes and compares three different diagnostic schemes that address the issue of fault detection and isolation for the drivetrain and generator-converter subsystems of a wind turbine. The first diagnostic scheme is based on a cascade of two Kalman filters intended to alleviate the effect of the nonlinear aerodynamic torque generation in the drivetrain dynamics. The second scheme uses a bank of dedicated observers, each of which exploits Thaus argument for systems featuring nonlinear static feedback. The third scheme is a secondary H∞ filtering mechanism constructed from parity equations by treating the nonlinearity as bounded uncertainty. The performance of each scheme is demonstrated using simulations of the wind turbine system. Robustness of the schemes has been analyzed in terms of parametric uncertainties and different operating conditions. A detailed comparison is also presented pointing to the positive and negative aspects of each scheme.

Nonlinear Adaptive Observer for a Lithium-Ion Battery Cell Based on Coupled Electrochemical-Thermal Model

Real-time estimation of battery internal states and physical parameters is of the utmost importance for intelligent battery management systems (BMS). Electrochemical models, derived from the principles of electrochemistry, are arguably more accurate in capturing the physical mechanism of the battery cells than their counterpart data-driven or equivalent circuit models (ECM). Moreover, the electrochemical phenomena inside the battery cells are coupled with the thermal dynamics of the cells. Therefore, consideration of the coupling between electrochemical and thermal dynamics inside the battery cell can be potentially advantageous for improving the accuracy of the estimation. In this paper, a nonlinear adaptive observer scheme is developed based on a coupled electrochemical–thermal model of a Li-ion battery cell. The proposed adaptive observer scheme estimates the distributed Li-ion concentration and temperature states inside the electrode, and some of the electrochemical model parameters, simultaneously. These states and parameters determine the state of charge (SOC) and state of health (SOH) of the battery cell. The adaptive scheme is split into two separate but coupled observers, which simplifies the design and gain tuning procedures. The design relies on a Lyapunovs stability analysis of the observers, which guarantees the convergence of the combined state-parameter estimates. To validate the effectiveness of the scheme, both simulation and experimental studies are performed. The results show that the adaptive scheme is able to estimate the desired variables with reasonable accuracy. Finally, some scenarios are described where the performance of the scheme degrades.

Nonlinear observer designs for state-of-charge estimation of Lithium-ion batteries

State-of-Charge (SOC) information is very crucial for the control, diagnostics and monitoring of Li-ion cells/batteries. Compared to conventional data-driven or equivalent circuit models often employed in battery management systems, electrochemical battery models have the potential to give physically accurate the SOC information by tracking the Li-ion concentration in each electrode. In this paper, two nonlinear observer designs are presented to estimate Li-ion battery State-of-Charge based on reductions of an electrochemical model. The first observer design uses a constant gain Luenberger structure whereas the second one improves it by weighing the gain with the output Jacobian. For both observer designs, Lyapunovs direct method is applied and the design problems are converted to solving LMIs. Simulation results are included to demonstrate the effectiveness of both observer designs.

Sensor Fault Detection, Isolation, and Estimation in Lithium-Ion Batteries

In battery management systems (BMSs), real-time diagnosis of sensor faults is critical for ensuring the safety and reliability of the battery. For example, a current sensor fault leads to erroneous estimates of state of charge and other parameters, which in turn affects the control actions in the BMS. A temperature sensor fault may lead to ineffective thermal management. In this brief, a model-based diagnostic scheme is presented that uses sliding mode observers designed based on the electrical and thermal dynamics of the battery. It is analytically shown how the extraction of the equivalent output error injection signals on the sliding manifolds enables the detection, the isolation, as well as the estimation of the temperature, voltage, and current sensor faults. This brief includes simulation and experimental studies to demonstrate and evaluate the effectiveness of the proposed scheme. Discussions are also included on the effects of uncertainty and on threshold design.

Robotic automotive paint curing using thermal signature feedback

Purpose – The purpose of this paper is to present a new closed‐loop radiative robotic paint curing process that could replace less efficient and bulky convection‐based paint curing processes in automotive manufacturing.Design/methodology/approach – The proposed robotic paint curing processes uses an Ultraviolet LED panel for a heat source, an infra‐red camera for non‐contact thermal signature feedback of cure level, and a robot control strategy that incorporates the cure‐level information in an inverse dynamics control of the robotic manipulator. To demonstrate the advantage of the closed‐loop process in improving cure uniformity, detailed models and discussions of the irradiation process, the robotics and the control strategy are presented.Findings – A simulation‐based comparison of the closed‐loop robotic curing with the open‐loop robotic curing clearly shows the benefits of using thermal signature feedback in improving cure level uniformity.Originality/value – This is a new approach proposed to exploit...

Control-oriented MIMO modeling of laser-aided powder deposition processes

This paper proposes a control-oriented multiple input multiple output (MIMO) model for a class of laser aided powder deposition (LAPD) processes. First, the various components of a multi-physics model of LAPD processes are briefly reviewed including the laser-powder interaction, heat transfer with phase change, fluid flow and surface deformation. The difficulty of capturing these nonlinear, coupled, spatio-temporal multi-physical interactions via lumped parameter modeling is highlighted. Then, a new MIMO model is derived in Hammerstein form by concatenating a linearized dynamics with coupled nonlinear relationships derived from mass and heat balance considerations. This MIMO model captures the coupled dynamics with laser power and scanning speed as inputs and deposited layer height and melting pool temperature as outputs. To identify the unknown model parameters, a constrained optimization problem is solved using the detailed multi-physics models. The MIMO model is in a form suitable for multivariable control designs for LAPD processes.