Saeid Habibi

Design of a new high-performance electrohydraulic actuator

This paper describes the design and prototyping of a new high-performance actuation system that combines the benefits of conventional hydraulic systems and direct-drive electrical actuators, namely high torque/mass ratio and modularity. It is referred to as the electrohydraulic actuator (EHA) and results from the fusion of the above-mentioned technologies. The EHA is a unique device with its own characteristics and requires hydraulic components that are specifically tailored to its needs and requirements. Based on a mathematical model of the EHA, the requirements for its components are determined. These requirements are used as a basis for component selection, component modification, and design of a customized new symmetrical linear actuator. The analysis of the EHA presented is supported by experimental data and explains the extremely high level of performance attained by a prototype of the EHA.

The Smooth Variable Structure Filter

In this paper, a new method for state estimation, referred to as the smooth variable structure filter (SVSF), is presented. The SVSF method is model based and applies to smooth nonlinear dynamic systems. It allows for the explicit definition of the source of uncertainty and can guarantee stability given an upper bound for uncertainties and noise levels. The performance of the SVSF improves with more refined definition of upper bounds on parameter variations or uncertainties. Furthermore, most filtering methods provide as their measure of performance the filter innovation vector or (output) estimation error. However in addition to the innovation vector, the SVSF has a secondary set of performance indicators that correlate to the modeling errors specific to each state or parameter that is being estimated. The combined robustness and multiple indicators of performance allow for dynamic refinement of internal models in the SVSF. Dynamic refinement and robustness are features that are particularly advantageous in fault diagnosis and prediction. In this paper, the applications of the SVSF to linear and nonlinear systems, including one pertaining to fault detection, are provided. The characteristics of this filter in terms of its accuracy and rate of convergence are discussed.

The Variable Structure Filter

This paper presents a new strategy for state estimation. The strategy may be applied to linear systems and is referred to as the variable structure filter. The filter is considered for discrete-time systems subject to random disturbances and measurement noise. It requires a parametric model and can be formulated to accommodate modeling uncertainties. A proof of stability for the filter is provided. For stability this concept requires a specification of an upper bound for uncertainties, disturbances, and measurement noise. The application of this filter to a third-order linear system is demonstrated.

Parameter Identification for a High-Performance Hydrostatic Actuation System Using the Variable Structure Filter Concept

Parameter estimation is an important concept that can be used for health and condition monitoring. Estimation or measurement of physically meaningful parameters and their evaluation against predetermined thresholds allows detection of gradual or abrupt deteriorations in the plant. This early detection of faults enables preventative unscheduled maintenance that is of benefit to industries concerned with reliability and safety. In this paper, a recently proposed state estimation strategy referred to as the smooth variable structure filter (SVSF) is reviewed and extended to parameter estimation. The SVSF is applied to a novel hydrostatic actuation system referred to as the electrohydraulic actuator (EHA). Condition monitoring of the EHA for preventative unscheduled maintenance would increase its safety in applications pertaining to aerospace and would reduce its operational and maintenance costs.

Design of a new high performance electrohydraulic actuator

This paper describes the design and prototyping of a new high performance actuation system that combines the benefits of conventional hydraulic systems and direct drive electrical actuators, namely high torque/mass ratio and modularity. It is referred to as the electrohydraulic actuator (EHA) and results from the fusion of the above mentioned technologies. EHA is a unique device with its own characteristics and requires hydraulic components that are specifically tailored to its needs and requirements. Based on a mathematical model of EHA, the requirements for its components are determined. These requirements are used as a basis for component selection, component modification, and design of a customized new symmetrical linear actuator. The analysis of EHA is supported by experimental data, and explains the extremely high level of performance attained by a prototype of EHA.

A new form of the smooth variable structure filter with a covariance derivation

State and parameter estimation is important for the control of systems, particularly when not all of the system information is available for the designer. Filters are used to extract state information from measurements, which are typically corrupted by noise. A common measure of the performance of an estimate by a filter is through the use of a covariance matrix. This essentially provides a measure of the error in the estimate. Furthermore, knowledge of this covariance can lead to a more accurate derivation and greater number of applications for the filter. Introduced in 2007, the smooth variable structure filter (SVSF) is a relatively new filter. It is a predictor-correct estimator based on sliding mode control and estimation. In its current form, the SVSF is not a classical filter in the sense that it does not have a covariance matrix. This paper introduces the SVSF in a new form without affecting its original proof of stability, and outlines the derivation of a covariance matrix that can be used for comparative purposes as well as other applications. A linear mechanical system referred to as an electrohydrostatic actuator (EHA) is used to numerically demonstrate the new SVSF. The results are compared with the classical Kalman filter (KF), which is the most common and efficient filtering strategy for linear systems.

Derivation of Design Requirements for Optimization of a High Performance Hydrostatic Actuation System

Abstract The competitive global market dictates greater quality of product models produced at lower cost and in shorter duration. During the past two decades, the efficiency of production processes and the quality of products have been differentiating factors in establishing competitive advantage in mature industries such as fluid power. The survival of such industries is increasingly dependent on their ability of optimizing their component characteristics as well as integrating these in complex subsystems. Reduction of cost of poor quality is thus critical. This cost often originates from inadequate or sub-optimal design requirements. Mature industries involved in the design and production of complex systems, have recognized the importance of design requirements definition in reducing cost and increasing profitability. This paper considers linking of system requirements to design parameters for a high performance actuation system referred to as the Electro Hydraulic Actuator (EHA). EHA is based on the hydrostatic actuation concept. It has been prototyped and has demonstrated a very high level of performance. The mathematical model of EHA is reviewed and used for linking its performance to its design parameters through a set of mathematical functions. The actual and expected performances of the prototype are compared in order to validate the proposed mathematical functions and an improved design is proposed.

Reduced-Order Electrochemical Model Parameters Identification and SOC Estimation for Healthy and Aged Li-Ion Batteries Part I: Parameterization Model Development for Healthy Batteries

The current phase in our transportation system represents a paradigm shift from conventional, fossil-fuel-based vehicles into the second-generation electric and hybrid vehicles. Electric vehicles (EVs) provide numerous advantages compared with conventional vehicles because they are more efficient, sustainable, greener, and cleaner. The commercial market penetration and success of EVs depend on the efficiency, safety, cost, and lifetime of the traction battery pack. One of the current key electrification challenges is to accurately estimate the battery pack state of charge (SOC) and state of health (SOH), and therefore provide an estimate of the remaining driving range at various battery states of life. To estimate the battery SOC, a high-fidelity battery model along with a robust, accurate estimation strategy is necessary. This paper provides three main contributions: 1) introducing a new SOC parameterization strategy and employing it in setting up optimizer constraints to estimate battery parameters; 2) identification of the full-set of the reduced-order electrochemical battery model parameters by using noninvasive genetic algorithm optimization on a fresh battery; and 3) model validation by using real-world driving cycles. Extensive tests have been conducted on lithium iron phosphate-based cells widely used in high-power automotive applications. Models can be effectively used onboard of battery management system.

Novel Model-Based Estimators for the Purposes of Fault Detection and Diagnosis

The interacting multiple model (IMM) strategy is particularly useful for systems that behave according to a number of different operating modes. In this strategy, each operating mode is described by a model and has its own filter. The filters are run in parallel, and an overall operating mode probability is calculated that provides an indication of the current operating regime of the system. The smooth variable structure filter (SVSF) is a relatively new estimation method based on the sliding mode concept, formulated in a predictor-corrector form. For systems with modeling uncertainties, the SVSF has shown to be more accurate and robust when compared with other methods such as the extended Kalman filter (EKF). A newer form of the SVSF makes use of a time-varying smoothing boundary layer (SVSF-VBL). This paper introduces new model-based estimators; based on the IMM strategy combined with the SVSF and SVSF-VBL, referred to as the IMM-SVSF and IMM-SVSF-VBL, respectively. The new strategies are applied to a type of aerospace actuator referred to as an electrohydrostatic actuator, which provides a comprehensive system for fault detection and diagnosis. The results are compared with the popular IMM-EKF strategy.

Automotive Internal-Combustion-Engine Fault Detection and Classification Using Artificial Neural Network Techniques

In this paper, an engine fault detection and classification technique using vibration data in the crank angle domain is presented. These data are used in conjunction with artificial neural networks (ANNs), which are applied to detect faults in a four-stroke gasoline engine built for experimentation. A comparative study is provided between the popular backpropagation (BP) method, the Levenberg-Marquardt (LM) method, the quasi-Newton (QN) method, the extended Kalman filter (EKF), and the smooth variable structure filter (SVSF). The SVSF is a relatively new estimation strategy, based on the sliding mode concept. It has been formulated to efficiently train ANNs and is consequently referred to as the SVSF-ANN. The accuracy of the proposed method is compared with the standard accuracy of the Kalman-based filters and the popular BP algorithms in an effort to validate the SVSF-ANN performance and application to engine fault detection and classification. The customizable fault diagnostic system is able to detect known engine faults with various degrees of severity, such as defective lash adjuster, piston chirp (PC), and chain tensioner (CT) problems. The technique can be used at any dealership or assembly plant to considerably reduce warranty costs for the company and manufacturer.