Fault Detection of an Electrohydrostatic Actuator With the SVSF Time-Varying Boundary Layer Concept

2013 ◽  
Author(s):  
S. Andrew Gadsden ◽  
Saeid R. Habibi
Keyword(s):  
Boundary Layer ◽  
Sliding Mode ◽  
Variable Structure ◽  
Estimation Methods ◽  
Gear Pump ◽  
Time Varying ◽  
Aerospace Applications ◽  
Performance Metric ◽  
Efficient Type ◽  
Predictor Corrector

The electrohydrostatic actuator (EHA) is an efficient type of actuator commonly used in aerospace applications. It makes use of a closed hydraulic circuit, a number of control valves, an electric motor, and a fluid pump (usually a type of gear pump). The smooth variable structure filter (SVSF) is a relatively new estimation strategy based on sliding mode concepts formulated in a predictor-corrector fashion. The SVSF offers a number of advantages over other traditional estimation methods, including robust and stable estimates, and an additional performance metric. A fixed smoothing boundary layer was implemented in an effort to ensure stable estimates, and is defined based on the amount of uncertainties and noise present in the estimation process. Recent advances in SVSF theory include a time-varying smoothing boundary layer. This method, known as the SVSF-VBL, offers an optimal formulation of the SVSF as well as a method for detecting changes or faults in a system. This paper implements the SVSF-VBL in an effort to detect faults in an EHA. The results are compared with traditional Kalman filter-based methods.

State Estimation and Fault Detection of an Electrohydrostatic Actuator

2014 ◽  
Author(s):  
S. A. Gadsden ◽  
S. R. Habibi
Keyword(s):  
Fault Detection ◽  
State Estimation ◽  
Sliding Mode ◽  
Optimal Solution ◽  
Variable Structure ◽  
Operating Conditions ◽  
Linear Actuator ◽  
Gear Pump ◽  
Aerospace Applications ◽  
Estimation Problems

The electrohydrostatic actuator (EHA) is an efficient type of linear actuator commonly found in aerospace applications. It consists of an external gear pump (fluid), an electric motor, a closed hydraulic circuit, a number of control valves and ports, and a linear actuator. An EHA, built for experimentation, is studied in this paper. Two types of estimation strategies, the popular Kalman filter (KF) and the smooth variable structure filter (SVSF), are applied to the EHA for kinematic state and parameter estimation. The KF strategy yields the statistical optimal solution to linear estimation problems. However, the KF becomes unstable when strict assumptions are violated. The SVSF is an estimation strategy based on sliding mode concepts, which brings an inherent amount of stability to the estimation process. Recent advances in SVSF theory include a time-varying smoothing boundary layer. This method, known as the SVSF-VBL, offers an optimal formulation of the SVSF as well as a method for detecting changes or faults in a system. In addition to the application of the KF and SVSF for state estimation, the SVSF-VBL is applied to the EHA for the purposes of fault detection. The EHA is operated under various operating conditions (normal, friction fault, leakage fault, and so on), and the experimental results are presented and discussed.

On the Synchronization of a Class of Sprott Circuits Using Variable Structure Controller

2007 ◽  
Author(s):  
Kaveh Merat ◽  
Hoda Sadeghian ◽  
Hassan Salarieh ◽  
Aria Alasty
Keyword(s):  
Boundary Layer ◽  
Sliding Mode ◽  
External Disturbance ◽  
Variable Structure ◽  
Parametric Uncertainties ◽  
Time Varying ◽  
Sliding Surface ◽  
High Quality ◽  
Variable Boundary ◽  
Chaotic Circuits

In this paper the synchronization of a class of nonlinear chaotic circuits known as Sprott Circuits is studied. The Synchronization is obtained using a variable structure method based on sliding mode control with time varying sliding surface and variable boundary layer in presence of external disturbance and parametric uncertainties. The simulation is presented to show the effectiveness of this method. The results show the high quality and good performance of the method presented in the paper for synchronization of different drive-response chaotic Sprott circuits.

A Review of Smooth Variable Structure Filters: Recent Advances in Theory and Applications

2015 ◽  
Author(s):  
S. Andrew Gadsden ◽  
Hamed H. Afshari
Keyword(s):  
Review Paper ◽  
Sliding Mode ◽  
Variable Structure ◽  
Estimation Methods ◽  
Original Form ◽  
Estimation Technique ◽  
Modeling Errors ◽  
Recent Developments ◽  
Predictor Corrector ◽  
Smooth Variable Structure Filter

The smooth variable structure filter (SVSF) is a relatively new state and parameter estimation technique. Introduced in 2007, it is based on the sliding mode concept, and is formulated in a predictor-corrector fashion. The main advantages of the SVSF, over other estimation methods, are robustness to modeling errors and uncertainties, and its ability to detect system changes. Recent developments have looked at improving the SVSF from its original form. This review paper provides an overview of the SVSF, and summarizes the main advances in its theory.

scholarly journals Chattering-Free Single-Phase Robustness Sliding Mode Controller for Mismatched Uncertain Interconnected Systems with Unknown Time-Varying Delays

Energies ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 282 ◽  
Author(s):  
Cong-Trang Nguyen ◽  
Thanh Long Duong ◽  
Minh Quan Duong ◽  
Duc Tung Le
Keyword(s):  
Single Phase ◽  
Sliding Mode ◽  
Variable Structure Control ◽  
Variable Structure ◽  
Sliding Mode Controller ◽  
Time Varying ◽  
Structure Control ◽  
Time Varying Delay ◽  
Chattering Free ◽  
Varying Delay

Variable structure control with sliding mode can provide good control performance and excellent robustness. Unfortunately, the chattering phenomenon investigated due to discontinuous switching gain restricting their applications. In this paper, a chattering free improved variable structure control (IVSC) for a class of mismatched uncertain interconnected systems with an unknown time-varying delay is proposed. A sliding function is first established to eliminate the reaching phase in traditional variable structure control (TVSC). Next, a new reduced-order sliding mode estimator (ROSME) without time-varying delay is constructed to estimate all unmeasurable state variables of plants. Then, based on the Moore-Penrose inverse approach, a decentralized single-phase robustness sliding mode controller (DSPRSMC) is synthesized, which is independent of time delays. A DSPRSMC solves a complex interconnection problem with an unknown time-varying delay term and drives the system’s trajectories onto a switching surface from the initial time instance. Particularly, by applying the well-known Barbalat’s lemma, the chattering phenomenon in control input is alleviated. Moreover, a sufficient condition is established by using an appropriate Lyapunov theory and linear matrix inequality (LMI) method such that a sliding mode dynamics is asymptotically stable from the beginning time. Finally, a developed method is validated by numerical example with computer simulations.

scholarly journals Variable structure observer design for a class of uncertain systems with a time-varying delay

2012 ◽  
Vol 22 (3) ◽  
pp. 575-583 ◽  
Author(s):  
Wen-Jeng Liu
Keyword(s):  
Uncertain Systems ◽  
Sliding Mode ◽  
Variable Structure ◽  
Observer Design ◽  
State Feedback Control ◽  
Invariance Property ◽  
Time Varying ◽  
Time Varying Delay ◽  
Error System ◽  
Varying Delay

Abstract Design of a state observer is an important issue in control systems and signal processing. It is well known that it is difficult to obtain the desired properties of state feedback control if some or all of the system states cannot be directly measured. Moreover, the existence of a lumped perturbation and/or a time delay usually reduces the system performance or even produces an instability in the closed-loop system. Therefore, in this paper, a new Variable Structure Observer (VSO) is proposed for a class of uncertain systems subjected to a time varying delay and a lumped perturbation. Based on the strictly positive real concept, the stability of the equivalent error system is verified. Based on the generalized matrix inverse approach, the global reaching condition of the sliding mode of the error system is guaranteed. Also, the proposed variable structure observer will be shown to possess the invariance property in relation to the lumped perturbation, as the traditional variable structure controller does. Furthermore, two illustrative examples with a series of computer simulation studies are given to demonstrate the effectiveness of the proposed design method.

scholarly journals Neural-Fuzzy-Based Adaptive Sliding Mode Automatic Steering Control of Vision-based Unmanned Electric Vehicles

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Jinghua Guo ◽  
Keqiang Li ◽  
Jingjing Fan ◽  
Yugong Luo ◽  
Jingyao Wang
Keyword(s):  
Electric Vehicles ◽  
Sliding Mode ◽  
Variable Structure ◽  
Control Law ◽  
Time Varying ◽  
Steering Control ◽  
Structure Control ◽  
Adaptive Sliding Mode ◽  
Automatic Steering ◽  
Neural Fuzzy

AbstractThis paper presents a novel neural-fuzzy-based adaptive sliding mode automatic steering control strategy to improve the driving performance of vision-based unmanned electric vehicles with time-varying and uncertain parameters. Primarily, the kinematic and dynamic models which accurately express the steering behaviors of vehicles are constructed, and in which the relationship between the look-ahead time and vehicle velocity is revealed. Then, in order to overcome the external disturbances, parametric uncertainties and time-varying features of vehicles, a neural-fuzzy-based adaptive sliding mode automatic steering controller is proposed to supervise the lateral dynamic behavior of unmanned electric vehicles, which includes an equivalent control law and an adaptive variable structure control law. In this novel automatic steering control system of vehicles, a neural network system is utilized for approximating the switching control gain of variable structure control law, and a fuzzy inference system is presented to adjust the thickness of boundary layer in real-time. The stability of closed-loop neural-fuzzy-based adaptive sliding mode automatic steering control system is proven using the Lyapunov theory. Finally, the results illustrate that the presented control scheme has the excellent properties in term of error convergence and robustness.

scholarly journals Variable Structure Controller for Surface Ship Steering

2017 ◽  
Vol 2 (2) ◽  
Author(s):  
Ringo Rimbe ◽  
Raidandi Danwe ◽  
Babagana M Mustapha
Keyword(s):  
Sliding Mode ◽  
Linear Time ◽  
Variable Structure ◽  
Time Varying ◽  
Variable Structure Systems ◽  
Ship Model ◽  
Linear Time Invariant ◽  
Surface Ship ◽  
Ship Steering ◽  
Time Varying Environment

A Lyapunov approach to constructing switching surfaces for variable structure systems is investigated in this paper. The method guarantees sliding mode for any initial condition of the state vector and asymptotic stability is always achieved during sliding motion. An application for  the design of  a variable structure ship steering controller is carried out and  simulation results are presented. The designed controller exhibits robustness as applied to a linear time-invariant ship model and a time varying non-linear  ship model operating in  an uncertain and  time-varying environment.

scholarly journals An Improved Smooth Variable Structure Filter for Robust Target Tracking

2021 ◽  
Vol 13 (22) ◽  
pp. 4612
Author(s):  
Yu Chen ◽  
Luping Xu ◽  
Guangmin Wang ◽  
Bo Yan ◽  
Jingrong Sun
Keyword(s):  
Target Tracking ◽  
Sliding Mode ◽  
Sudden Change ◽  
Variable Structure ◽  
The State ◽  
Modeling Uncertainties ◽  
Modeling Errors ◽  
The Face ◽  
Predictor Corrector ◽  
Smooth Variable Structure Filter

As a new-style filter, the smooth variable structure filter (SVSF) has attracted significant interest. Based on the predictor-corrector method and sliding mode concept, the SVSF is more robust in the face of modeling errors and uncertainties compared to the Kalman filter. Since the estimation performance is usually insufficient in real cases where the measurement vector is of fewer dimensions than the state vector, an improved SVSF (ISVSF) is proposed by combining the existing SVSF with Bayesian theory. The ISVSF contains two steps: firstly, a preliminary estimation is performed by SVSF. Secondly, Bayesian formulas are adopted to improve the estimation for higher accuracy. The ISVSF shows high robustness in dealing with modeling uncertainties and noise. It is noticeable that ISVSF could deliver satisfying performance even if the state of the system is undergoing a sudden change. According to the simulation results of target tracking, the proposed ISVSF performance can be better than that obtained with existing filters.

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