Iterative Smooth Variable Structure Filter for Parameter Estimation

2011 ◽  
Vol 2011 ◽  
pp. 1-18 ◽  
Author(s):  
Mohammad Al-Shabi ◽  
Saeid Habibi
Keyword(s):  
Parameter Estimation ◽  
Sliding Mode ◽  
Estimation Method ◽  
Variable Structure ◽  
Model Parameters ◽  
Modeling Uncertainties ◽  
Control Concept ◽  
Chattering Effect ◽  
Combined Strategy ◽  
Smooth Variable Structure Filter

The smooth variable structure filter (SVSF) is a recently proposed predictor-corrector filter for state and parameter estimation. The SVSF is based on the sliding mode control concept. It defines a hyperplane in terms of the state trajectory and then applies a discontinuous corrective action that forces the estimate to go back and forth across that hyperplane. The SVSF is robust and stable to modeling uncertainties making it suitable for fault detection application. The discontinuous action of the SVSF results in a chattering effect that can be used to correct modeling errors and uncertainties in conjunction with adaptive strategies. In this paper, the SVSF is complemented with a novel parameter estimation technique referred to as the iterative bi-section/shooting method (IBSS). This combined strategy is used for estimating model parameters and states for systems in which only the model structure is known. This combination improves the performance of the SVSF in terms of rate of convergence, robustness, and stability. The benefits of the proposed estimation method are demonstrated by its application to an electrohydrostatic actuator.

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.

scholarly journals A Novel Smooth Variable Structure Smoother for Robust Estimation

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1781
Author(s):  
Yu Chen ◽  
Luping Xu ◽  
Bo Yan ◽  
Cong Li
Keyword(s):  
Sliding Mode ◽  
Variable Structure ◽  
Estimation Accuracy ◽  
Maneuvering Target Tracking ◽  
State Estimate ◽  
Maneuvering Target ◽  
Modeling Uncertainties ◽  
New Type ◽  
Gaussian System ◽  
Smooth Variable Structure Filter

The smooth variable structure filter (SVSF) is a new-type filter based on the sliding-mode concepts and has good stability and robustness in overcoming the modeling uncertainties and errors. However, SVSF is insufficient to suppress Gaussian noise. A novel smooth variable structure smoother (SVSS) based on SVSF is presented here, which mainly focuses on this drawback and improves the SVSF estimation accuracy of the system. The estimation of the linear Gaussian system state based on SVSS is divided into two steps: Firstly, the SVSF state estimate and covariance are computed during the forward pass in time. Then, the smoothed state estimate is computed during the backward pass by using the innovation of the measured values and covariance estimate matrix. According to the simulation results with respect to the maneuvering target tracking, SVSS has a better performance compared with another smoother based on SVSF and the Kalman smoother in different tracking scenarios. Therefore, the SVSS proposed in this paper could be widely applied in the field of state estimation in dynamic system.

Parameter Estimation Using a Combined Variable Structure and Kalman Filtering Approach

2008 ◽  
Vol 130 (5) ◽  
Author(s):  
Saeid Habibi
Keyword(s):  
Neural Network ◽  
Parameter Estimation ◽  
Estimation Error ◽  
Estimation Method ◽  
Variable Structure ◽  
Network Adaptation ◽  
Estimation Process ◽  
Multiple Indicators ◽  
Modeling Uncertainties

A method that is often used for parameter estimation is the extended Kalman filter (EKF). EKF is a model-based strategy that implicitly considers the effect of modeling uncertainties. This implicit consideration often leads to the tuning of the filter by trial and error. When formulated for parameter estimation, the “tuned” EKF becomes sensitive to uncertainties in its internal model. The EKF’s robustness can be improved by combining it with the recently proposed variable structure filter (VSF) concept. In a combined form, the modeling uncertainties no longer affect stability, but impact the performance and the quality of the estimation process. Furthermore, the VSF concept provides a secondary set of indicators of performance that is in addition to the estimation error and that pertains to the range of parametric uncertainties. As such, the robustness of the combined method and its multiple indicators of performance allow the use of intelligent adaptation for improving the performance of the estimation process. For real-time applications, online neural network adaptation may be used to improve the performance by progressively reducing specific modeling uncertainties in the system. In this paper, a new parameter estimation method that uses concepts associated with the EKF, the VSF, and neural network adaptation is introduced. The performance of this method is considered and discussed for applications that involve parameter estimation such as fault detection.

Genetic Algorithm-Based Maximum Likelihood Parameter Estimation for Transit Buses

2001 ◽  
Author(s):  
Jie Xiao ◽  
Bohdan T. Kulakowski
Keyword(s):  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Dynamic Models ◽  
Estimation Method ◽  
Optimization Techniques ◽  
Model Parameters ◽  
Parameter Estimation Method ◽  
Transit Buses ◽  
Simulation And Control ◽  
And Control

Abstract Vehicle dynamic models include parameters that qualify the dependence of input forces and moments on state and control variables. The accuracy of the model parameter estimates is important for modeling, simulation, and control. In general, the most accurate method for determining values of model parameters is by direct measurement. However, some parameters of vehicle dynamics, such as suspension damping or moments of inertia, are difficult to measure accurately. This study aims at establishing an efficient and accurate parameter estimation method for developing dynamic models for transit buses, such that this method can be easily implemented for simulation and control design purposes. Based on the analysis of robustness, as well as accuracy and efficiency of optimization techniques, a parameter estimation method that integrates Genetic Algorithms and the Maximum Likelihood Estimation is proposed. Choices of output signals and estimation criterion are discussed involving an extensive sensitivity analysis of the predicted output with respect to model parameters. Other experiment-related aspects, such as imperfection of data acquisition, are also considered. Finally, asymptotic Cramer-Rao lower bounds for the covariance of estimated parameters are obtained. Computer simulation results show that the proposed method is superior to gradient-based methods in accuracy, as well as robustness to the initial guesses and measurement uncertainty.

scholarly journals Suitability of Several Statistical Models to Simulate Observed Distribution of Sample Test Results in Inspections of Aflatoxin-Contaminated Peanut Lots

1996 ◽  
Vol 79 (4) ◽  
pp. 981-988 ◽  
Author(s):  
Thomas Whitaker ◽  
Francis Giesbrecht ◽  
Jeremy Wu
Keyword(s):  
Parameter Estimation ◽  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Statistical Models ◽  
Negative Binomial ◽  
Estimation Method ◽  
Likelihood Method ◽  
Model Parameters ◽  
Test Results ◽  
Parameter Estimation Method

Abstract The acceptability of 10 theoretical distributions to simulate observed distribution of sample aflatoxin test results was evaluated by using 2 parameter estimation methods and 3 goodness of fit (GOF) tests. All theoretical distributions were compared with 120 observed distributions of aflatoxin test results of farmers' stock peanuts. For a given parameter estimation method and GOF test, the negative binomial distribution had the highest percentage of statistically acceptable fits. The log normal and Poisson-gamma (gamma shape parameter = 0.5) distributions had slightly fewer but an almost equal percentage of acceptable fits. For the 3 most acceptable statistical models, the negative binomial had the greatest percentage of best or closest fits. Both the parameter estimation method and the GOF test had an influence on which theoretical distribution had the largest number of acceptable fits. All theoretical distributions, except the negative binomial distribution, had more acceptable fits when model parameters were determined by the maximum likelihood method. The negative binomial had slightly more acceptable fits when model parameters were estimated by the method of moments. The results also demonstrated the importance of using the same GOF test for comparing the acceptability of several theoretical distributions.

Maneuvering Car Tracking Using the Interacting Multiple Model and the Dynamic 2nd-Order SVSF Method

2015 ◽  
Author(s):  
Mina Attari ◽  
Hamed Hossein Afshari ◽  
Saeid Habibi
Keyword(s):  
State Estimation ◽  
Sliding Mode ◽  
Estimation Method ◽  
Variable Structure ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Structure Control ◽  
Position Data ◽  
Car Driving ◽  
Tracking Strategy

Car tracking algorithms have recently found a major role in intelligent automotive applications. They are mainly based on the state estimation techniques to solve the maneuvering car tracking problems. The dynamic 2nd-order SVSF method is a novel robust state estimation method that is based on the variable structure control theory. It benefits from the accuracy, robustness, and chattering suppression properties of second-order sliding mode systems for robust state estimation. The main contribution of this paper is to present and implement a new tracking strategy that is a combination of the dynamic 2nd-order SVSF with the IMM filter. It benefits from the robust performance of the dynamic 2nd-order SVSF and the switching property of the IMM filter. This strategy is simulated and examined under several car driving patterns and experimental position data that are captured by a GPS device. The robustness and efficiency of this strategy is then compared with the Kalman filter-based counterparts.

Sliding Mode Controller and Filter Applied to a Pneumatic McKibben Muscle Actuator

2009 ◽  
Author(s):  
V. Jouppila ◽  
S. A. Gadsden ◽  
S. R. Habibi ◽  
G. M. Bone ◽  
A. Ellman
Keyword(s):  
Control Strategy ◽  
Sliding Mode ◽  
Variable Structure ◽  
Fast Response ◽  
Sliding Mode Controller ◽  
Modeling Uncertainties ◽  
Full State ◽  
Full State Feedback ◽  
New Strategy ◽  
Mckibben Muscle

In this paper, a robust and stable control strategy is applied to a Festo fluidic muscle actuator, with the objective of trajectory following control. A complete model of this system is not available which leads to unmodeled dynamics and uncertainties. Furthermore, full-state feedback is required for this type of control. However, in practice not all of the states are measurable or available due to cost or availability of instruments, thus a full-state observer is required. The Smooth Variable Structure Filter (SVSF) is a recently introduced robust predictor-corrector method used for state and parameter estimation, and has a form that is able to provide full-state information. In this regard, a new strategy that combines Sliding Mode Control (SMC) with the SVSF is used to control this system. The estimated states from the SVSF are used by the sliding mode controller to obtain a discontinuous control signal. This signal drives the plant to follow a desired state trajectory required by the pneumatic McKibben muscle actuator. Simulation results were generated based on a realistic desired trajectory. The results of the SMC-SVSF control strategy are compared with a tuned PID controller. The described control strategy is able to overcome the nonlinearities present in the system, has a fast response time, and is robust to modeling uncertainties and measurement noise.

Self-Tuning Fuzzy Sliding Controller for the Ship Heading Problem

2010 ◽  
Author(s):  
Nassim Khaled ◽  
Nabil G. Chalhoub
Keyword(s):  
Fuzzy Logic Controller ◽  
Sliding Mode ◽  
Variable Structure ◽  
Guidance System ◽  
Tuning Parameter ◽  
Variable Structure Systems ◽  
Modeling Uncertainties ◽  
Self Tuning ◽  
The Stability ◽  
Yaw Angle

A self-tuning fuzzy-sliding mode controller is presented in the current work. It aims at combining the advantages of the variable structure systems (VSS) theory with the self-tuning fuzzy logic controller. Neither the development of an accurate dynamic model of the plant nor the construction of a rule-based expert system is required for designing the controller. The only requirement is that the upper bound of the modeling uncertainties has to be known. The stability of the controlled system is ensured by forcing the tuning parameter to satisfy the sliding condition. The controller is implemented to control the heading of an under-actuated ship. The simulation results demonstrate the robust performance of the controller in accurately tracking the desired yaw angle specified by the guidance system in the presence of considerable modeling imprecision and environmental disturbances.

Sliding Mode Controller and Filter Applied to a Model oft an Electrohydraulic Actuator System

2005 ◽  
Author(s):  
Shu Wang ◽  
Richard Burton ◽  
Saeid Habibi
Keyword(s):  
Parameter Estimation ◽  
Sliding Mode Control ◽  
Stochastic Systems ◽  
Sliding Mode ◽  
Variable Structure ◽  
Variable Structure Systems ◽  
Estimation Strategy ◽  
Mode Control ◽  
Actuator System ◽  
Electrohydraulic Actuator

A new robust state and parameter estimation strategy called the Variable Structure Filter (VSF) has recently been proposed and used for state and parameter estimation. A very common problem of linear stochastic systems is to design a combined robust control and estimation strategy, given system and noise uncertainties. Variable Structure Control (VSC) and its special form of Sliding Mode Control (SMC) show superb robustness. This paper proposes a new strategy involving the Sliding Mode Control and the Variable Structure Filter. Both the estimator and controller are based on the concepts of Variable Structure Systems (VSS). In the presence of bounded parametric uncertainties and noise, a robust stability is guaranteed. Further more, the combined strategy can be used to achieve high regulation rates or short settling times. The object of this paper is to introduce this combined VSF and SMC strategy and to demonstrate its application to a third order model of a high precision hydrostatic system, referred to as the Electrohydraulic Actuator System (EHA).

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