scholarly journals Impact of Mode Decision Delay on Estimation Error in Continuous-Time Controlled System

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 73265-73272 ◽  
Author(s):  
Shengwen Xiang ◽  
Hongqi Fan ◽  
Qiang Fu
Keyword(s):  
Continuous Time ◽  
Estimation Error ◽  
Mode Decision ◽  
Controlled System ◽  
Decision Delay

scholarly journals Ground Reaction Force Estimation in Prosthetic Legs With Nonlinear Kalman Filtering Methods

2017 ◽  
Vol 139 (11) ◽  
Author(s):  
Seyed Fakoorian ◽  
Vahid Azimi ◽  
Mahmoud Moosavi ◽  
Hanz Richter ◽  
Dan Simon
Keyword(s):  
Kalman Filter ◽  
Continuous Time ◽  
Lyapunov Functions ◽  
Unscented Kalman Filter ◽  
Estimation Error ◽  
Reaction Force ◽  
Force Estimation ◽  
Joint Angles ◽  
Estimation Errors ◽  
Reaction Forces

A method to estimate ground reaction forces (GRFs) in a robot/prosthesis system is presented. The system includes a robot that emulates human hip and thigh motion, along with a powered (active) transfemoral prosthetic leg. We design a continuous-time extended Kalman filter (EKF) and a continuous-time unscented Kalman filter (UKF) to estimate not only the states of the robot/prosthesis system but also the GRFs that act on the foot. It is proven using stochastic Lyapunov functions that the estimation error of the EKF is exponentially bounded if the initial estimation errors and the disturbances are sufficiently small. The performance of the estimators in normal walk, fast walk, and slow walk is studied, when we use four sensors (hip displacement, thigh, knee, and ankle angles), three sensors (thigh, knee, and ankle angles), and two sensors (knee and ankle angles). Simulation results show that when using four sensors, the average root-mean-square (RMS) estimation error of the EKF is 0.0020 rad for the joint angles and 11.85 N for the GRFs. The respective numbers for the UKF are 0.0016 rad and 7.98 N, which are 20% and 33% lower than those of the EKF.

H∞-Property of the Continuous-Time Extended Kalman Filter

2016 ◽  
Author(s):  
Jennifer L. Bonniwell ◽  
Susan C. Schneider ◽  
Edwin E. Yaz
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Continuous Time ◽  
Estimation Error ◽  
Finite Energy ◽  
Minimum Variance ◽  
Nonlinear Observer ◽  
Minimum Variance Estimator ◽  
Error Energy ◽  
Special Case

This work elucidates another theoretical property of the ubiquitous extended Kalman filter by analyzing the energy gain of the continuous-time extended Kalman filter used as a nonlinear observer in the presence of finite-energy disturbances. The analysis provides a bound on the ratio of estimation error energy to disturbance energy, which shows that the extended Kalman filter inherently has the H∞-property along with being the locally optimal minimum variance estimator. A special case of this result is also shown to be the H2-property of the extended Kalman filter.

scholarly journals A MODEL-BASED SCHEME FOR ANTICONTROL OF SOME CHAOTIC SYSTEMS

2003 ◽  
Vol 13 (11) ◽  
pp. 3449-3457 ◽  
Author(s):  
ÖMER MORGÜL
Keyword(s):  
Continuous Time ◽  
Chaotic Systems ◽  
Controlled System ◽  
Model Based ◽  
Continuous Time Systems ◽  
Chaotic Model ◽  
Controllable Systems ◽  
Synchronization Scheme ◽  
Time Systems

We consider a model-based approach for the anticontrol of some continuous time systems. We assume the existence of a chaotic model in an appropriate form. By using a suitable input, we match the dynamics of the controlled system and the chaotic model. We show that controllable systems can be chaotified with the proposed method. We give a procedure to generate such chaotic models. We also apply an observer-based synchronization scheme to compute the required input.

scholarly journals Identification of the Thermoelectric Cooler Using Hybrid Multi-Verse Optimizer and Sine Cosine Algorithm Based Continuous-Time Hammerstein Model

2021 ◽  
Vol 21 (3) ◽  
pp. 160-174
Author(s):  
Julakha Jahan Jui ◽  
Mohd Ashraf Ahmad ◽  
Mohamed Sultan Mohamed Ali ◽  
Mohd Anwar Zawawi ◽  
Mohd Falfazli Mat Jusof
Keyword(s):  
Continuous Time ◽  
Estimation Error ◽  
Model Identification ◽  
Hammerstein Model ◽  
Rank Test ◽  
Thermoelectric Cooler ◽  
Hammerstein System ◽  
Sine Cosine Algorithm ◽  
Experimental Findings ◽  
Time Linear

Abstract This paper presents the identification of the ThermoElectric Cooler (TEC) plant using a hybrid method of Multi-Verse Optimizer with Sine Cosine Algorithm (hMVOSCA) based on continuous-time Hammerstein model. These modifications are mainly for escaping from local minima and for making the balance between exploration and exploitation. In the Hammerstein model identification a continuous-time linear system is used and the hMVOSCA based method is used to tune the coefficients of both the Hammerstein model subsystems (linear and nonlinear) such that the error between the estimated output and the actual output is reduced. The efficiency of the proposed method is evaluated based on the convergence curve, parameter estimation error, bode plot, function plot, and Wilcoxon’s rank test. The experimental findings show that the hMVOSCA can produce a Hammerstein system that generates an estimated output like the actual TEC output. Moreover, the identified outputs also show that the hMVOSCA outperforms other popular metaheuristic algorithms.

scholarly journals Stabilization for hybrid stochastic differential equations driven by Lévy noise via periodically intermittent control

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Yong Ren ◽  
Qi Zhang
Keyword(s):  
Continuous Time ◽  
Stochastic Systems ◽  
Main Idea ◽  
Lévy Noise ◽  
Intermittent Control ◽  
Controlled System ◽  
Hybrid Stochastic Systems ◽  
Levy Noise ◽  
Stability Results ◽  
Drift Coefficient

<p style='text-indent:20px;'>In this work, the issue of stabilization for a class of continuous-time hybrid stochastic systems with Lévy noise (HLSDEs, in short) is explored by using periodic intermittent control. As for the unstable HLSDEs, we design a periodic intermittent controller. The main idea is to compare the controlled system with a stabilized one with a periodic intermittent drift coefficient, which enables us to use the existing stability results on the HLSDEs. An illustrative example is proposed to show the feasibility of the obtained result.</p>

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