Nonlinear observer design based on immersion and invariance method: an insight to chaotic systems

This paper presents some use of reduced order observers for two classes of chaotic systems. The first class includes rod-type plasma torch and Arneodo systems and the second class contains Lorenz and Chen systems. In this regard, a recently developed approach called immersion and invariance method is employed. During the estimation the first state of the system is assumed measured. Implementation of the proposed nonlinear observer is simple and does not need high gain to estimate unavailable state in comparison with conventional methods by rendering manifold observer design inspired form sliding mode theory. An important point of this design is to find appropriate mapping that give rise to exponential stability of observer dynamic. It is worth mentioning that the problem of designing a reduced order observer can be turned into the problem of finding a proper invariant manifold. Stability of the proposed scheme is proven via Lyapunov theorem whilst the simulation results confirm the significance of the proposed immersion and invariance observer.

[1]  R. Ortega,et al.  A globally exponentially convergent immersion and invariance speed observer for mechanical systems with non-holonomic constraints , 2010, Autom..

[2]  Guangjun Liu,et al.  An Adaptive Unscented Kalman Filtering Approach for Online Estimation of Model Parameters and State-of-Charge of Lithium-Ion Batteries for Autonomous Mobile Robots , 2015, IEEE Transactions on Control Systems Technology.

[3]  Zheng Chen,et al.  State of Charge Estimation of Lithium-Ion Batteries in Electric Drive Vehicles Using Extended Kalman Filtering , 2013, IEEE Transactions on Vehicular Technology.

[4]  Milad Malekzadeh,et al.  An immersion and invariance based input voltage and resistive load observer for DC–DC boost converter , 2020 .

[5]  Alireza Khosravi,et al.  Immersion and invariance-based filtered transformation with application to estimator design for a class of DC-DC converters , 2019 .

[6]  Mohammad Mehdi Fateh,et al.  Design of an adaptive dynamic sliding mode control approach for robotic systems via uncertainty estimators with exponential convergence rate , 2020 .

[7]  Her-Terng Yau,et al.  Design of sliding mode controller for Lorenz chaotic system with nonlinear input , 2004 .

[8]  Saeed Khorashadizadeh,et al.  Designing multi-layer quantum neural network controller for chaos control of rod-type plasma torch system using improved particle swarm optimization , 2019, Evol. Syst..

[9]  Jean-Pierre Corriou,et al.  Observer design for a nonlinear diffusion system based on the Kirchhoff transformation , 2018 .

[10]  Alireza Khosravi,et al.  Application of Adaptive Neural Network Observer in Chaotic Systems , 2014 .

[11]  Fahad Mumtaz Malik,et al.  Sampled data control of non-linear systems using extended order high gain observer , 2019 .

[12]  Alireza Khosravi,et al.  A novel adaptive output feedback control for DC–DC boost converter using immersion and invariance observer , 2020, Evol. Syst..

[13]  Milad Malekzadeh,et al.  Observer based control scheme for DC-DC boost converter using sigma–delta modulator , 2018 .

[14]  R. V. Jategaonkar,et al.  Aerodynamic Parameter Estimation from Flight Data Applying Extended and Unscented Kalman Filter , 2010 .

[15]  Zhi-Hong Guan,et al.  Adaptive synchronization for Chen chaotic system with fully unknown parameters , 2004 .

[16]  Cong Cong Observer-based robust control of uncertain systems via an integral quadratic constraint approach , 2019 .

[17]  Mojtaba Alizadeh,et al.  Adaptive PID controller design for wing rock suppression using self-recurrent wavelet neural network identifier , 2016, Evol. Syst..

[18]  Alireza Khosravi,et al.  A novel sensorless control scheme for DC-DC boost converter with global exponential stability , 2019, The European Physical Journal Plus.

[19]  Ahmed Alenany,et al.  A modified observer/Kalman filter identification (OKID) algorithm employing output residuals , 2019, International Journal of Dynamics and Control.

[20]  Alireza Khosravi,et al.  A Genesio-Tesi chaotic control using an adaptive-neural observer based RISE controller , 2015, 2015 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI).

[21]  Mohammad Mehdi Fateh,et al.  Adaptive task-space control of robot manipulators using the Fourier series expansion without task-space velocity measurements , 2018 .

[22]  Thomas Meurer,et al.  On the Extended Luenberger-Type Observer for Semilinear Distributed-Parameter Systems , 2013, IEEE Transactions on Automatic Control.

[23]  Leonid M. Fridman,et al.  Implementation of Super-Twisting Control: Super-Twisting and Higher Order Sliding-Mode Observer-Based Approaches , 2016, IEEE Transactions on Industrial Electronics.

[24]  Bharat Bhushan Sharma,et al.  Unknown input reduced order observer based synchronization framework for class of nonlinear systems , 2018 .

[25]  W. Kang,et al.  Observability for optimal sensor locations in data assimilation , 2015 .

[26]  Mojtaba Alizadeh,et al.  Parameter estimation of photovoltaic cells using improved Lozi map based chaotic optimization Algorithm , 2019, Solar Energy.

[27]  Oliver Sawodny,et al.  An immersion and invariance based speed and rotation angle observer for the ball and beam system , 2013, 2013 American Control Conference.

[28]  Hamed Mojallali,et al.  Multi-objective optimal backstepping controller design for chaos control in a rod-type plasma torch system using Bees algorithm , 2015 .

[29]  S. Bhaumik,et al.  On detectability and observer design for rectangular linear descriptor systems , 2016 .

[30]  Domenico Prattichizzo,et al.  Observer design via Immersion and Invariance for vision-based leader-follower formation control , 2010, Autom..

[31]  Ayub Khan,et al.  Synchronization on the adaptive sliding mode controller for fractional order complex chaotic systems with uncertainty and disturbances , 2019, International Journal of Dynamics and Control.