Robust Adaptive Observer Design Research Articles

Finite excitation based robust adaptive observer for MIMO LTI systems

SummaryThe article addresses the problem of a robust adaptive observer design for multi‐input multi‐output linear time‐invariant plants subjected to bounded disturbances in the system dynamics and the output relation. An adaptive observer design is proposed based on the finite excitation leading to a switched estimation strategy thus relaxing persistence of excitation (PE) condition. The novel finite persistence excitation (f‐PE) condition introduced in this work is significantly milder than the PE condition in terms of excitation requirement and online verifiability. The switched estimator uses the finite excitation via input signal and ensures uniformly ultimately bounded stability of the estimation error dynamics. A multiple switching strategy, which continuously tracks the information richness (about the unknown parameters), is utilized to reduce the intermediate bounds and is proved to be instrumental in improving the result.

Design of Robust Adaptive Observer against Measurement Noise for Sensorless Vector Control of Induction Motor Drives

Position control in electrical drives is a challenging problem which is complicated by sensor noise and unknown disturbances. This paper proposes a new cascade sensorless speed control technique for induction motor drives suitable for electric vehicle applications using the full-order adaptive Luenberger observer that is insensitive to measurement noise and parametric variation. The adaptive speed law is obtained by the Lyapunov method using the estimated currents and fluxes. This technique ensures the stability of the induction motor considered as nonlinear dynamic system. Since the Luenberger observer works on deterministic environment, and it is most effective when sensor noise is limited, the present study aims to design a robust observer insensitive to measurement noise and parametric variation integrated in a cascade structure. The observer allows the filtering of the measured currents. To highlight the advantages of the new scheme, a comparative study and spectrum analysis will be presented. The proposed structure is verified using MATLAB/Simulink.

Robust adaptive nonlinear observer design via multi-time scales neural network

This paper deals with the robust adaptive observer design for nonlinear dynamic systems that have an underlying multiple time-scales structure via different time-scales neural network. The Lyapunov function method is used to develop a novel stable updating law for the multi-time scales neural networks model and prove that the state error, output estimation error and the neural network weights errors are all uniformly ultimately bounded around the zero point during the entire learning process. Furthermore, passivity-based approach is used to derive the robust property of the proposed multi-time scales neural networks observer. Compared with the other nonlinear observers without considering the time scales, the proposed observer demonstrates faster convergence and more accurate properties. Two examples are presented confirming the validity of the above approach.

Robust Adaptive Observer Design for Uncertain Systems With Bounded Disturbances

This paper presents a robust adaptive observer design methodology for a class of uncertain nonlinear systems in the presence of time-varying unknown parameters with absolutely integrable derivatives, and nonvanishing disturbances. Using the universal approximation property of radial basis function (RBF) neural networks and the adaptive bounding technique, the developed observer achieves asymptotic convergence of state estimation error to zero, while ensuring boundedness of parameter errors. A comparative simulation study is presented by the end.

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