Adaptive Reduced-order Observers Research Articles

A Position-Sensorless Control of Doubly Fed Induction Machine by Stator-Equation-Based Adaptive Reduced-Order Observer

A position-sensorless control is developed for doubly fed induction machines (DFIM). In this article, an adaptive reduced-order observer is used to estimate both rotor speed and position. The proposed reduced-order observer is based on the stator equation, and four issues are explored. First, the stator equation is used to realize the adaptive reduced-order observer, by which the structure is simple without flux calculation. Second, the stability of adaptive reduced-order observer is analyzed by the linearization method, which paves the way for design procedure of observer's feedback gain. Third, the tracking performance of the rotor-position estimation is investigated through the acceleration/deceleration of rotor speed; an explicit guideline is provided to design the adaptation integral gain to satisfy the specified tracking response. Fourth, sensitivity to machine's parameter variations is studied; the robust stability property is enhanced by the observer's feedback gain; and the robust sensitivity to inductance variation is strengthened by the adjustment of mutual inductance with stator voltage accordingly. Experiment is conducted with a 4-kW DFIM drive system to verify the validation of the proposed theoretical concepts.

Reduced order model-based observer design for online temperature distribution estimation in lithium-ion batteries

Time/space separation-based modeling methods have been widely researched for estimating lithium-ion battery (LIB) thermal dynamics. However, these methods have been developed in an offline environment and may not perform well in real-time application since the battery systems in electric vehicles (EVs) are usually subject to external disturbances. Furthermore, the onboard measurements of temperature are often corrupted by significant error. To address these problems, we present a reduced model-based observer design for online temperature distribution estimation in LIBs. First, an extreme learning machine (ELM)-based offline spatiotemporal model is constructed to approximate the thermal dynamics of LIB. Second, an adaptive reduced order observer is designed based on the offline model developed in the previous step. The offline model is then updated with the estimation results of the observer. As the performance of the estimator is highly related to the placement of sensors, a genetic algorithm (GA)-based integrated optimization strategy is also developed to determine the optimal sensor location for online estimation. Finally, the whole temperature distribution is estimated in real time using the observer, the measured voltage, current and the limited available temperature data. Two experiments on different batteries with different input currents verify the effectiveness of this developed model.

Output Feedback Control of Underactuated Spacecraft Hovering in Circular Orbit With Radial or In-Track Controller Failure

This paper proposes the output feedback control schemes for underactuated spacecraft hovering without either the radial or the in-track thrust. In contrast to the conventional fully actuated output feedback hovering control that only lacks velocity signals, a new problem is observed in the underactuated case that the simultaneous loss of velocity measurements and thrust does not only result in the loss of velocity information but also gives rise to unknown system parameters’ effects. Furthermore, due to the loss of thrust, the disturbances turn into unmatched ones. To achieve velocity-free hovering control for the underactuated cases, a novel adaptive reduced-order observer is first proposed for the purpose of velocity and parameter estimation. With the estimates provided by the observer, an output feedback controller is then designed by using the inherent coupling of system states. The asymptotic stability of the overall closed-loop system for either case is guaranteed by a Lyapunov-based method. Finally, numerical simulations are presented to demonstrate the feasibility and validity of the proposed controllers for hovering perturbed by the unmatched disturbances with the simultaneous loss of velocity measurements and thrust.

Wavelet reduced order observer based adaptive tracking control for a class of uncertain nonlinear systems using reinforcement learning

This Paper investigates the mean to design the reduced order observer and observer based controllers for a class of uncertain nonlinear system using reinforcement learning. A new design approach of wavelet based adaptive reduced order observer is proposed. The proposed wavelet adaptive reduced order observer performs the task of identification of unknown system dynamics in addition to the reconstruction of states of the system. Reinforcement learning is used via two wavelet neural networks (WNN), critic WNN and action WNN, which are combined to form an adaptive WNN controller. The “strategic” utility function is approximated by the critic WNN and is minimized by the action WNN. Owing to their superior learning capabilities, wavelet networks are employed in this work for the purpose of identification of unknown system dynamics. Using the feedback control, based on reconstructed states, the behavior of closed loop system is investigated. By Lyapunov approach, the uniformly ultimate boundedness of the closed-loop tracking error is verified. A numerical example is provided to verify the effectiveness of theoretical development.

Wavelet reduced order observer-based adaptive tracking control for a class of uncertain delayed non-linear systems subjected to actuator saturation using actor-critic architecture

This paper investigates the mean to design the reduced order observer-controller strategy for a class of uncertain delayed non-linear system subject to actuator saturation using actor-critic architecture. A new design approach of wavelet-based adaptive reduced order observer is proposed. The task of the proposed wavelet adaptive reduced order observer is to identify the unknown system dynamics and to reconstruct the states of the system. Wavelet neural network (WNN) is implemented to approximate the uncertainties present in the system and to compensate the non-linearities introduced due to actuator saturation. Reinforcement learning is applied through actor-critic architecture where a separate structure is for both perception (critic) and action (actor). Reinforcement learning is used via two wavelet neural networks (WNNs), critic WNN and action WNN. By Lyapunov-Krasovskii approach, the closed-loop tracking error is proved to be uniformly ultimate bounded. A numerical example is provided to verify the effectiveness of theoretical development.

Wavelet Adaptive Reduced Order Observer Based Tracking Control for a Class of Uncertain Time Delay Nonlinear Systems Subjected to Actuator Saturation

This Paper investigates the mean to design the reduced order observer and observer based controllers for a class of delayed uncertain nonlinear system subjected to actuator saturation. A new design approach of wavelet based adaptive reduced order observer is proposed. The proposed wavelet adaptive reduced order observer performs the task of identification of unknown system dynamics in addition to the reconstruction of states of the system. Wavelet neural network (WNN) is used to approximate the uncertainties present in the system as well as to identify and compensate the nonlinearities introduced in the system due to actuator saturation. Using the feedback control, based on reconstructed states, the behavior of closed loop system is investigated. In addition robust control terms are also designed to attenuate the approximation error due to WNN. Adaptation laws are developed for the online tuning of the wavelet parameters and the stability of the overall systems is assured by using the Lyapunov- Krasovskii functional. A numerical example is provided to verify the effectiveness of theoretical development.

Wavelet Reduced Order Observer Based Adaptive Tracking Control for a Class of Uncertain Multiple Time Delay Nonlinear Systems Subjected to Actuator Saturation Using Actor Critic Architecture

This Paper investigates the mean to design the reduced order observer and observer based controller for a class of uncertain delayed nonlinear system subjected to actuator saturation using Actor Critic architecture. A new design approach of wavelet based adaptive reduced order observer is proposed. The task of the proposed wavelet adaptive reduced order observer is to identify the unknown system dynamics and to reconstruct the states of the system. Wavelet neural network (WNN) is implemented to approximate the uncertainties present in the system as well as to identify and compensate the nonlinearities introduced in the system due to actuator saturation. Reinforcement learning is applied through Actor-Critic architecture where a separate structure is for both perception (critic) and action (actor). Reinforcement learning is used via two Wavelet Neural networks (WNN), critic WNN and action WNN, which are combined to form an adaptive WNN controller. The critic WNN approximates the “strategic” utility function which is then minimized by the action WNN. Using the feedback control, based on reconstructed states, the behavior of closed loop system is investigated. By Lyapunov-Krasovskii approach, the closed-loop tracking error is proved to be uniformly ultimate bounded. A numerical example is provided to verify the effectiveness of theoretical development.

Modelling and Computer On-line Estimation of Batch Fermentation Processes

The paper presents some problems concerning the modellization and computer on-line estimation of batch fermentation processes. Nonlinear model of batch fermentation processes is discussed as well as its discretization and linearization procedures for the model. The nonlinear transformation of nonlinear model is presented as well as their observability evaluation. For state estimation of batch fermentation processes the extended Kalman filter technique as well as an adaptive reduced order observer application are presented. Finally the application of extended Kalman filter to the simultaneous state and parameter recon-struction of batch fermentation processes is described.