Adaptive Neural Network Observer Research Articles

Adaptive neural network observer for proton-exchange membrane fuel cell system

Abstract This paper develops an adaptive neural network (NN) observer for proton-exchange membrane fuel cells (PEMFCs). Indeed, information on the oxygen excess ratio (OER) value is crucial to ensure optimal management of the durability and reliability of the PEMFC. The OER indicator is computed from the mass of oxygen and nitrogen inside the PEMFC cathode. Unfortunately, the measurement process of both these masses is difficult and costly. To solve this problem, the design of a PEMFC state observer is attractive. However, the behaviour of the fuel cell system is highly non-linear and its modelling is complex. Due to this constraint, a multilayer perceptron neural network (MLPNN)-based observer is proposed in this paper to estimate the oxygen and nitrogen masses. One notable advantage of the suggested MLPNN observer is that it does not require a database to train the NN. Indeed, the weights of the NN are updated in real time using the output error. In addition, the observer parameters, namely the learning rate and the damping factor, are online adapted using the optimization tools of extremum seeking. Moreover, the proposed observer stability analysis is performed using the Lyapunov theory. The observer performances are validated by simulation under MATLAB®/Simulink®. The supremacy of the proposed adaptive MLPNN observer is highlighted by comparison with a fixed-parameter MLPNN observer and a classical high-gain observer (HGO). The mean relative error value of the excess oxygen rate is considered the performance index, which is equal to 1.01% for an adaptive MLPNN and 3.95% and 9.95% for a fixed MLPNN and HGO, respectively. Finally, a robustness test of the proposed observer with respect to measurement noise is performed.

Observer‐based adaptive neural fault‐tolerant control for nonlinear systems with prescribed performance and input dead‐zone

AbstractThis article investigates the adaptive fault‐tolerant tracking control problem for nonlinear systems with prescribed performance and input dead‐zone. First, a new composite variable is constructed by using the characteristics of actuator fault and input dead‐zone for the modeling purpose. Second, an adaptive neural network observer is designed to estimate the system states in the presence of inaccurate feedback information. Third, the proposed control strategy effectively counteracts the effects of sensor failure and unknown nonlinear functions, which makes the tracking error confined within the performance boundary and all the signals of the closed‐loop system semi‐globally uniformly ultimately bounded. Finally, an application oriented example is provided to demonstrate the effectiveness of the proposed control algorithm.

Design and implementation of an adaptive neural network observer–based backstepping sliding mode controller for robot manipulators

Robot manipulators as an indispensable part of automatic operation are becoming increasingly important in intelligent manufacturing systems, such as grinding and assembly. Although control methods of robot manipulators have been extensively studied, high-precision robots still face new challenges from uncertainties caused by changes in the environment and enhancement of interference. As a consequence, the neural network-based observer is utilized to reduce the influence of uncertainties and external disturbances. In this work, a new kind of nonlinear disturbance observer is designed which could well function with observed states. To improve the control efficiency and response characteristic, a kind of new integral sliding manifold is devised and the control input is generated via backstepping techniques. The stability is proved with Lyapunov theory, and the experimental results are given to demonstrate the effectiveness of the proposed controller.

Adaptive neural network observer control for a class of nonlinear strict-feedback systems with unmeasurable states

This article addresses the backstepping control problem of the nonlinear strict-feedback system with an immeasurable state. To eliminate the effect coming from the immeasurable state variable, a new adaptive state observer method is developed for backstepping design by employing a neural network (NN) approximation strategy. As one of the highlighting contributions, the observer method can be performed with the relaxed condition because it does not require the design parameters to satisfy the Hurwitz equation, therefore it can be more easily applied and extended to serve for nonlinear system control than the existing observer methods. Finally, by integrating the observer dynamic into the backstepping design, the adaptive tracking control is achieved. Lastly, the feasibility of the method is proved by both stability theory and computer simulation.

Adaptive finite‐time output feedback control for Markov jumping nonlinear systems

SummaryIn this article, the problem of output feedback tracking control for uncertain Markov jumping nonlinear systems is studied. A finite‐time control scheme based on command filtered backstepping and adaptive neural network (NN) technique is given. The finite‐time command filter solves the problem of differential explosions for virtual control signals, the NN is utilized to approximate the uncertain nonlinear dynamics and the adaptive NN observer is applied to restructure the state of system. The finite‐time error compensation mechanism is established to compensate the errors brought by filtering process. The proposed finite‐time tracking control algorithm can ensure that the solution of the closed‐loop system is practically finite‐time stable in mean square. Two simulation examples are employed to demonstrate the effectiveness of the proposed control algorithm.

Novel neural observer based fault estimation, reconstruction and fault-tolerant control scheme for nonlinear systems

This research is concerned with the adaptive neural network observer based fault approximation and fault-tolerant control of time-varying nonlinear systems. A new strategy for adaptively updating the weights of neural network parameters is proposed to enhance fault detection accuracy. Lyapunov function theory (LFT) is applied for adaptively updating the learning parameters weights of multi-layer neural network (MLNN). The purpose of using adaptive learning rates to update the weight parameters of MLNN is to obtain the global minima for highly nonlinear functions without increasing the computational complexities and costs and increase the efficacy of fault detection. Results of the proposed adaptive MLNN observer are compared with conventional MLNN observer and high gain observer. The effects of various faults or failures are studied in detail. The proposed strategy shows more robustness to disturbances, uncertainties, and unmodelled system dynamics compared to the conventional neural network, high gain observer and other existing techniques in literature. Fault tolerant control (FTC) schemes are also proposed to account for the presence of various faults and failures. Separate sliding mode control (SMC) based FTC schemes are designed for each observer to ensure stability of the faulty system. The suggested strategy is validated on Boeing 747 100/200 aircraft. Results demonstrate the effectiveness of both the proposed adaptive MLNN observer and the FTC based on the proposed adaptive MLNN compared to the conventional MLNN, high gain observer and other existing schemes in literature. Comparison of the performance of all the strategies validates the superiority of the proposed strategy and shows that the FTC based on proposed adaptive MLNN strategy provides better robustness to various situations such as disturbances and uncertainties. It is concluded that the proposed strategy can be integrated into the aircraft for the purpose of fault diagnosis, fault isolation and FTC scheme for increasing the performance of the system.

Adaptive dynamic programming-based feature tracking control of visual servoing manipulators with unknown dynamics

This paper investigates a feature tracking control method for visual servoing (VS) manipulators adaptive dynamic programming (ADP)-based the unknown dynamics. The major superiority of ADP-based optimal control lies in that the visual tracking problem is converted to the feature tracking error control with optimal cost function. Moreover, an adaptive neural network observer is developed to approximate the entire uncertainties, which are utilized to construct an improved cost function. By establishing a critic neural network, the Hamilton–Jacobi–Bellman (HJB) equation is solved, and the approximate optimal error control policy is derived. The closed-loop VS manipulator system is verified to be ultimately uniformly bounded with the developed ADP-based feature tracking control strategy according to the Lyapunov theory. Finally, simulation results under various situations demonstrate that the proposed method achieves higher tracking accuracy than other methods, as well as satisfies energy optimal requirements.

RBF NN observer based adaptive feedback control for the ABS system under parametric uncertainties and modelling errors

An antilock braking (ABS) scheme control is a relatively difficult task due to its uncertain nonlinear dynamics. According to the requirement that the braking process must be fast and robust, we contribute to extending the universal function approximation property of the radial-basis-function (RBF) neural network (NN) to design both: (a) adaptive NN observer to estimate the tracking error dynamics; and (b) intelligent NN output feedback controller (OFC) that will overcome successfully the existing high uncertainties. Notice that the OFC is introduced to linearise the ABS nonlinear system, and the dynamic compensator is involved to stabilise the linearised system. The estimated states are used in the adaptation laws as an error signal. Simulations of the proposed control algorithm based adaptive RBFNN observer are conducted then compared to the Bang-bang controller to demonstrate its practical potential. Furthermore, its efficiency has been successfully confirmed through a robustness test.

Velocity-Observer-Based Distributed Finite-Time Attitude Tracking Control for Multiple Uncertain Rigid Spacecraft

This article addresses the distributed finite-time attitude tracking control problem for a group of uncertain rigid spacecraft in the presence of unavailable angular velocity under the directed topology condition. First, a finite-time adaptive neural network observer is proposed for each follower to estimate its own unavailable angular velocity. Unlike existing velocity-observer-based design methods, the proposed one does not need the exact knowledge of the system model, and works well for the systems with both vanishing and nonvanishing uncertainties. Further, another finite-time observer is provided to obtain the precise angular velocity information of the dynamic leader in a distributed manner. Based on these two observers and adding a power integrator technique, a continuous distributed finite-time control scheme with only attitude measurements is finally established. A rigorous theoretical proof shows that the entire finite-time stability of the combined observer–controller closed-loop system is ensured. Simulation results illustrate the benefits and effectiveness of the developed control scheme.

Adaptive Fuzzy Terminal Sliding-Mode Observer with Experimental Applications

In this paper, conventional gradient-descent-based adaptive fuzzy observer is improved by using the terminal sliding-mode theory for a class of nonlinear systems. The improvement is made in two ways: first, the switching term of the sliding-mode approach is added to the state of the observer. Second, the measurement error of the system is designed as the input of the observer instead of measured state. The stability of the observer and boundedness of the parameters are proved using Lyapunov approach. Contributions of the paper are summarized as follows: (i) the robustness and convergence properties of newly proposed observer are improved, (ii) the proposed adaptive fuzzy terminal sliding-mode observer, conventional adaptive fuzzy observer, adaptive neural-network observer, and Euler filtering approaches are compared in terms of their ability to estimate velocities of three real-time experimental systems reliably. The performance of the designed observers is discussed with root mean squared-error criterion where the proposed adaptive fuzzy terminal sliding-mode observer provided much accurate state estimation results than classical observers.

Model Free Command Filtered Backstepping Control for Marine Power Systems

In order to retrain chaotic oscillation of marine power systems which are excited by periodic electromagnetism perturbation, two novel model free command-filtered backstepping control methods are designed in this paper. Firstly, the dynamic model of marine power system based on the two parallel nonlinear models is established. Secondly, extended state observer (ESO) and adaptive neural network observer (NNO) are designed to estimate the velocity signal and the unknown dynamic model. Moreover, the uniform form of ESO and NNO is given. Next, the model free command-filtered backstepping controller is put forward based on the uniform observer form. Finally, the simulation results indicate that the two proposed control algorithms can quickly retrain chaotic oscillation and their effectiveness and potential are amply demonstrated.

Adaptive neural observer‐based backstepping fault tolerant control for near space vehicle under control effector damage

In this study, a theoretical framework for reconfigurable flight control is developed and applied to near space vehicle (NSV) attitude dynamics. First, NSV reentry mode is described. Second, an adaptive neural network observer is proposed, which ensures asymptotic convergence of the state observer error to zero under control effector damage and uncertainty. Next, a reconfigurable command-filter backstepping controller is designed based on the adaptive neural network observer. The authors focus is on the accommodation of the control effector damage, uncertainty and resulting disturbances. It is shown that the presented new control design results in asymptotic convergence of the attitude tracking error to zero. Finally, simulation results are given to demonstrate the effectiveness and potential of the proposed fault tolerant control scheme.

Application of Adaptive Neural Network Observer in Chaotic Systems

Chaos control is an important subject in control theory. Chaos control usually confronts with some problems due to unavailability of states or losing the system characteristics during the modeling process. In this situation, using an appropriate observer in control strategy may overcome the problem. In this paper, states are estimated using an observer without having complete prior information from nonlinear term based on neural network. Simulation results verify performance of the proposed structure in estimating nonlinear term specifically for an online practical use.

Adaptive output feedback control of a class of non-linear systems using neural networks

This paper presents tools for the design of a neural network based adaptive output feedback controller for a class of partially or completely unknown non-linear multi-input multi-output systems without zero dynamics. Each of the outputs is assumed to have relative degree less or equal to 2. A neural network based adaptive observer is designed to estimate the derivatives of the outputs. Subsequently, the adaptive observer is integrated into a neural network based adaptive controller architecture. Conditions are derived which guarantee the ultimate boundedness of all the errors in the closed loop system. Stability analysis reveals simultaneous learning rules for both the adaptive neural network observer and adaptive neural network controller. The design approach is illustrated using a fourth order two-input two-output example, in which each output has relative degree two.