Adaptive Output Feedback Control Scheme Research Articles

Adaptive fuzzy output-feedback fault-tolerant tracking control of a class of uncertain nonlinear switched systems

ABSTRACT This paper considers the output-feedback fault-tolerant tracking control problem for a class of uncertain nonlinear switched systems with nonlinear faults and strict-feedback form, where the faults which are nonaffine occur on the actuator. As a kind of specialised function approximating tool, fuzzy logic systems (FLSs), are employed to approximate the unknown smooth nonlinear functions. A switched fuzzy observer is designed to address the problem of unmeasurable states, filtered signals are used to address algebraic loop problem and the average dwell time (ADT) method is further utilised to prove the stability of the resulting closed-loop systems under a type of slowly switching signals. Based on the backstepping recursive design technique and Lyapunov function method, an adaptive fuzzy output-feedback control scheme is developed. The developed control method can ensure all the signals are semi-globally uniformly ultimately bounded (SGUUB) and the system output tracks the reference signal tightly even if unknown fault occurs. A simulation carried on an example demonstrates the validity of the obtained control scheme.

Observer-based adaptive neural control for switched stochastic pure-feedback systems with input saturation

The purpose of this paper is to investigate the problem of adaptive neural network (NN) output-feedback tracking control for input saturated switched stochastic nonlinear systems in pure-feedback form with unmeasured states. In order to facilitate the controller design process, a dead zone-based model of saturation is implemented and radial basis function NNs (RBFNNs) are employed to approximate unknown nonlinear functions and to construct an NN switched nonlinear observer to cope with difficulties raised by the presence of immeasurable state variables. Based on the adaptive backstepping technique and Lyapunov function method, an adaptive NN output feedback control scheme is developed. Furthermore, it is proved that the proposed controller can provide that, under arbitrary deterministic switching, all signals in the closed-loop system are semiglobally uniformly ultimately bounded in probability and the tracking error converges to a small neighborhood of the origin. Finally, simulation examples are presented to validate the effectiveness of the proposed adaptive NN control approach.

Adaptive neural output feedback tracking control for a class of nonlinear systems

In this paper, an adaptive neural output feedback control scheme based on backstepping technique and dynamic surface control (DSC) approach is developed to solve the tracking control problem for a class of nonlinear systems with unmeasurable states. Firstly, a nonlinear state observer is designed to estimate the unmeasurable states. Secondly, in the controller design process, radial basis function neural networks (RBFNNs) are utilised to approximate the unknown nonlinear functions, and then a novel adaptive neural output feedback tracking control scheme is developed via backstepping technique and DSC approach. It is shown that the proposed controller ensures that all signals of the closed-loop system remain bounded and the tracking error converges to a small neighbourhood around the origin. Finally, two numerical examples and one realistic example are given to illustrate the effectiveness of the proposed design approach.

Observer-based finite time control of nonlinear systems with actuator failures

This article considers a finite-time stabilization problem of nonlinear systems with actuator failures. The controlled system is in non-strict-feedback form and the system states are immeasurable. By utilizing a structure property of neural network in Lemma 4, the design difficulty caused by non-strict-feedback form is handled. By establishing a state observer and applying the approximation property of neural network, a new adaptive output-feedback control scheme is formed. Under the proposed strategy, the actuator fault is compensated successfully. Different from the existing fault-tolerant control studies, the presented adaptive output-feedback control scheme can guarantee the stability of nonlinear systems in finite time, which is of more theoretical and practical significance.

Observer-based fuzzy adaptive stabilization of uncertain switched stochastic nonlinear systems with input quantization

This paper proposes an observer-based fuzzy adaptive output feedback control scheme for a class of uncertain single-input and single-output (SISO) nonlinear stochastic systems with quantized input signals and arbitrary switchings. The SISO system under consideration contains completely unknown nonlinear functions, unmeasured system states and quantized input signals quantized by a hysteretic quantizer. By adopting a new nonlinear disposal of the quantized input, the relationship between the control input and the quantized input is established. The hysteretic quantizer that we take can effectively avoid the chattering phenomena. Furthermore, the introduction of a linear observer makes the estimation of the states possible. Based on the universal approximation ability of the fuzzy logic systems (FLSs) and backstepping recursive design with the common stochastic Lyapunov function approach, a quantized output feedback control scheme is constructed, where the dynamic surface control (DSC) is explored to alleviate the computation burden. The proposed control scheme cannot only guarantee the boundedness of signals but also make the output of the system converge to a small neighborhood of the origin. The simulation results are exhibited to demonstrate the validity of the control scheme.

Adaptive Fault-Tolerant H-Infinity Output Feedback Control for Lead-Wing Close Formation Flight

This paper investigates the attitude and position tracking control problem of the Lead–Wing close formation system with unknown multiplicative actuator faults. In close formation flight, the movement of the Wing unmanned aerial vehicle (UAV) is influenced by the vortex effects of the neighboring Lead UAV. This situation requires a modeling of the aerodynamic coupling vortex effects and linearization on the basis of optimal close formation geometry. Adaptive actuator fault parameters are identified by merging the improved unknown input observers, and adaptive laws with projection functions are presented for actuator fault tracking. The sufficient condition corresponding to the existence of unknown input observers is given. Then, an adaptive fault-tolerant H-infinity output feedback control scheme is developed to guarantee the asymptotic stability, H-infinity closed-loop system performance, and attitude and position tracking properties while the Lead UAV is being maneuvered. Simulation results of the Lead–Wing close formation flight validate the efficiency of the proposed fault-tolerant control algorithm.

Distributed Event-Triggered Adaptive Control for Consensus of Linear Multi-Agent Systems with External Disturbances.

This paper investigates the consensus problem of linear multi-agent systems subject to external disturbances via distributed event-triggered adaptive control. First, a distributed event-triggered adaptive output feedback control strategy is proposed for each agent. It is shown that under this control strategy, the consensus problem can be solved for any connected undirected communication graph in a fully distributed manner without using any global information. Then a distributed self-triggered adaptive output feedback control strategy is designed with which continuous monitoring of the measurement error is no longer needed. It is further shown that for the proposed event-triggered and self-triggered control strategies, no agent will exhibit Zeno behavior. Finally, the effectiveness of the proposed two control strategies is illustrated on a group of two-mass-spring systems.

Observer-based adaptive fuzzy output constrained control for uncertain nonlinear multi-agent systems

In this paper, consensus control problem is studied for uncertain nonlinear multi-agent systems with output constraint. Fuzzy logic systems (FLSs) and fuzzy state observer are employed to approximate unknown nonlinear functions and estimate unmeasured states, respectively. Barrier Lyapunov Function (BLF) is introduced to handle with the problem of output constraint. By combining adaptive backstepping and dynamic surface control (DSC) technique, a distributed adaptive fuzzy output feedback control scheme is proposed. It is proved that the semi-globally uniformly ultimately boundedness (SGUUB) of all the signals in the closed-loop can be guaranteed and all followers’ outputs can be well synchronized to the leader's output while maintaining consensus tracking errors to be bounded. The simulation example is provided to show the effectiveness of the presented control method.

Robust adaptive output feedback control to a class of non-triangular stochastic nonlinear systems

In this paper, the robust adaptive control design problem is studied for a class of non-triangular nonlinear systems with unmodeled dynamics and stochastic disturbances. It is assumed that the states of the systems to be controlled are unmeasurable, and thus an adaptive state observer is first developed. By utilizing the stochastic small-gain theorem and the backstepping recursive design procedure, a robust adaptive output feedback control scheme is then proposed. It is shown that all the signals in the resulting closed-loop system are bounded in probability, and the system output converges to a small residual set of the equilibrium in probability.

Adaptive output feedback tracking control for switched non‐strict‐feedback non‐linear systems with unknown control direction and asymmetric saturation actuators

The problem of adaptive tracking control is studied for a class of switched non-strict-feedback non-linear systems with unknown control direction and asymmetric saturation actuators under arbitrary switchings. The main technical difficulty comes from developing a common adaptive control scheme in the presence of unknown control direction and unknown asymmetric saturation non-linearities. A linear state transformation is exploited to deal with the unknown control coefficients and a Gaussian error function-based smooth model is used to approximate the asymmetric saturation non-linearity. Based on an input-driven observer, a common adaptive output-feedback control strategy containing only one adaptive parameter is developed for such systems via a novel combination of common Lyapunov function method, backstepping technique, variable separation approach and neural network approximation. Under the proposed control scheme, the tracking error converges to an adjustable neighbourhood of the origin. Finally, two illustrative examples are included to verify the effectiveness of the proposed control design.

Adaptive decentralized fuzzy output feedback tracking control for a class of nonlinear large-scale systems with input delays

In this paper, the decentralized tracking control problem of nonlinear large-scale systems with immeasurable states and unknown nonlinearities in each subsystem and their interconnections subject to input delays is proposed. Based on the universal approximation properties of fuzzy systems, a fuzzy observer is designed to estimate the inaccessible states of the system. Subsequently, by defining the appropriate change of coordinates and employing the backstepping technique, an adaptive fuzzy backstepping output feedback control scheme is proposed for nonlinear interconnected systems subject to input delays. Owing to the problem of so-called explosion of complexity that inherently arises in the design procedures of the traditional backstepping technique, an adaptive fuzzy dynamic surface output feedback control approach is also presented for this class of large-scale systems. Moreover, it is shown that the two proposed adaptive decentralized fuzzy observer-based control approaches ensure all the signals of the closed loop system uniformly ultimately bounded and the norms of the tracking errors as well as the norms of the observation errors can converge to small desired values by proper selection of the design parameters. Finally, the theoretic achievements are carried out on the chemical reactor recycle system to illustrate and compare the effectiveness of the two proposed approaches.

Decentralized adaptive approximation-based fuzzy output-feedback control of uncertain switched stochastic interconnected nonlinear systems

In this paper, a decentralized adaptive approximation-based output feedback control scheme is developed for a class of uncertain switched stochastic nonlinear interconnected systems. Fuzzy logic systems (FLSs) combined with adaptive backstepping method are applied to model the unknown nonlinear functions of the switched system. The full-order observer is designed to estimate the immeasurable states and the dynamic surface control (DSC) technique is adopted to ensure the computation burden is greatly reduced. Moreover, the proposed controllers guarantee the semi-global bounded of the resulting closed-loop system under any switching laws. Simulation results show the effectiveness of the proposed control scheme.

Global Output-Feedback Adaptive Stabilization for Planar Nonlinear Systems with Unknown Growth Rate and Output Function

This paper studies the problem of global output-feedback stabilization by adaptive method for a class of planar nonlinear systems with unmeasurable state dependent growth and unknown output function. It is worth emphasizing that not only the growth rate, but also the upper and lower bounds of the derivative of output function are not required to be known in the paper. To solve the problem, we propose a new adaptive output-feedback control scheme based on only one dynamic high-gain, by skillfully constructing the new Lyapunov function, and flexibly using the ideas of universal control and backstepping. It is shown that the state of the closed-loop system is bounded while global asymptotic stability can be achieved. Two examples including a practical example are given to demonstrate the effectiveness of the theoretical results.

Adaptive control of hypersonic vehicles based on characteristic models with fuzzy neural network estimators

Strong uncertainties and time-variations of hypersonic vehicles during the reentry phase pose huge challenges to their control system design. This paper addresses a novel adaptive output feedback control scheme based on characteristic models with fuzzy neural network estimators to guarantee the stable and accurate attitude tracking for the hypersonic vehicle, which is subject to unknown time-varying aerodynamics. By characteristic modeling, the time-varying uncertainties are integrated into several characteristic parameters to be estimated online, which inherit the time-variation property from the aerodynamics. And then the characteristic model-based adaptive control law is constructed, while the fuzzy neural network estimators are designed to estimate the time-varying characteristic parameters. The control performances including the property of estimators and the attitude tracking error are also analyzed. At last, the effectiveness of the proposed adaptive control scheme is illustrated by several representative simulations.

General Stochastic Convergence Theorem and Stochastic Adaptive Output-Feedback Controller

This paper is devoted to the analysis methods/tools to stochastic convergence and stochastic adaptive output-feedback control. As the first contribution, a general stochastic convergence theorem is proposed for stochastic nonlinear systems. The theorem doesn't necessarily involve a positive-definite function of the system states with negative-semidefinite infinitesimal, essentially different from stochastic LaSalle's theorem (see e.g., [1] ), and hence can provide more opportunities to achieve stochastic convergence. Moreover, as a direct extension of the convergence theorem, a general version of stochastic Barb a lat's lemma is obtained, which requires the concerned stochastic process to be almost surely integrable, rather than absolutely integrable in the sense of expectation, unlike in [2] . As the second contribution, supported by the general stochastic convergence theorem, an adaptive output-feedback control strategy is established for the global stabilization of a class of stochastic nonlinear systems with severe parametric uncertainties coupled to unmeasurable states. Its feasibility analysis takes substantial effort, and is largely based on the general stochastic convergence theorem. Particularly, for the resulting closed-loop system, certain stochastic boundedness and integrability are shown by the celebrated nonnegative semimartingale convergence theorem, and furthermore, the desired stochastic convergence is achieved via the general stochastic convergence theorem.

Decentralized adaptive tracking control for a class of interconnected nonlinear systems with input quantization

In this paper, a decentralized output-feedback adaptive control scheme is proposed for a class of interconnected nonlinear systems with input quantization. Both logarithmic quantizers and improved hysteretic quantizers are studied, and a linear time-varying model is introduced to handle the difficulty caused by quantization. The proposed scheme allows the parameters of the quantizers to be freely changed during operation, and can guarantee global stability of the overall closed-loop system regardless of the coarseness of the quantizers and the existence of interactions among subsystems. Moreover, with the aid of a kind of high-gain K-filters, it is shown that all tracking errors converge to a residual set which can be made arbitrarily small by adjusting some design parameters. Simulation results are presented to illustrate the effectiveness of the proposed scheme.

Fuzzy Adaptive Output Feedback Control of Uncertain Nonlinear Systems With Prescribed Performance.

This paper investigates the tracking control problem for a family of strict-feedback systems in the presence of unknown nonlinearities and immeasurable system states. A low-complexity adaptive fuzzy output feedback control scheme is proposed, based on a backstepping method. In the control design, a fuzzy adaptive state observer is first employed to estimate the unmeasured states. Then, a novel error transformation approach together with a new modification mechanism is introduced to guarantee the finite-time convergence of the output error to a predefined region and ensure the closed-loop stability. Compared with the existing methods, the main advantages of our approach are that: 1) without using extra command filters or auxiliary dynamic surface control techniques, the problem of explosion of complexity can still be addressed and 2) the design procedures are independent of the initial conditions. Finally, two practical examples are performed to further illustrate the above theoretic findings.

Adaptive Neural Output Feedback Control for Uncertain Robot Manipulators with Input Saturation

This paper presents an adaptive neural output feedback control scheme for uncertain robot manipulators with input saturation using the radial basis function neural network (RBFNN) and disturbance observer. First, the RBFNN is used to approximate the system uncertainty, and the unknown approximation error of the RBFNN and the time-varying unknown external disturbance of robot manipulators are integrated as a compounded disturbance. Then, the state observer and the disturbance observer are proposed to estimate the unmeasured system state and the unknown compounded disturbance based on RBFNN. At the same time, the adaptation technique is employed to tackle the control input saturation problem. Utilizing the estimate outputs of the RBFNN, the state observer, and the disturbance observer, the adaptive neural output feedback control scheme is developed for robot manipulators using the backstepping technique. The convergence of all closed-loop signals is rigorously proved via Lyapunov analysis and the asymptotically convergent tracking error is obtained under the integrated effect of the system uncertainty, the unmeasured system state, the unknown external disturbance, and the input saturation. Finally, numerical simulation results are presented to illustrate the effectiveness of the proposed adaptive neural output feedback control scheme for uncertain robot manipulators.

Observer-based tracking control for MIMO pure-feedback nonlinear systems with time-delay and input quantisation

ABSTRACTIn addressing the adaptive neural backstepping control for multiple-input and multiple-output nonlinear systems in pure-feedback form with time-delay and input quantisation, we construct a high-gain state observer and an output-feedback adaptive control scheme using backstepping method, with neural networks to estimate the uncertain nonlinear functions. Then, we propose an output feedback neural controller that ensures all the state trajectories in the time-delay quantised nonlinear systems are ultimately bounded, with the control signal being quantised by either a hysteretic quantiser or a logarithmic quantiser. An illustrative example is presented to show the applicability of the new control method developed.

Adaptive feedback suppression of unknown periodic components of acoustic noises with time-varying characteristics

Effective attenuation of the noise level is an important problem in acoustic systems. In this paper, we propose a robust adaptive output feedback control scheme that can considerably attenuate narrow-band noises made up of periodic signals mixed with random noise in the presence of modeling uncertainties. The amplitude, phase and frequencies as well as the number of periodic terms are unknown and could vary with time. The performance and robustness of the proposed scheme with respect to unstructured modeling uncertainties are analyzed for continuous-time single-input, single-output systems; the results, however, are extendable to multi-channel systems. The successful attenuation of the unknown periodic components of the disturbance despite the time variations, modeling errors, and random noise is demonstrated using simulations. In addition, guidelines how to choose certain design parameters for performance improvement have been presented.