Adaptive output feedback regulation for a class of uncertain feedforward nonlinear systems

In this paper, we solve the problem of global output feedback regulation for uncertain feedforward nonlinear systems. The nonlinear functions, in the class of systems under consideration, are assumed to be dominated by an input‐output function multiplied by an unknown parameter and a linear unmeasured states. Contrarily to the previous works, the interval of the output's power has been expanded from to . A numerical example is provided to illustrate the effectiveness of the proposed design scheme. Copyright © 2016 John Wiley & Sons, Ltd.     

Adaptive output feedback control of uncertain nonlinear systems with input delay and output constraint

This paper proposes an adaptive neural‐network control design for a class of output‐feedback nonlinear systems with input delay and unmodeled dynamics under the condition of an output constraint. A coordinate transformation with an input integral term and a Nussbaum function are combined to solve the problem of the input possessing both time delay and unknown control gain. By utilizing a barrier Lyapunov function and designing tuning functions, the adjustment of multiparameters is handled with a single adaptive law. The uncertainty of the system is approximated by dynamic signal and radial basis function neural networks (RBFNNs). Based on Lyapunov stability theory, an adaptive tracking control scheme is developed to guarantee all the signals of the closed‐loop systems are semiglobally uniformly ultimately bounded, and the output constraint is not violated.     

Adaptive output feedback actuator failure compensation for a class of non‐linear systems

An adaptive compensation control scheme using output feedback is designed and analysed for a class of non‐linear systems with state‐dependent non‐linearities in the presence of unknown actuator failures. For a linearly parameterized model of actuator failures with unknown failure values, time instants and pattern, a robust backstepping‐based adaptive non‐linear controller is employed to handle the system failure, parameter and dynamics uncertainties. Robust adaptive parameter update laws are derived to ensure closed‐loop signal boundedness and small tracking errors, in general, and asymptotic regulation, in particular. An application to controlling the angle of attack of a non‐linear hypersonic aircraft dynamic model in the presence of elevator segment failures is studied and simulation results show that the developed adaptive control scheme has desired actuator failure compensation performance. Copyright © 2004 John Wiley & Sons, Ltd.     

Adaptive finite-time control for output regulation of nonlinear systems with completely unknown control directions

This article studies the finite-time output regulation problem for nonlinear strict-feedback systems with completely unknown control directions and unknown functions. First, according to the necessary conditions for the solvability of the output regulation problem, the output regulation problem of nonlinear strict-feedback systems and the external system is transformed into a stabilization problem of nonlinear systems. Second, an internal model with external signals is designed. Third, based on finite time, fuzzy control, output feedback control, and Nussbaum gain functions, the control law is designed so that all signals of the closed-loop system are the semi-global practically finite-time stable (SGPFS), and the tracking error converges to a small neighborhood of the origin in a finite-time. Finally, the proposed algorithm is applied to the finite-time tracking problem of Chua's oscillator system.     

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

In 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.     

Adaptive state‐feedback control with sensor failure compensation for asymptotic output tracking

This paper addresses a new adaptive output tracking problem in the presence of uncertain plant dynamics and uncertain sensor failures. A new unified nominal state‐feedback control law is developed to deal with various sensor failures, which is directly constructed by state sensor outputs. Such a new state‐feedback compensation control law is able to ensure the desired plant‐model matching properties under different failure patterns. Based on the nominal compensation control design, a new adaptive compensation control scheme is proposed, which guarantees closed‐loop signal boundedness and asymptotic output tracking. The new adaptive compensation scheme not only expands the sensor failures types that the system could tolerate but also avoids some signal processing procedures that the traditional fault‐tolerant control techniques are forced to encounter. A complete stability analysis and a representative simulation study are conducted to evaluate the effectiveness of the proposed adaptive compensation control scheme.     

Transients in output feedback adaptive systems with observer‐like reference models

This note presents analysis and quantification of transient dynamics in Model Reference Adaptive Control (MRAC) with output feedback and observer‐like reference models. A practical design methodology for this class of systems was first introduced in 1 , 2 , where an output error feedback was added to the reference model dynamics. Here, this design is complemented with an analysis of the corresponding transients. Specifically, it is shown that employing observer‐like reference models in MRAC leads to a trade‐off between achieving fast transient dynamics and using large error feedback gains in the modified reference model. For clarity sake, only systems with matched uncertainties are analyzed, yet the reported results can be extended to a broader class of uncertainties by utilizing MRAC modifications for robustness 3 , 4 . The note ends with a summary of the derived results and a discussion on practical design guidelines for adaptive output feedback controllers with observer‐like reference models. Copyright © 2015 John Wiley & Sons, Ltd.     

Adaptive output feedback control for a class of nonlinear systems with unknown virtual control coefficients signs

This paper presents a global output-feedback control scheme for a class of nonlinear systems that are transformed via a parameter-independent change of co-ordinates into a form in which there exist three kinds of unknown parameters: one is the unknown virtual control coefficients, one is the unknown parameters that multiply output nonlinearities and the other kind is the unknown parameters that multiply affine functions of the derivative of the measured output with coefficients that are smooth nonlinear functions of the measured output. We use two parameter-dependent changes of co-ordinates to transform the system considered into parametric output-feedback form. One transformation is used to eliminate the difficulty in dealing with unknown virtual control coefficients and the other transformation is used to remove the nonlinearities which are affine functions of the derivative of the measured output with coefficients that are smooth nonlinear functions of the measured output. Then the scheme presented by Ye (IEEE Trans. Automat. Control 2001; 46 :112–115) can be applied to the new system. Global results can be obtained for the overall closed-loop systems without any constraints on the nonlinear terms. Copyright © 2006 John Wiley & Sons, Ltd.     

Adaptive neural network output feedback stabilization of nonlinear non‐minimum phase systems

This paper presents an adaptive output feedback stabilization method based on neural networks (NNs) for nonlinear non‐minimum phase systems. The proposed controller comprises a linear, a neuro‐adaptive, and an adaptive robustifying parts. The NN is designed to approximate the matched uncertainties of the system. The inputs of the NN are the tapped delays of the system input–output signals. In addition, an appropriate reference signal is proposed to compensate the unmatched uncertainties inherent in the internal system dynamics. The adaptation laws for the NN weights and adaptive gains are obtained using Lyapunov's direct method. These adaptation laws employ a linear observer of system dynamics that is realizable. The ultimate boundedness of the error signals are analytically shown using Lyapunov's method. The effectiveness of the proposed scheme is shown by applying to a translation oscillator rotational actuator model. Copyright © 2009 John Wiley & Sons, Ltd.     

Adaptive output feedback dynamic surface control of stochastic nonlinear systems with state and input unmodeled dynamics

In this paper, an adaptive neural output feedback control scheme is investigated for a class of stochastic nonlinear systems with unmeasured states and four kinds of uncertainties including uncertain nonlinear function, dynamic disturbance, input unmodeled dynamics, and stochastic inverse dynamics. The unmeasured states are estimated by K‐filters, and stochastic inverse dynamics is dealt with by constructing a changing supply function. The considered input unmodeled dynamic subsystem possesses nonlinear feature, and a dynamic normalization signal is introduced to counteract the unstable effect produced by the input unmodeled dynamics. Combining dynamic surface control technique with stochastic input‐to‐state stability, small‐gain condition, and Chebyshev's inequality, the designed robust adaptive controller can guarantee that all the signals in the closed‐loop system are bounded in probability, and the error signals are semi‐globally uniformly ultimately bounded in mean square or the sense of four‐moment. Simulation results are provided to verify the effectiveness of the proposed approach. Copyright © 2015 John Wiley & Sons, Ltd.     

Observer‐based adaptive optimal output containment control problem of linear heterogeneous Multiagent systems with relative output measurements

This paper develops a relative output‐feedback–based solution to the containment control of linear heterogeneous multiagent systems. A distributed optimal control protocol is presented for the followers to not only assure that their outputs fall into the convex hull of the leaders' output but also optimizes their transient performance. The proposed optimal solution is composed of a feedback part, depending of the followers' state, and a feed‐forward part, depending on the convex hull of the leaders' state. To comply with most real‐world applications, the feedback and feed‐forward states are assumed to be unavailable and are estimated using two distributed observers. That is, a distributed observer is designed to measure each agent's states using only its relative output measurements and the information that it receives by its neighbors. Another adaptive distributed observer is designed, which uses exchange of information between followers over a communication network to estimate the convex hull of the leaders' state. The proposed observer relaxes the restrictive requirement of having access to the complete knowledge of the leaders' dynamics by all the followers. An off‐policy reinforcement learning algorithm on an actor‐critic structure is next developed to solve the optimal containment control problem online, using relative output measurements and without requiring the leaders' dynamics. Finally, the theoretical results are verified by numerical simulations.     

Global adaptive regulation control for a class of nonlinear systems with unknown control coefficients

This paper is concerned with the global asymptotic regulation control problem for a class of nonlinear uncertain systems with unknown control coefficients. The allowed class of uncertainties include unmeasured input‐to‐state stable (ISS) and/or weaker integral ISS (iISS) inverse dynamics, parametric uncertainties, and uncertain nonlinearities. By using the Nussbaum‐type gain technique and changing the ISS/integral ISS inverse dynamics supply rates, we design a dynamic output feedback controller which could guarantee that the system states are asymptotically regulated to the origin from any initial conditions, and the other signals are bounded in closed‐loop systems. The numerical example of a simple pendulum with all unknown parameters and without velocity measurement illustrates our theoretical results. The simulation results demonstrate its efficacy. Copyright © 2015 John Wiley & Sons, Ltd.     

PFC Design via FRIT Approach for Adaptive Output Feedback Control of Discrete‐Time Systems

This paper deals with a design problem of an adaptive output feedback control for discrete‐time systems with a parallel feedforward compensator (PFC), which is designed for making the augmented controlled system “Almost Strictly Positive Real” (ASPR). A PFC design scheme by a fictitious reference iterative tuning (FRIT) approach with only using an input/output experimental data set will be proposed for discrete‐time systems in order to design an adaptive output feedback control system. Furthermore, the effectiveness of the proposed PFC design method will be confirmed through numerical simulations by designing an adaptive control system with adaptive NN (neural network) for an uncertain discrete‐time system. © 2014 Wiley Periodicals, Inc. Electr Eng Jpn, 187(1): 24–32, 2014; Published online in Wiley Online Library ( wileyonlinelibrary.com ). DOI 10.1002/eej.22456     

Adaptive neural control for nonstrict‐feedback stochastic nonlinear time‐delay systems with input and output constraints

This paper investigates an adaptive neural tracking control for a class of nonstrict‐feedback stochastic nonlinear time‐delay systems with input saturation and output constraint. First, the Gaussian error function is used to represent a continuous differentiable asymmetric saturation model. Second, the appropriate Lyapunov‐Krasovskii functional and the property of hyperbolic tangent functions are used to compensate the time‐delay effects, the neural network is used to approximate the unknown nonlinearities, and a barrier Lyapunov function is designed to ensure that the output parameters are restricted. At last, based on Lyapunov stability theory, a robust adaptive neural control method is proposed, and the designed controller decreases the number of learning parameters and thus reduces the computational burden. It is shown that the designed neural controller can ensure that all the signals in the closed‐loop system are 4‐Moment (or 2 Moment) semi‐globally uniformly ultimately bounded and the tracking error converges to a small neighborhood of the origin. Two examples are given to further verify the effectiveness of the proposed approach.     

水轮机导叶开度的自适应非线性输出反馈控制

水轮机导叶开度的控制归结为一个复杂的非最小相位非线性控制模型,其自适应控制不易采用传统非线性控制方法解决。为解决水轮机导叶开度的自适应控制问题,将通用自适应输出反馈控制应用到水轮机导叶开度的控制。首先通过一个线性可逆变换将水轮机导叶开度控制的非线性模型变换成满足通用自适应输出反馈控制定理要求的形式,然后证明变换后的形式满足通用自适应输出反馈控制定理要求的条件,最后对变换后的模型应用通用自适应输出反馈控制定理得到相应的控制器。数值仿真表明,基于通用自适应输出反馈设计的水轮机导叶非线性开度控制是有效的,且对系统参数变化具有极强的鲁棒性。     

Global adaptive learning control of robotic manipulators by output error feedback

This paper addresses the problem of designing a global, output error feedback based, adaptive learning control for robotic manipulators with revolute joints and uncertain dynamics. The reference signals to be tracked are assumed to be smooth and periodic with known period. By developing in Fourier series expansion the input reference signals of every joint, an adaptive, output error feedback, learning control is designed, which ‘learns’ the input reference signals by identifying their Fourier coefficients: global asymptotic and local exponential stability of the tracking error dynamics are obtained when the Fourier series expansion of each input reference signal is finite, while arbitrary small tracking errors are achieved otherwise. The resulting control is not model based and depends only on the period of the reference signals and on some constant bounds on the robot dynamics. Copyright © 2008 John Wiley & Sons, Ltd.     

An adaptive nonlinear output feedback controller using dynamic bounding with an aircraft control application

An adaptive output feedback control scheme is developed for a class of nonlinear systems with uncertain nonlinearities, which are bounded by both static and dynamic functions of the system output, and with actuator failures whose failure time instants, patterns and values are unknown, as motivated from an aircraft flight control application. An adaptive backstepping control law using dynamic bounding is constructed to deal with unknown actuator failures as well as system parameter and dynamics uncertainties to guarantee desired system performance. Complete stability and performance analysis and illustrative simulation results of an application to aircraft flight control are presented. Copyright © 2008 John Wiley & Sons, Ltd.     

Adaptive input‐to‐output stabilization of nonlinear systems

The problem of adaptive asymptotic stabilization of a set for nonlinear convexly parameterized dynamical systems is considered. The main result provides a framework for adaptive input‐to‐output stabilization theory in the presence of disturbances. Copyright © 2008 John Wiley & Sons, Ltd.     

一类不确定非线性系统的动态面输出调节方法

针对一类由线性中性稳定的外系统驱动的具有未建模动态和外界扰动的非线性不确定系统的输出调节问题,结合动态面控制法和内模原理提出一种具有动态面控制的设计方法.根据非线性输出调节问题可解的必要条件,运用状态变换和标准内模将输出调节问题转化为镇定问题.运用动态面控制法将一阶滤波器引入反步设计中,避免了反步设计中所存在的"膨胀项...