Adaptive Neural Network-Based Control for a Class of Nonlinear Pure-Feedback Systems With Time-Varying Full State Constraints

In this paper, an adaptive neural network(NN)control approach is proposed for nonlinear pure-feedback systems with time-varying full state constraints. The pure-feedback systems of this paper are assumed to possess nonlinear function uncertainties. By using the mean value theorem, pure-feedback systems can be transformed into strict feedback forms. For the newly generated systems, NNs are employed to approximate unknown items. Based on the adaptive control scheme and backstepping algorithm, an intelligent controller is designed. At the same time, time-varying Barrier Lyapunov functions(BLFs)with error variables are adopted to avoid violating full state constraints in every step of the backstepping design. All closedloop signals are uniformly ultimately bounded and the output tracking error converges to the neighborhood of zero, which can be verified by using the Lyapunov stability theorem. Two simulation examples reveal the performance of the adaptive NN control approach.

Robust adaptive fuzzy control for a class of perturbed pure-feedback nonlinear systems

A new design scheme of direct adaptive fuzzy controller for a class of perturbed pure-feedback nonlinear systems is proposed. The design is based on backstepping and the approximation capability of the first type fuzzy systems. A continuous robust term is adopted to minif-y the influence of modeling errors or disturbances. By introducing the modified integral-type Lyapunov function, the approach is able to avoid the requirement of the upper bound of the first time derivation of the high frequency control gain. Through theoretical analysis, the closed-loop control system is proven to be semi-globally uniformly ultimately bounded, with tracking error converging to a residual set.

Nonlinear feedback synchronisation control between fractional-order and integer-order chaotic systems

This paper focuses on the synchronisation between fractional-order and integer-order chaotic systems.Based on Lyapunov stability theory and numerical differentiation,a nonlinear feedback controller is obtained to achieve the synchronisation between fractional-order and integer-order chaotic systems.Numerical simulation results are presented to illustrate the effectiveness of this method.

Adaptive Output-feedback Regulation for Nonlinear Delayed Systems Using Neural Network

A novel adaptive neural network(NN)output-feedback regulation algorithm for a class of nonlinear time-varying time- delay systems is proposed.Both the designed observer and controller are independent of time delay.Different from the existing results, where the upper bounding functions of time-delay terms are assumed to be known,we only use an NN to compensate for all unknown upper bounding functions without that assumption.The proposed design method is proved to be able to guarantee semi-global uniform ultimate boundedness of all the signals in the closed system,and the system output is proved to converge to a small neighborhood of the origin.The simulation results verify the effectiveness of the control scheme.

Output Feedback Control for a Class of Nonlinear Systems

这篇论文为不明确的非线性的系统的一个家庭由一个产量控制器学习全球稳定问题令人满意一些放松了三角形类型的条件并且确切与不能是的动力学知道。用一个反馈支配设计方法，我们明确地构造全球性稳定如此的一个不明确的非线性的系统的一个动态输出赔偿者。我们的结果的实用性与一个例子被说明。关键词非线性的系统 - 全球稳定 - 产量反馈这个工作被日本教育部，科学，运动和文化在两个下面部分地支持一般科学研究的资助帮助(没有。C-15560387 ) ，并且优越(COE ) 节目的第 21 世纪中心。Keylan Alimhan 在 1998 在信息科学从 Xinjiang 大学，中国，和硕士度在 1985 在数学收到了学士学位从东京 Denki 大学，日本并且他在东京 Denki 在 2003 完成了他的博士功课工作大学。从 1985 ～ 1996，他在数学的系担任了一个讲师， Xinjiang 大学。从 2003 ～ 2004，他是在信息科学的系的一个研究伙伴，东京 Denki 大学。自从春天 2004，他是在信息科学的系的讲师，东京 Denki 大学。特别地，他的研究兴趣包括非线性的控制理论非线性的系统的输出反馈控制和非线性的柔韧的控制。阿利姆汉先生是日本的仪器和控制工程师的协会的一个成员。Hiroshi Inaba 在 1972 在奥斯汀从得克萨斯的大学收到了博士学位。从 1974 ～ 1976，他在太空工程的系担任了一个助教，在奥斯汀的得克萨斯的大学。在 1977，他在数学科学的系作为教授加入了，东京 Denki 大学(TDU ) ，并且自从 1986，他与 TDU 的信息科学的系。从 1993 ～ 1997，他担任了科学和工程的研究生院的院长，并且从 2000 ～ 2004 作为为科技的研究院的主任，在 TDU。他的主要研究兴趣包括数学系统理论，特别地为 infinitedimensional 强调理论线性系统，在戒指上的线性系统，神经网络和动态机器视觉。伊纳巴博士是系统，控制和日本，仪器的协会和日本的控制工程师的信息工程师的研究所的一个成员，研究所电并且电子学工程师( IEEE )，协会为工业并且应用数学(暹罗)，并且国际线性代数学协会( ILAS )分别地。

ABSOLUTE STABILITY OF CONTROL SYSTEM WITH MULTI-NONLINEAR FEEDBACK TERMS

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Global stabilizer of a general class of feedback nonlinear systems and its exponential convergence

We discuss the global stabilization procedure which renders a general class of feedback nonlinear systems exponential convergent. Our stabilizer consists of a nested saturation function, which is a nonlinear combination of saturation functions. Here we prove the exponential convergence of the stabilizer for the first time and give numerical examples to illustrate the efficiency of the result given above.

Adaptive State-feedback Stabilization for More General High-order Stochastic Nonlinear Systems

适应州反馈的稳定为在的高顺序的随机的非线性的系统的一个类被调查函数 fi 的上面的界限(?? 铄吗？？

Adaptive output-feedback control for MIMO nonlinear systems with time-varying delays using neural networks

An adaptive neural network output-feedback regulation approach is proposed for a class of multi-input-multi-output nonlinear time-varying delayed systems.Both the designed observer and controller are free from time delays.Different from the existing results,this paper need not the assumption that the upper bounding functions of time-delay terms are known,and only a neural network is employed to compensate for all the upper bounding functions of time-delay terms,so the designed controller procedure is more simplified.In addition,the resulting closed-loop system is proved to be semi-globally ultimately uniformly bounded,and the output regulation error converges to a small residual set around the origin.Two simulation examples are provided to verify the effectiveness of control scheme.

Observer-based Output-feedback Stabilization Control Design for a Class of Nonlinear Uncertain Systems

The output-feedback stabilization control problem is investigated for a class of nonlinear uncertain systems. Based on the multivariable analog of circle criterion, an observer is designed to estimate the system states and hence the dynamical equations that the estimation error satisfies are derived first. Then, by using integral backstepping approach together with completing square technique, the output-feedback stabilization control is constructively designed such that the closed-loop system is asymptotically stable. Finally, an example is given to illustrate the main results of this paper.

Parameterized design of nonlinear feedback controllers for servo positioning systems

To achieve fast, smooth and accurate set point tracking in servo positioning systems, a parameterized design of nonlinear feedback controllers is presented, based on a so-called composite nonlinear feedback (CNF) control technique. The controller designed here consists of a linear feedback part and a nonlinear part. The linear part is responsible for stability and fast response of the closed-loop system. The nonlinear part serves to increase the damping ratio of closed-loop poles as the controlled output approaches the target reference. The CNF control brings together the good points of both the small and the large damping ratio cases, by continuously scheduling the damping ratio of the dominant closed-loop poles and thus has the capability for superior transient performance, i.e. a fast output response with low overshoot. In the presence of constant disturbances, an integral action is included so as to remove the static bias. An explicitly parameterized controller is derived for servo positioning systems characterized by second-order model. Practical application in a micro hard disk drive servo system is then presented, together with some discussion of the rationale and characteristics of such design. Simulation and experimental results demonstrate the effectiveness of this control design methodology.

State Feedback Stabilzation for a Class of Time-delay Nonlinear Systems

The problem of global stabilization by state feedback for a class of time-delay nonlinear system is considered. By constructing the appropriate Lyapunov-Krasovskii functionals (LKF) and using the backstepping design, a linear state feedback controller making the closed-loop system globally asymptotically stable is constructed.

Output-feedback Adaptive Control for a Class of Nonlinear Systems with Unknown Control Directions

在这份报纸，产量反馈适应稳定与未知控制方向为非线性的系统的一个班被调查。首先通过线性州的转变，未知控制系数在一起是 lumped，原来的系统被转变到控制设计为变得可行的一个新系统。在为状态和参数估计的一个观察员和一个评估者的介绍以后，分别地，然后，一个建设性的设计过程为产量反馈被给使用综合者 backstepping 并且调节功能技术的适应稳定控制器。而所有另外的靠近环的系统状态被围住，设计的控制器保证状态集成到起源的原来的系统，这被显示出而所有另外的靠近环的系统状态被围住。模拟结果被说明显示出建议途径的有效性。

Adaptive output feedback control for nonlinear time-delay systems using neural network

This paper extends the adaptive neural network (NN) control approaches to a class of unknown output feedback nonlinear time-delay systems. An adaptive output feedback NN tracking controller is designed by backstep- ping technique. NNs are used to approximate unknown functions dependent on time delay. Delay-dependent filters are introduced for state estimation. The domination method is used to deal with the smooth time-delay basis functions. The adaptive bounding technique is employed to estimate the upper bound of the NN approximation errors. Based on Lyapunov- Krasovskii functional, the semi-global uniform ultimate boundedness of all the signals in the closed-loop system is proved. The feasibility is investigated by two illustrative simulation examples.

Robust Output Feedback Control for a Class of Nonlinear Systems with Input Unmodeled Dynamics

The robust global stabilization problem of a class of uncertain nonlinear systems with input unmodeled dynamics is considered using output feedback,where the uncertain nonlinear terms satisfy a far more relaxed condition than the existing triangular-type condition.Under the assumption that the input unmodeled dynamics is minimum-phase and of relative degree zero,a dynamic output compensator is explicitly constructed based on the nonseparation principle.An example illustrates the usefulness of the proposed method.

Neural Network Based Adaptive Tracking Control for a Class of Pure Feedback Nonlinear Systems With Input Saturation

In this paper, an adaptive neural networks(NNs)tracking controller is proposed for a class of single-input/singleoutput(SISO) non-affine pure-feedback non-linear systems with input saturation. In the proposed approach, the original input saturated nonlinear system is augmented by a low pass filter.Then, new system states are introduced to implement states transformation of the augmented model. The resulting new model in affine Brunovsky form permits direct and simpler controller design by avoiding back-stepping technique and its complexity growing as done in existing methods in the literature.In controller design of the proposed approach, a state observer,based on the strictly positive real(SPR) theory, is introduced and designed to estimate the new system states, and only two neural networks are used to approximate the uncertain nonlinearities and compensate for the saturation nonlinearity of actuator. The proposed approach can not only provide a simple and effective way for construction of the controller in adaptive neural networks control of non-affine systems with input saturation, but also guarantee the tracking performance and the boundedness of all the signals in the closed-loop system. The stability of the control system is investigated by using the Lyapunov theory. Simulation examples are presented to show the effectiveness of the proposed controller.

Observer-Based Nonlinear Feedback Controls for Heartbeat ECG Tracking Systems

The analysis and design of observed-based nonlinear control of a heartbeat tracking system is investigated in this paper. Two of Zeeman’s heartbeat models are investigated and modified by adding the control input as a pacemaker, thereby creating the control-affine nonlinear system models that capture the general heartbeat behavior of the human heart. The control objective is to force the output of the heartbeat models to track and generate a synthetic electrocardiogram (ECG) signal based on the actual patient reference data, obtained from the William Beaumont Hospitals, Michigan, and the PhysioNet database. The formulations of the proposed heartbeat tracking control systems consist of two phases: analysis and synthesis. In the analysis phase, nonlinear controls based on input-output feedback linearization are considered. This approach simplifies the difficult task of developing nonlinear controls. In the synthesis phase, observer-based controls are employed, where the unmeasured state variables are estimated for practical implementations. These observer-based nonlinear feedback control schemes may be used as a control strategy in electronic pacemakers. In addition, they could be used in a software-based approach to generate a synthetic ECG signal to assess the effectiveness of diagnostic ECG signal processing devices.

Acceleration Feedback Enhanced H_∞Disturbance Attenuation Control for a Class of Nonlinear Underactuated Vehicle Systems

在这篇论文，一个概括加速反馈控制(声频抗流圈) 设计方法，命名声频抗流圈提高了 H ∞控制器，为两个被建议完整激活并且在激活的非线性的自治车辆系统下面。声频抗流圈基于已知的动力学作为柔韧的改进被设计到正常控制。首先，以便拒绝不确定性和外部骚乱，线性 prefilter 在新声频抗流圈设计方法被使用在正常声频抗流圈代替高获得。然后，背走算法被用于 AFC 设计在激活的系统下面。两个的分析在有限获得 L2 稳定性显示出的频率领域和输入产量的骚乱变细新控制器设计方法是适用的。最后，模拟关于无人的模型直升飞机的追踪的控制被进行。结果与没有声频抗流圈，追踪的控制获得验证新方法的可行性的那些相比。