具有未建模动态和输出约束的耦合系统的分散自适应控制

针对一类具有输入、状态未建模动态和非线性输入的耦合系统,提出一种自适应神经网络控制方案.利用径向基函数神经网络逼近未知非线性连续函数;引入动态信号和正则化信号处理状态及输入未建模动态;通过引入非线性映射,将具有时变输出约束的严格反馈系统化为不含约束的严格反馈系统.最后,通过理论分析验证闭环系统中所有信号是半全局一致最终有界的,仿真结果进一步验证了所提出控制方案的有效性.     

Data-driven optimal terminal iterative learning control

This paper presents a data-driven optimal terminal iterative learning control (TILC) approach for linear and nonlinear discrete-time systems. The iterative learning control law is updated from only terminal output tracking error instead of entire output trajectory tracking error. The only required knowledge of a controlled system is that the Markov matrices of linear systems or the partial derivatives of nonlinear systems with respect to control inputs are bounded. Rigorous analysis and convergence proof are developed with sufficient conditions for the terminal ILC design and the results are developed for both linear and nonlinear discrete-time systems. Simulation results illustrate the applicability and effectiveness of the proposed approach.     

一类非线性非最小相位系统的直接自适应控制

针对一类不确定的离散时间非线性非最小相位动态系统,提出了一种基于神经网络和多模型的直接自适应控制方法．该控制方法由线性直接自适应控制器,神经网络非线性直接自适应控制器以及切换机构组成．线性控制器用来保证闭环系统输入输出信号有界,非线性控制器用来改善系统性能．切换策略通过对上述两种控制器的切换,保证闭环系统输入输出有界的同时,改善了系统性能．理论分析以及仿真结果表明了所提出的直接自适应控制方法的有效性．     

Hidden control neural architecture modeling of nonlinear timevarying systems and its applications

The application of neural networks to modeling time-invariant nonlinear systems has been difficult for complicated nonstationary signals, such as speech, because the networks are unable to characterize temporal variability. This problem is addressed by proposing a network architecture, called the hidden control neural network (HCNN), for modeling signals generated by nonlinear dynamical systems with restricted time variability. The mapping implemented by a multilayered neural network is allowed to change with time as a function of an additional control input signal. The network is trained using an algorithm based on ;backpropagation' and segmentation algorithms for estimating the unknown control together with the network's parameters. Application of the network to the segmentation and modeling of a signal produced by a time-varying nonlinear system, speaker-independent recognition of spoken connected digits, and online recognition of handwritten characters demonstrates the ability of the HCNN to learn time-varying nonlinear dynamics and its potential for high-performance recognition of signals produced by time-varying sources.     

Nonlinear multivariable adaptive control using multiple models and neural networks

In this paper, a multivariable adaptive control approach is proposed for a class of unknown nonlinear multivariable discrete-time dynamical systems. By introducing a k-difference operator, the nonlinear terms of the system are not required to be globally bounded. The proposed adaptive control scheme is composed of a linear adaptive controller, a neural-network-based nonlinear adaptive controller and a switching mechanism. The linear controller can assure boundedness of the input and output signals, and the neural network nonlinear controller can improve performance of the system. By using the switching scheme between the linear and nonlinear controllers, it is demonstrated that improved performance and stability can be achieved simultaneously. Theory analysis and simulation results are presented to show the effectiveness of the proposed method.     

Adaptive tracking of nonlinear systems with non-symmetric dead-zone input

Quite successfully adaptive control strategies have been applied to uncertain dynamical systems subject to dead-zone nonlinearities. However, adaptive tracking of systems with non-symmetric dead-zone characteristics has not been fully discussed with minimal knowledge of the dead-zone parameters. It is shown that the controlled system preceded by a non-symmetric dead-zone input can be represented as an uncertain nonlinear system subject to a linear input with time-varying input coefficient. To cope with this problem, a new adaptive compensation algorithm is employed without constructing the dead-zone inverse. The proposed adaptive scheme requires only the information of bounds of the dead-zone slopes and treats the time-varying input coefficient as a system uncertainty. The new control scheme ensures bounded-error trajectory tracking and assures the boundedness of all the signals in the adaptive closed loop. By appropriate selections of the controller parameters, we show that the smoothness of the controller does not affect the accuracy of trajectory tracking control. A numerical example is included to show the effectiveness of the theoretical results.     

Discrete time neural network synthesis using input and outputactivation functions

A new very fast algorithm for synthesis of a new structure of discrete-time neural networks (NN) is proposed. For this purpose the following concepts are employed: (i) combination of input and output activation functions, (ii) input time-varying signal distribution, (iii) time-discrete domain synthesis and (iv) one-step learning iteration approach. The problem of input-output mappings of time-varying vectors is solved. Simulation results based on the synthesis of a new structure of feedforward NN of an universal logical unit are presented. The proposed NN synthesis procedure is useful for applications to identification and control of nonlinear, very fast, dynamical systems. In this sense a feedforward NN for an adaptive nonlinear robot control is designed. Finally, a new algorithm for the direct inverse modeling of input/output nonquadratic systems is discussed.     

基于神经网络与多模型的非线性自适应广义预测控制

针对一类不确定非线性离散时间动态系统, 提出了基于神经网络与多模型的非线性广义预测自适应控制方法. 该自适应控制方法由线性鲁棒广义预测自适应控制器, 神经网络非线性广义预测自适应控制器和切换机制三部分构成. 线性鲁棒广义预测自适应控制器保证闭环系统的输入输出信号有界, 神经网络非线性广义预测自适应控制器能够改善系统的性能. 切换策略通过对上述两种控制器的切换, 保证系统稳定的同时, 改善系统性能. 给出了所提自适应方法的稳定性和收敛性分析. 最后通过仿真实例验证了所提方法的有效性.     

Design of fault diagnosis filters and fault-tolerant control for aclass of nonlinear systems

This paper presents a set of algorithms for fault diagnosis and fault tolerant control strategy for affine nonlinear systems subjected to an unknown time-varying fault vector. First, the design of fault diagnosis filter is performed using nonlinear observer techniques, where the system is decoupled through a nonlinear transformation and an observer is used to generate the required residual signal. By introducing an extra input to the observer, a direct estimation of the time-varying fault is obtained when the residual is controlled, by this extra input, to zero. The stability analysis of this observer is proved and some relevant sufficient conditions are obtained. Using the estimated fault vector, a fault tolerant controller is established which guarantees the stability of the closed loop system. The proposed algorithm is applied to a combined pH and consistency control system of a pilot paper machine, where simulations are performed to show the effectiveness of the proposed approach     

Data-driven output-feedback fault-tolerant control for unknown dynamic systems with faults changing system dynamics

This paper studies the data-driven output-feedback fault-tolerant control (FTC) problem for unknown dynamic systems with faults changing system dynamics. In a framework of active FTC, two basic issues are addressed: the fault detection employing only the measured input–output information; the controller reconfiguration to achieve optimal output-feedback control in the presence of multiple faults. To detect faults and write the system state via the input–output data, an approach to data-driven design of a residual generator with a full-rank transformation matrix is presented. An output-feedback approximate dynamic programming method is developed to solve the optimal control problem under the condition that the unknown linear time-invariant discrete-time plant has multiple outputs. According to the above results and the proposed input–output data-based value function approximation structure of time-varying plants, a model-free output-feedback FTC scheme considering optimal performance is given. Finally, two numerical examples and a practical example of a DC motor control system are used to demonstrate the effectiveness of the proposed methods.     

An approximate stability analysis of nonlinear systems described by Universal Learning Networks

Stability is one of the most important subjects in control systems. As for the stability of nonlinear dynamical systems, Lyapunov's direct method and linearized stability analysis method have been widely used. But, it is generally recognized that finding an appropriate Lyapunov function is fairly difficult especially for the nonlinear dynamical systems, and also it is not so easy for the linearized stability analysis to find the locally asymptotically stable region. Therefore, it is crucial and highly motivated to develop a new stability analysis method, which is easy to use and can easily study the locally asymptotically stable region at least approximately, if not exactly. On the other hand, as for the calculation of the higher order derivative, Universal Learning Networks (ULNs) are equipped with a systematic mechanism that calculates their first and second order derivatives exactly.So, in this paper, an approximate stability analysis method based on η approximation is proposed in order to overcome the above problems and its application to a nonlinear dynamical control system is discussed. The proposed method studies the stability of the original trajectory by investigating whether the perturbed trajectory can approach the original trajectory or not. The above investigation is carried out approximately by using the higher order derivatives of ULNs.In summarizing the proposed method, firstly, the absolute values of the first order derivatives of any nodes of the trajectory with respect to any initial disturbances are calculated by using ULNs. If they approach zero at time infinity, then the trajectory is locally asymptotically stable. This is an alternative linearized stability analysis method for nonlinear trajectories without calculating Jacobians directly. In the method, the stability analysis of time-varying systems with multi-branches having any sample delays is possible, because the systems are modeled by ULNs. Secondly, the locally asymptotically stable region, where asymptotical stability is secured approximately, is obtained by finding the area where the first order terms of Taylor expansion are dominant compared to the second order terms with η approximation assuming that the higher order terms more than the third order are negligibly small in the area.Simulations of an inverted pendulum balancing system are carried out. From the results of the simulations, it is clarified that the stability of the inverted pendulum control system is easily analyzed by the proposed method in terms of studying the locally asymptotically stable region.     

Adaptive Iterative Learning Control for a Class of Nonlinear Time-varying Systems with Unknown Delays and Input Dead-zone

This paper presents an adaptive iterative learning control (AILC) scheme for a class of nonlinear systems with unknown time-varying delays and unknown input dead-zone. A novel nonlinear form of dead-zone nonlinearity is presented. The assumption of identical initial condition for iterative learning control (ILC) is removed by introducing boundary layer function. The uncertainties with time-varying delays are compensated for by using appropriate Lyapunov-Krasovskii functional and Young0s inequality. Radial basis function neural networks are used to model the time-varying uncertainties. The hyperbolic tangent function is employed to avoid the problem of singularity. According to the property of hyperbolic tangent function, the system output is proved to converge to a small neighborhood of the desired trajectory by constructing Lyapunov-like composite energy function (CEF) in two cases, while keeping all the closedloop signals bounded. Finally, a simulation example is presented to verify the effectiveness of the proposed approach.      

具有未建模动态及输入输出约束的单相全桥逆变器动态面事件触发控制

全桥逆变器是一类典型的开关型非线性系统,系统中存在很多非线性和不确定因素,易导致系统性能下降,甚至造成不稳定.对于具有未建模动态和时变输出约束的单相全桥逆变器系统,利用动态信号处理未建模动态,设计辅助动态系统补偿控制信号,提出一种事件触发的自适应动态面跟踪控制策略;引入跟踪误差变换,解决输出约束问题;对控制输入进行约束,使用模糊系统调节参数向量的欧氏范数作为自适应参数,设计事件触发控制,这些技术的采用可有效降低控制器计算量,保证实际系统的可实现性,完善了具有输入约束条件下动态面控制方法的稳定性分析和证明.逆变器精确模型无需已知,实际控制系统具有较好的稳定性和鲁棒性.理论分析表明,闭环系统的所有信号半全局一致终结有界,所提出方案的有效性通过仿真实验得到进一步验证.     

一种改进的紧格式无模型自适应控制方法

针对一类由一般非线性函数描述的离散时间非线性系统,采用等价动态线性化技术,提出一种改进的紧格式无模型自适应控制(iCF-MFAC)方法.iCF-MFAC方法的自适应控制律包含两项:时变比例控制项和时变积分控制项,与只有一项时变积分项的原CF-MFAC方法相比,iCF-MFAC方法具有更好的通用性和灵活性,并能够提供更好...     

Stabilization of uncertain nonholonomic systems via time-varying sliding mode control

This note addresses the robust stabilization problem for a general class of nonholonomic systems in the presence of drift uncertainties. The control approach developed is based on the combined applications of the sliding mode control technique and nonlinear time-varying systems theory. First, some properties of nonlinear time-varying systems are introduced for the purpose of designing sliding mode controller. An explicit time-varying feedback form is provided to guarantee the existence and uniqueness of periodic time-varying solution for the corresponding linear periodic partial differential equation. Second, an explicit discontinuous feedback control law is presented to guarantee the existence of sliding mode. The first integrals obtained by the previous periodic partial differential equation are then directly used to determine the switching function. The uniform asymptotic stability of the closed loop system is proved via the invariance principle of nonlinear time-varying systems. Finally, an example is given to illustrate the proposed approach.     

Adaptive neural control of stochastic nonlinear systems with multiple time-varying delays and input saturation

This paper investigates the problem of adaptive neural control for a class of strict-feedback stochastic nonlinear systems with multiple time-varying delays, which is subject to input saturation. Via the backstepping technique and the minimal learning parameters algorithm, the problem is solved. Based on the Razumikhin lemma and neural networks’ approximation capability, a new adaptive neural control scheme is developed. The proposed control scheme can ensure that the error variables are semi-globally uniformly ultimately bounded in the sense of four-moment, while all the signals in the closed-loop system are bounded in probability. Two simulation examples are provided to demonstrate the effectiveness of the proposed control approach.     

离散时间非线性系统的数据驱动无模型自适应迭代学习控制

本文基于迭代域的动态线性化方法,提出了一类单入单出离散时间非线性系统的数据驱动无模型自适应迭代学习控制方案.无模型自适应迭代学习控制本质上属于一种数据驱动控制方法,仅利用被控对象的输入输出数据即可实现控制方案的设计.理论分析表明无模型自适应迭代学习控制方案可以保证最大学习误差的单调收敛性.数值仿真和快速路交通控制应用验证了无模型自适应迭代学习控制方案的有效性.     

An adaptive neuro-fuzzy tracking control for multi-input nonlinear dynamic systems

An adaptive neuro-fuzzy control design is suggested in this paper, for tracking of nonlinear affine in the control dynamic systems with unknown nonlinearities. The plant is described by a Takagi–Sugeno (T–S) fuzzy model, where the local submodels are realized through nonlinear dynamical input–output mappings. Our approach relies upon the effective approximation of certain terms that involve the derivative of the Lyapunov function and the unknown system nonlinearities. The above task is achieved locally, using linear in the weights neural networks. A novel resetting scheme is proposed that assures validity of the control input. Stability analysis provides the control law and the adaptation rules for the network weights, assuring uniform ultimate boundedness of the tracking and the signals appearing in the closed-loop configuration. Illustrative simulations highlight the approach.     

Constrained data-driven optimal iterative learning control

A constrained optimal ILC for a class of nonlinear and non-affine systems, without requiring any explicit model information except for the input and output data, is proposed in this work. In order to address the nonlinearities, an iterative dynamic linearization method without omitting any information of the original plant is introduced in the iteration direction. The derived linearized data model is equivalent to the original nonlinear system and reflects the real-time dynamics of the controlled plant, rather than a static approximate model. By transferring all the constraints on the system output, control input, and the change rate of input signals into a linear matrix inequality, a novel constrained data-driven optimal ILC is developed by minimizing a predesigned objective function. The optimal learning gain is unfixed and updated iteratively according to the input and output measurements, which enhances the flexibility regarding modifications and expansions of the controlled plant. The results are further extended to the point-to-point control tasks where the exact tracking performance is required only at certain points and a constrained data-driven optimal point-to-point ILC is proposed by only utilizing the error measurements at the specified points only.     

Adaptive neural/fuzzy control for interpolated nonlinear systems

Adaptive control for nonlinear time-varying systems is of both theoretical and practical importance. We propose an adaptive control methodology for a class of nonlinear systems with a time-varying structure. This class of systems is composed of interpolations of nonlinear subsystems which are input-output feedback linearizable. Both indirect and direct adaptive control methods are developed, where the spatially localized models (in the form of Takagi-Sugeno fuzzy systems or radial basis function neural networks) are used as online approximators to learn the unknown dynamics of the system. Without assumptions on rate of change of system dynamics, the proposed adaptive control methods guarantee that all internal signals of the system are bounded and the tracking error is asymptotically stable. The performance of the adaptive controller is demonstrated using a jet engine control problem.