基于神经网络MIMO非线性系统自适应输出反馈控制

针对一类具有对象不确定和外部干扰的MIMO(多输入多输出)非线性系统提出了自适应鲁棒输出跟踪控制方案.使用了高斯径向基神经网络自适应补偿对象非线性,高增益观测器被用来估计不能直接测量的输出导数.此方法所设计的控制器不仅保证闭环系统稳定,而且所有状态有界以及跟踪误差一致终值有界.仿真结果充分表明了该方案的有效性和可行性.     

Neural network-based adaptive output feedback control for MIMO non-affine systems

An adaptive output feedback control scheme is proposed for a class of multi-input-multi-output (MIMO) non-affine nonlinear systems in which the output signal can track the reference signal. In the systems, the relative degree of the regulated output is assumed to be known. A state observer is constructed to estimate the unknown state in the systems. A neural network (NN) is introduced to compensate the modeling errors, and a robust control is also used to reduce the approximation error, which improves the capacity of resisting disturbance of the systems. The stability of the systems is rigidly proved through Lyapunov’s direct method. Simulation results demonstrate the effectiveness and feasibility of proposed scheme.     

Passivity-based neural network adaptive output feedback control for nonlinear nonnegative dynamical systems

The potential clinical applications of adaptive neural network control for pharmacology in general, and anesthesia and critical care unit medicine in particular, are clearly apparent. Specifically, monitoring and controlling the depth of anesthesia in surgery is of particular importance. Nonnegative and compartmental models provide a broad framework for biological and physiological systems, including clinical pharmacology, and are well suited for developing models for closed-loop control of drug administration. In this paper, we develop a neural adaptive output feedback control framework for adaptive set-point regulation of nonlinear uncertain nonnegative and compartmental systems. The proposed framework is Lyapunov-based and guarantees ultimate boundedness of the error signals corresponding to the physical system states and the neural network weighting gains. The approach is applicable to nonlinear nonnegative systems with unmodeled dynamics of unknown dimension and guarantees that the physical system states remain in the nonnegative orthant of the state-space for nonnegative initial conditions. Finally, a numerical example involving the infusion of the anesthetic drug midazolam for maintaining a desired constant level of depth of anesthesia for noncardiac surgery is provided to demonstrate the efficacy of the proposed approach.     

一种简化的自适应神经网络输出反馈镇定

针对一类输出反馈非线性时滞系统,提出一种简化的自适应神经网络镇定算法.所设计的状态观测器和控制器不依赖于时滞.不同于现有的结果,系统的时滞项假定完全未知,仅采用一个神经网络补偿所有未知非线性函数,因此控制器设计更加简单,而且最终的闭环系统被证明是半全局渐近稳定的.仿真结果进一步验证了该控制方案的有效性.     

不确定非线性系统的鲁棒自适应输出反馈控制

基于自适应非线性阻尼,提出一种鲁棒自适应输出反馈控制方法。该方法适用于带有未建模动态、未知非线性、有界扰动、未知非线性参数和不确定控制系数的多输入多输出非线性系统。理论证明,在一定的假设条件下,该方法能保证闭环系统所有动态信号有界;不论有多少不确定非线性参数、多高阶的非线性系统,只需要一个自适应控制参数和观察参数;而且通过选择适当的控制器和观测器参数,能使控制误差和估计误差达到任意小。仿真结果表明了所提出方法的有效性。     

时变关联系统的分散自适应输出反馈控制

针对一类具有动静态关联项和未建模动态的时变关联系统,通过引入输入滤波器及一系列坐标变换,给出了一种分散自适应输出反馈控制器的设计方案.当时变参数的变化率属于L1∩L∞,外界干扰属于L2∩L∞,未建模动态的幅值在某砦范围内变化时,证明了闭环系统的稳定性,且每一个子系统的输出收敛于零.仿真例子验证了这一控制方案的有效性.     

Decentralized adaptive output feedback control via input/output inversion

A decentralized adaptive output feedback control design method is presented for control of large-scale interconnected systems. It is assumed that all the controllers share prior information about the subsystem reference models. Based on that information, a linear dynamic output feedback compensator and linearly parameterized neural network (NN) are introduced for each subsystem to partially cancel the effect of the interconnections on the tracking performance. Boundedness of error signals is shown through Lyapunov's direct method.     

水下机器人的神经网络自适应控制

研究了水下机器人神经网络直接自适应控制方法,采用Lyapunov稳定性理论,证明了存在有界外界干扰和有界神经网络逼近误差条件下,水下机器人控制系统的跟踪误差一致稳定有界.为了进一步验证该水控制方法的正确性和稳定性,利用水下机器人实验平台进行了动力定位实验、单自由度跟踪实验和水平面跟踪实验等验证实验.     

高速公路交通的神经网络自适应控制

建立宏观的高速公路交通流动态模型并设计理想的控制方案具有重要的理论意义和实际应用价值.在高速公路交通流宏观模型中含有复杂的非线性关系,一般的最优控制方案在计算上存在维数灾难问题,而且控制目标函数的设计往往有失偏颇.为此,本文建立了一个包括神经网络结构在内的宏观高速公路交通流动态模型,从高速公路管理者和使用者两方面的需求出发,设计了能够反映高速公路总体运营水平的目标函数,并提出相应的自适应控制方案.该方案结构简单、计算方便.数值仿真实验的结果表明控制效果良好,与不施加控制的运营方式相比较能够提高高速公路交通的总体运营水平.     

Global adaptive output feedback tracking control of robotmanipulators

This paper presents a solution to the problem of global, output feedback, tracking control of uncertain robot manipulators, specifically, a desired compensation adaptation law plus a nonlinear feedback term coupled to a dynamic nonlinear filter is designed to produce global asymptotic link position tracking while compensating for parametric uncertainty and requiring only link position measurements     

非线性不确定系统的直接自适应输出反馈模糊控制

针对一类单输入单输出非线性不确定系统，基于状态观测器并结合自适应模糊系统和滑模控制，提出一种稳定的直接自适应模糊输出反馈控制算法。该算法不需要系统状态可测的条件，并能保证闭环系统稳定。仿真结果表明了该方法的有效性。     

非线性系统的直接自适应输出反馈监督模糊控制

针对一类单输入单输出非线性不确定系统，提出一种稳定的直接自适应模糊输出反馈监督控制算法，该算法不需要系统的状态完全可测的假设条件，监督控制不仅迫使系统的状态在指定的集合内，而且当模糊自适应控制处于良好的工作状态时，监督控制可以关闭，证明了整个模糊自适应输出反馈控制算法可以保证闭环系统稳定。     

Neural network payload estimation for adaptive robot control

A concept is proposed for utilizing artificial neural networks to enhance the high-speed tracking accuracy of robotic manipulators. Tracking accuracy is a function of the controller's ability to compensate for disturbances produced by dynamical interactions between the links. A model-based control algorithm uses a nominal model of those dynamical interactions to reduce the disturbances. The problem is how to provide accurate dynamics information to the controller in the presence of payload uncertainty and modeling error. Neural network payload estimation uses a series of artificial neural networks to recognize the payload variation associated with a degradation in tracking performance. The network outputs are combined with a knowledge of nominal dynamics to produce a computationally efficient direct form of adaptive control. The concept is validated through experimentation and analysis on the first three links of a PUMA-560 manipulator. A multilayer perceptron architecture with two hidden layers is used. Integration of the principles of neural network pattern recognition and model-based control produces a tracking algorithm with enhanced robustness to incomplete dynamic information. Tracking efficacy and applicability to robust control algorithms are discussed.     

A modified high-order adaptive output feedback control algorithm for linear plants

A modified output feedback control algorithm is proposed for a linear plant subjected to parametric uncertainty; its efficiency is validated and the results of numerical simulations are presented.     

Bounding set calculation for neural network-based output feedback adaptive control systems

Evaluating the bounding set of dynamic systems subject to direct neural-adaptive control is a critical issue in applications where the control system must undergo a rigorous verification process in order to comply with certification standards. In this paper, the boundedness problem is addressed for a comprehensive class of uncertain dynamic systems. Several common but unnecessary approximations that are typically performed to simplify the Lyapunov analysis have been avoided in this effort. This leads to a more accurate and general formulation of the bounding set for the overall closed loop system. The conditions under which boundedness can be guaranteed are carefully analyzed; additionally, the interactions between the control design parameters, the ‘Strictly Positive Realness’ condition, and the shape and dimensions of the bounding set are discussed. Finally, an example is presented in which the bounding set is calculated for the neuro-adaptive control of an F/A-18 aircraft, along with a numerical study to evaluate the effect of several design parameters.     

Event-triggered adaptive output feedback control for hyperbolic PDEs: swapping-based design

<正>Partial differential equations(PDEs) characterize transport phenomena and fluid flow, and common cases include heat exchangers and road traffic [1]. Recently, scholars have become enthusiastic about event-triggered control(ETC) of hyperbolic PDEs, primarily because it can help conserve computing and communication resources. For example,Ref. [2] presented an output feedback ETC scheme for 2 × 2hyperbolic PDEs.     

从机器人输出反馈自适应神经控制中学习

针对系统参数完全未知且仅输出可测的机器人,使用径向基函数(RBF)神经网络和高增益观测器设计了一种自适应神经控制算法.该算法不仅实现了闭环系统所有信号的最终一致有界,而且沿周期跟踪轨迹实现了对未知闭环系统动态的确定学习.学过的知识可用来改进系统的控制性能,也可应用于后续相同或相似的控制任务以节约时间和能量.仿真研究表明了所设计的控制算法的正确性和有效性.     

Coordinated decentralized adaptive output feedback control of interconnected systems

A decentralized adaptive output feedback control design is proposed for large-scale interconnected systems. It is assumed that all the controllers share prior information about the system reference models. Based on that information, a linearly parameterized neural network is introduced for each subsystem to partially cancel the effect of the interconnections on tracking performance. Boundedness of error signals is shown through Lyapunov's direct method.     

Neural network adaptive control for nonlinear nonnegative dynamical systems

Nonnegative and compartmental dynamical system models are derived from mass and energy balance considerations that involve dynamic states whose values are nonnegative. These models are widespread in engineering and life sciences and typically involve the exchange of nonnegative quantities between subsystems or compartments wherein each compartment is assumed to be kinetically homogeneous. In this paper, we develop a full-state feedback neural adaptive control framework for adaptive set-point regulation of nonlinear uncertain nonnegative and compartmental systems. The proposed framework is Lyapunov-based and guarantees ultimate boundedness of the error signals corresponding to the physical system states and the neural network weighting gains. In addition, the neural adaptive controller guarantees that the physical system states remain in the nonnegative orthant of the state-space for nonnegative initial conditions.     

欠驱动船舶神经网络自适应路径跟踪控制

针对模型参数未知的欠驱动船舶路径跟踪问题,将神经网络技术与反演设计法相结合,提出一种神经网络稳定自适应控制方法。首先根据运动学误差方程和线性变换确定辅助的前进速度和艏摇角,然后利用神经网络逼近技术对模型中任意不确定因素进行补偿,设计自适应控制律,使得实际的前进速度和艏摇角分别收敛到辅助值。应用Lyapunov函数证明了船舶路径跟踪闭环系统的误差信号最终一致有界。仿真结果表明,利用设计的控制律可以迫使欠驱动船舶跟踪曲线和直线路径,并且具有较强的鲁棒性。