不确定非线性多智能体系统的分布式模糊自适应镇定控制

针对一类具有未知非线性和未知参数摄动的非线性多智能体系统, 提出一种分布式模糊自适应镇定控制方法. 基于邻接智能体信息和部分智能体的自身信息, 分别设计静态耦合和动态耦合的分布式模糊自适应控制律. 基于Lyapunov 稳定性理论, 证明了所提出的控制器能使得系统状态最终稳定于原点的邻域内. 仿真实例验证了所提出方法的有效性.

     

具有未知负载扰动的水井钻机电液伺服系统无模型自适应控制

针对电液伺服系统在水井钻机推进工况下存在的参数不确定以及未知负载扰动突变等非线性因素,提出了基于径向基(RBF)神经网络扰动观测器的无模型自适应控制方法.首先,通过改进的无模型自适应控制动态线性化方法,将被控系统线性化为与输入输出相关的增量形式,并将未知负载扰动合并到一个非线性项中;然后,设计了径向基神经网络扰动观测器对含有未知负载扰动的非线性项进行估计,作为对未知扰动的补偿;最后,设计了时变参数估计律,通过在线调整伪偏导数,给出了电液伺服系统的控制更新律.仿真结果表明,所设计的控制器能够对未知负载扰动突变进行补偿,并能确保跟踪误差有界收敛.     

一种新的数据驱动的非线性自适应切换控制方法

针对一类非线性离散时间动态系统, 提出了一种新的非线性自适应切换控制方法. 该方法首先把非线性项分解为前一拍可测部分与未知增量和的形式, 并充分利用被控对象的大数据信息和知识, 把非线性项前一拍可测数据与未知增量都用于控制器设计, 分别设计了线性自适应控制器, 带有非线性项前一拍可测数据补偿的非线性自适应控制器以及带有非线性项未知增量估计与补偿的非线性自适应控制器. 三个自适应控制器通过切换函数和切换规则来协调控制被控对象. 既保证了闭环系统的稳定性, 同时又提高了闭环系统的性能. 分析了闭环切换系统的稳定性和收敛性. 最后, 通过水箱液位系统的物理实验, 实验结果验证了所提算法的有效性.     

基于扰动补偿的无微分模型参考自适应控制系统设计

针对一类含有参数不确定性和未知非线性扰动的系统,本文提出一种基于扰动补偿的无微分模型参考自适应控制方法,实现系统输出对参考模型输出信号的高精度跟踪.首先,利用被控对象模型信息设计扰动估计器,对系统非线性扰动进行在线估计;其次,基于非线性扰动估计值设计参考模型和无微分参数更新律,构建无微分模型参考自适应控制器,建立基于扰动补偿和状态反馈的自适应控制律,以消除参数不确定性和非线性扰动对系统输出的影响,保证系统输出对参考模型输出的准确跟踪;然后,给出闭环系统误差信号收敛条件和控制器参数整定方法;最后,通过数值仿真验证所提方法的有效性和优越性.     

近空间飞行器的多模型切换控制

针对受到扰动的变机翼后掠角近空间飞行器,研究一类基于多模型切换的多输入多输出非线性系统的模糊自适应鲁棒控制器的设计问题.通过构造公共Lyapunov函数设计系统的控制器,采用动态面控制方法避免了控制器设计中的计算膨胀问题,利用自适应模糊系统和鲁棒控制项在线消除系统中的未知干扰影响.仿真结果表明了该方法的有效性.     

基于LuGre 摩擦模型的机械臂模糊神经网络控制

针对未知摩擦非线性会使机械臂控制精度难以提高的缺陷,建立基于动态LuGre摩擦的机械臂模型.在系统参数未知和机械臂负载变化的情况下,设计一种自适应模糊神经网络控制器,采用基函数中心和宽度均自适应变化的模糊神经网络补偿器,实现对系统中包括LuGre摩擦在内的非线性环节的逼近,并利用滑模控制项减小逼近误差.通过Lyapunov方法证明了闭环系统的稳定性,并通过仿真结果验证了所提出控制方法的有效性.     

带有海浪滤波器的船舶航向反步自适应输出反馈控制

针对复杂海况下船舶航向控制中的模型非线性、参数不确定和海浪扰动问题,提出了一种基于反步法的非线性自适应输出反馈控制算法.首先基于无源理论设计了一种状态观测器以实现海浪滤波和状态估计,这种观测器无需海浪扰动的方差信息从而减少了观测器参数数量.然后假定系统模型参数未知,基于反步法给出了非线性控制律和参数自适应律.利用Lyapunov理论证明了这种自适应输出反馈控制系统的稳定性.仿真结果表明本文所提控制器具有较好的控制性能,对不确定性模型参数具有良好的自适应性.     

基于自适应滑模的不确定Euler-Lagrange多智能体系统抗扰动蜂拥控制

针对具有参数不确定性和未知外部扰动的Euler-Lagrange多智能体系统,设计一种基于自适应滑模控制的分布式蜂拥算法.该算法使用自适应滑模控制和自适应控制律分别补偿未知的外部扰动与模型中可线性参数化回归的不确定项,从而在实现蜂拥控制的同时,避免智能体对外部扰动先验知识的要求.理论分析表明,在多智能体达成蜂拥的同时,算法保证滑模的自适应增益有界.此外,所提出的算法同时考虑虚拟领导者追踪与基于目标区域的跟踪问题,并给出碰撞避免的条件.最后,通过算例仿真验证所提出算法的有效性.     

带有摄动死区输入的未知非线性系统自适应模糊控制

用自适应模糊控制来实现对带有摄动死区输入的一类未知非线性系统的控制. 文中给出了一种新的死区执行器模型, 该模型含有时变并且摄动的执行增益. 通过将死区非线性分解为一个线性类似项, 一个非线性项和一个扰动类似项降低了扰动类似项的上界, 从而可以用更小的控制力度来实现系统的鲁棒性. 利用反步后推技术与非线性参数化的模糊逼近器结合导出控制器, 该设计取消了模糊基函数须事先已知的限制. 本文不仅从理论上证明了所给控制器能够保证闭环系统的稳定性和预期的跟踪性能, 还用仿真实验验证了控制器的有效性.     

基于滤波器估计的正交函数神经网络非线性系统H∞控制

针对含有多个未知非线性项的非线性系统难于控制的问题,提出利用Chebyshev正交函数构建基于神经网络滤波器的控制器,并在权值学习误差有界和跟踪误差有界条件下,通过李雅普诺夫稳定性定理确定控制器的权值,保证了非线性系统的H_∞鲁棒控制.最后,利用所提出算法对非线性系统的滤波器和控制器进行确定,仿真结果验证了该方法的有效性.     

基于自适应滑模的欠驱动无人艇轨迹跟踪控制算法

针对欠驱动水面无人艇在航行过程中存在的海洋环境干扰、数学模型参数不确定、执行器故障等问题,提出了一种基于扰动观测器与神经网络技术的自适应滑模轨迹跟踪策略。在无人艇三自由度模型的基础上,结合视线制导率,提出了一种新的轨迹跟踪制导策略。采用自适应滑模控制技术设计了欠驱动无人艇轨迹跟踪控制器,有效地抑制了执行器衰减故障对无人艇控制系统的影响;同时运用了非线性扰动观测器和自适应径向基函数神经网络分别对无人艇受到的外界干扰和模型参数不确定性进行补偿和拟合,提高了控制系统的抗干扰能力。基于Lyapunov定理证明了所设计的控制系统的稳定性,并在MATLAB中进行了仿真测试。仿真结果表明,所提出的轨迹跟踪控制算法可以在较为复杂的环境下实现对欠驱动无人艇的精准控制;相较于对比算法,位置的平均跟踪误差减小了80%以上,具备较高的稳定性和鲁棒性。     

Adaptive neural control and learning of affine nonlinear systems

This paper presents deterministic learning from adaptive neural network control of affine nonlinear systems with completely unknown system dynamics. Thanks to the learning capability of radial basis function, neural network (NN), stable adaptive NN controller is designed for the unknown affine nonlinear systems. The designed adaptive NN controller is rigorously shown that learning of the unknown closed-loop system dynamics can be achieved during the stable control process because partial persistent excitation condition of some internal signals in the closed-loop system is satisfied. Subsequently, neural learning controller using the knowledge obtained from deterministic learning is constructed to achieve closed-loop stability and improve control performance. Numerical simulation is provided to show the effectiveness of the proposed control scheme.     

基于EKF滤波的倒立摆神经网络控制研究

为解决一类带干扰的模型不确定倒立摆系统中存在的两类未知项——未知函数和外界干扰,采用了基于Lyapunov函数稳定性的神经网络控制方法设计控制器。控制器设计中利用扩展卡尔曼滤波(EKF)消除系统观测噪声,获取系统状态的估计值,进而利用径向基函数(RBF)神经网络良好的逼近性来近似设计的控制律中的未知项。最后在倒立摆系统中对设计的神经网络控制器进行了仿真研究,仿真结果表明所设计的控制器能有效抑制外界干扰,在精确控制倒立摆的同时可以保证控制系统的稳定性和快速性。     

Robust adaptive backstepping control for a class of uncertain nonlinear systems based on disturbance observers

In this paper, a novel robust adaptive control scheme for a class of uncertain nonlinear systems is proposed using disturbance observer and backstepping method.Firstly, a disturbance observer is developed using radial basis function(RBF) neural network.The parameter updated law of the RBF neural network is given for monitoring subsystem disturbance well.The robust adaptive control scheme is then presented with backstepping method based on the designed disturbance observer.Semiglobal uniform ultimate bounded...     

The neural sliding mode controller design of fan-plate system

This paper proposed a sliding mode angle control with neural network estimator design for a fan-plate system. The neural network estimator is based on radial basis function and it estimates the unknown lumped bounded uncertainty of parameter variations and external disturbances in real-time. The abilities of anti-disturbance and anti-chattering are better than conventional sliding mode controller and adaptive sliding mode controller. The Lyapunov stability theorem is employed to ensure the stability of the proposed controller. The convergence and signal tracking properties are better than the conventional sliding mode controller. Finally, we employed the experiment to validate the proposed method is feasible.     

Robust adaptive backstepping tracking control of stochastic nonlinear systems with unknown input saturation: A command filter approach

This paper studies the problem of adaptive observer‐based radial basis function neural network tracking control for a class of strict‐feedback stochastic nonlinear systems comprising an unknown input saturation, uncertainties, and unknown disturbances. To handle the issue of a non‐smooth saturation input signal, a smooth function is chosen to approximate the saturation function and the state observer is used to estimate unmeasured states. By the so‐called command filter method in the controller design procedure, the implementation complexity is reduced in the proposed backstepping method. Moreover, a radial basis function neural network is deployed to reconstruct the unknown nonlinear functions. In addition, the gains of all radial basis function neural networks are updated through one updating law leading to a minimal learning parameter which is independent of the number of neural nodes and the order of the system. Comparing with the existing results, the proposed approach can stabilize a constrained stochastic system more effectively and with less computational burden. Finally, a practical example shows the performance of the proposed controller design.     

An Adaptive RBF Neural Network Control Method for a Class of Nonlinear Systems

This paper focuses on designing an adaptive radial basis function neural network (RBFNN) control method for a class of nonlinear systems with unknown parameters and bounded disturbances. The problems raised by the unknown functions and external disturbances in the nonlinear system are overcome by RBFNN, combined with the single parameter direct adaptive control method. The novel adaptive control method is designed to reduce the amount of computations effectively. The uniform ultimate boundedness of the closed-loop system is guaranteed by the proposed controller. A coupled motor drives (CMD) system, which satisfies the structure of nonlinear system, is taken for simulation to confirm the effectiveness of the method. Simulations show that the developed adaptive controller has favorable performance on tracking desired signal and verify the stability of the closed-loop system.      

Non-affine nonlinear adaptive control of decentralized large-scale systems using neural networks

This paper introduces a new decentralized adaptive neural network controller for a class of large-scale nonlinear systems with unknown non-affine subsystems and unknown interconnections represented by nonlinear functions. A radial basis function neural network is used to represent the controller’s structure. The stability of the closed loop system is guaranteed through Lyapunov stability analysis. The effectiveness of the proposed decentralized adaptive controller is illustrated by considering two nonlinear systems: a two-inverted pendulum and a turbo generator. The simulation results verify the merits of the proposed controller.     

Robust control for a class of time-delay uncertain nonlinear systems based on sliding mode observer

In this paper, a robust control scheme is proposed for a class of time-delay uncertain nonlinear systems with unknown input using the sliding mode observer. The sliding mode state observer is given with radial basis function neural networks, and then the robust control scheme is presented based on the designed sliding mode observer. The developed observer-based control scheme consists of two parts. One term is a linear controller and the other term is a neural network controller. Using the Lyapunov method, a criterion for bounded stability of the closed-loop system is developed in terms of linear matrix inequalities. Finally, a simulation example is used to illustrate the effectiveness of the proposed robust control scheme.