自适应反推神经网络的转台鲁棒控制器设计

针对未知非线性、外界干扰和参数摄动等不确定因素对实际转台控制系统的影响,提出了自适应反推神经网络的转台鲁棒控制器设计.首先给出自适应Backstepping控制器的设计方法及步骤,接着采用RBF神经网络对转台对象参数的不确定因素上界值加以辨识,实现转台系统的鲁棒控制.其中Backstepping鲁棒控制作为主控制器,RBF神经网络实现了不确定上界值的在线辨识.仿真结果表明,自适应Backstepping神经网络控制很好地克服了对象的不确定性,实现控制系统的较强鲁棒性,适于高精度飞行仿真转台系统的实时鲁棒控制.     

基于干扰观测器的船舶系统航向Backstepping控制

对船舶系统这样的非线性系统,要实现对它的控制,研究船舶系统的非线性鲁棒控制方法是非常有必要的。Backstepping设计方法即反步法,与Lyapunov型自适应律结合使用时,一定程度上能提高闭环系统的动静态系统。它对非线性控制领域发展意义颇大。此次设计主要利用Backstepping设计方法来完善船舶系统,设计出一种基于干扰观测器的船舶系统Backstepping鲁棒控制器。并通过MATLAB仿真试验来验证该控制器的合理性。     

约束不确定非线性系统的鲁棒优化镇定

针对状态和输入约束不确定非线性仿射系统,提出一种鲁棒镇定的优化控制器设计方法.基于弱鲁棒控制Lyapunov函数概念,构造一个参数可调控制器.再利用LaSalle定理和逆优化理论,验证该控制器的鲁棒镇定性和逆最优性.进一步,采用滚动优化原理在线计算控制器的可调参数,实现闭环系统的鲁棒优化镇定.最后对一个开环不稳定振荡器系统进行鲁棒优化镇定,其结果验证了文中方法的有效性.     

基于Backstepping 的非仿射非线性系统鲁棒控制

针对一类不确定非仿射非线性系统的跟踪控制问题, 提出一种鲁棒Backstepping 控制策略. 首先, 为利用仿 射非线性方法设计控制器, 给出一种适用于全局的非仿射非线性近似方法; 然后, 设计快速收敛非线性微分器以估计复合干扰和获取虚拟信号的微分, 进而给出不确定非仿射非线性系统的复合控制器, 其中鲁棒项和阻尼项分别用于减少逼近误差和近似方法中动态误差对系统跟踪的影响; 最后, 通过仿真实验验证了所提出方法的有效性.     

带有干扰的线性时变系统的非线性鲁棒控制

研究了含有未知时变参数和有界干扰的单输入单输出线性时变系统的鲁棒控制问题.系统时变参数只要求光滑有界而不限制为慢时变或参数上界已知.利用时变的状态变换得到新的动态系统,基于Backstepping方法,设计出一种非线性鲁棒控制器.通过适当选择控制器参数,可以保证闭环系统是全局渐近稳定的.仿真例子表明了算法的有效性.     

区域极点约束非脆弱鲁棒飞行控制器设计

研究飞行控制系统性能优化问题,针对区域极点约束条件下飞行控制系统非脆弱鲁棒控制器设计,飞控系统含有外部扰动,为了保证飞控系统的鲁棒性和响应品质,提出用模型参数摄动和加性控制器增益摄动的鲁棒控制,根据线性矩阵不等式理论,给出了非脆弱鲁棒控制器的可解性条件.同时为保证系统具有良好的动态特性,将闭环极点配置在复平面上指定的圆盘区域内.并用鲁棒控制和极点配置结合,给出满足鲁棒性、非脆弱性、干扰抑制性能和动态响应特性的控制器进行仿真.仿真结果表明控制器系统满足多目标控制要求,证明方法的可行性和有效性.     

基于观测器的线性系统非脆弱鲁棒控制及仿真

非脆弱鲁棒控制一直是控制领域的热门话题,近年来出现了很多有意义的研究成果,然而对基于观测器的非脆弱鲁棒控制的成果不多.针对线性不确定系统,提出基于观测器的非脆弱鲁棒控制器.基于Lyapunov稳定性理论和线性矩阵不等式变换,得到不确定线性系统趋于稳定的充分条件,实现了基于观测器的非脆弱鲁棒控制在不确定线性系统中的应用.最后,使用MATLAB软件搭建了仿真平台,对所设计非脆弱鲁棒控制器进行了计算机仿真,结果验证了该非脆弱鲁棒控制器的有效性.     

车辆座椅悬架系统的建模与H∞鲁棒控制研究

建立了四自由度的“车—椅—人”悬架系统动力学和数学模型．运用H∞鲁棒控制理论为车辆座椅悬架系统设计了鲁棒控制器，并且借助Matlab软件对鲁棒控制器和最优控制器的控制性能进行了仿真分析．仿真结果表明：与最优控制器相比所设计的鲁棒控制器能更好地抑制座椅悬架系统的垂直振动加速度，在共振频率附近其减振幅度能达到50%～60%，大大提高了乘坐舒适性，并且该系统能够承受一定的模型参数不确定性，具有良好的鲁棒稳定性．     

不确定时滞广义双线性系统的鲁棒镇定

针对不确定参数是时变但满足匹配条件的时滞广义双线性系统的鲁棒控制问题进行了研究,设计状态反馈鲁棒控制器,使得所有满足条件的不确定时滞广义双线性系统鲁棒稳定.基于Lyapunov稳定性理论,采用线性矩阵不等式方法,提出了不确定时滞广义双线性系统鲁棒镇定的充分条件,并给出了使得闭环系统鲁棒镇定的状态反馈控制器设计方法.定理解决了介于非线性和线性之间的不确定时滞广义双线性系统的有关理论.仿真实例说明了定理的有效性和合理性.     

带刚性基柔性附件振动鲁棒控制

研究了刚体基上柔性附件的振动鲁棒控制问题.介绍了结构奇异值μ理论,基于此理论设计鲁棒控制器,用压电材料作传感器和作动器(μ),用输出乘性不确定性结构来描述低阶标称模型与实际系统的误差,给出了系统μ控制器综合框架.以柔性梁附件为对象示例了分析过程.数值仿真结果表明μ控制器具有良好的鲁棒性能,用于振动控制是必要且可行的.     

一类不确定非线性大系统的分散鲁棒控制器设计

基于Backstepping方法，设计了一类具有不确定性扰动和不确定性关联项的非线性大系统的分散鲁棒稳定控制器。非线性大系统的关联项为时变有界非线性函数且不确定性扰动以仿射非线性方程的形式引入。为了提高系统的控制效果。将：Backstepping递推设计方法与L2增益控制相结合，所设计的分散鲁棒控制器不仅使每个子系统的状态向量跟踪一个指定的期望轨迹。而且还使系统的不确定性干扰具有L2增益控制。     

一种可保证瞬态特性的改进的鲁棒模型参考自适应控制

针对典型的鲁棒模型参考自适应控制中瞬态性能无法得到保障的问题, 提出一种改进的鲁棒模型参考自适应控制器. 该控制器在标准的鲁棒自适应控制中加入??_∞ 补偿器, 以抑制闭环自适应系统中参数估计误差和不确定扰动对系统输出跟踪性能造成的不利影响. 理论分析和仿真验证表明, 所提出的控制器不但保留了典型鲁棒模型参考自适应控制的理想特性, 并且通过设计适当的??∞ 补偿器使得闭环系统的瞬态性得到了较大的改善, 其改善的程度依赖于??∞ 补偿器性能指标的大小.

     

多电机驱动系统的保性能鲁棒Funnel控制

本文针对含参数不确定性的多电机驱动系统,提出一种基于最优保性能鲁棒的Funnel控制方法实现系统的规定跟踪性能.该控制方法通过构造Funnel函数对误差系统进行变换,并设计自适应反步控制器保证变换后系统的稳定性即可使跟踪误差的瞬态和稳态响应均被限制在给定的Funnel边界内.然而由于系统中存在的参数不确定性会影响系统的规定控制性能,本文在Funnel控制基础上又设计了最优保性能鲁棒控制器.它是通过将参数不确定性系统的保性能鲁棒控制问题转化为标称系统的最优控制问题,并求解新的黎卡提方程而得到的.因此所设计的控制器不但消除了参数不确定性对系统的影响并且能够使系统的性能指标达到一确定的上界.最后,对四电机驱动系统进行了仿真和实验验证,说明所提出控制方法的有效性.     

基于滤波反演法的参数不确定自动引导车的运动控制

针对参数不确定的自动引导车的运动控制问题,应用Backstepping方法设计自适应控制器,并运用Lyapunov稳定性理论与Barbalat定理证明了系统的稳定性;同时利用进化规划算法优化控制器参数,通过跟踪微分器对输入信号与虚拟控制信号进行滤波处理并提取微分信号,避免了对虚拟控制信号的解析求导,简化了控制器的设计过程。与传统PID控制的对比仿真结果表明,所提出的自适应控制策略能较好地补偿系统参数摄动的影响,提高了自动引导车的轨迹跟踪性能和鲁棒性。     

Hermite-neural-network-based adaptive control for a coupled nonlinear chaotic system

A TSK-type Hermite neural network (THNN) is studied in this paper. Since the output weights of the THNN use a functional-type form, it provides powerful representation, high learning performance and good generalization capability. Then, a Hermite-neural-network-based adaptive control (HNNAC) system which is composed of a neural controller and a robust compensator is proposed. The neural controller utilizes a THNN to online approximate an ideal controller, and the robust compensator is designed to eliminate the effect of the approximation error introduced by the neural controller upon the system stability. Moreover, a proportional-integral (PI)-type learning algorithm is derived to speed up the convergence of the tracking error. Finally, the proposed HNNAC system is applied to synchronize a coupled nonlinear chaotic system. In the simulation study, it shows that the proposed HNNAC system can achieve favorable synchronization performance without requiring a preliminary offline tuning.

     

Robust estimation‐free decentralized prescribed performance control of nonaffine nonlinear large‐scale systems

In this paper, a low‐complexity robust estimation‐free decentralized prescribed performance control scheme is proposed and analyzed for nonaffine nonlinear large‐scale systems in the presence of unknown nonlinearity and external disturbance. To tackle the high‐order dynamics of each tracking error subsystem, a time‐varying stable manifold involving the output tracking error and its high‐order derivatives is constructed, which is strictly evolved within the envelope of user‐specialized prescribed performance. Sequentially, a robust decentralized controller is devised for each manifold, under which the output tracking error and its high‐order derivatives are proven to converge asymptotically to a small residual domain with prescribed fast convergence rate. Additionally, no specialized approximation technique, adaptive scheme, and disturbance observer are needed, which alleviates the complexity and difficulty of robust decentralized controller design dramatically. Finally, 3 groups of illustrative examples are used to validate the effectiveness of the proposed low‐complexity robust decentralized control scheme for uncertain nonaffine nonlinear large‐scale systems.     

严格反馈非线性系统预设性能backstepping 控制器设计

针对一类具有一般形式的严格反馈非线性系统,提出一种基于预设性能的backstepping控制器设计方法.所谓预设性能是指在保证跟踪误差收敛到一个预先设定的任意小的区域的同时,保证收敛速度及超调量满足预先设定的条件.首先引入性能函数的概念,通过误差转化将原始的受限系统转换为等价的非受限系统;然后基于Lyapunov理论进行backstepping控制器的设计,并进行了稳定性分析;最后通过仿真实验验证了所提出方法的正确性.     

Robust attitude tracking for rigid spacecraft with prescribed transient performance

This paper investigates the prescribed performance attitude tracking control problem of a rigid spacecraft with uncertain dynamics and bounded disturbances. By suitable selection of the configuration error function, the trajectory tracking problem on SO(3) is transformed into a point stabilisation problem of an error dynamic system in the associated Lie algebra. The predefined transient performance indexes, which include the maximum steady-state error and overshoot, and the minimum convergence rate of the attitude error vector, are characterised as the inequality constraints. For the tracking controller design, the error transformation technique is utilised to transform the attitude error dynamics with inequality constraints to an equivalent ‘unconstrained’ error one. Then, a coordinate-free robust tracking controller is designed for the ‘unconstrained’ error dynamics to solve the prescribed performance attitude tracking control problem. A rigorous mathematical stability proof is given. Finally, numerical simulations are presented to demonstrate the effectiveness and robustness of the proposed controller.     

Neural network-based asymptotic tracking control of unknown nonlinear systems with continuous control command

ABSTRACT

This paper proposes a robust tracking controller for a class of nonlinear second-order systems with time-varying uncertainties. The controller is mainly based on the robust integral of the sign of the error (RISE) control approach to achieve an asymptotic stability result with a continuous control command in the presence of additive uncertainties. An adaptive feedforward neural network control term is blended with a new RISE controller to improve the system's transient performance. The proposed RISE controller is a modified version of the existing saturated RISE controller such that only sign of the derivative of the output is needed. The stability of the closed-loop system is well studied, where a local asymptotic stability is proven. The controller performance is validated through simulations on a two-degree-of-freedom lower limb robotic exoskeleton.     

Robust dynamic surface control of flexible joint robots using recurrent neural networks

A robust neuro-adaptive controller for uncertain flexible joint robots is presented. This control scheme integrates H-infinity disturbance attenuation design and recurrent neural network adaptive control technique into the dynamic surface control framework. Two recurrent neural networks are used to adaptively learn the uncertain functions in a flexible joint robot. Then, the effects of approximation error and filter error on the tracking performance are attenuated to a prescribed level by the embedded H-infinity controller, so that the desired H-infinity tracking performance can be achieved. Finally, simulation results verify the effectiveness of the proposed control scheme.