基于Prandtl-Ishlinskii模型的一类回滞非线性系统自适应控制

对带回滞驱动的一类单输入单输出的非线性不确定系统,本文采用Prandtl-Ishlinskii模型描述回滞特性,采用反步递推设计方法,实现自适应控制器的设计.仿真结果说明控制方法的有效性.     

非线性系统的神经网络自适应逆控制

提出了非线性系统的神经网络自适应逆控制方法。设计中使用了2个神经网络，经离线训练的NN1实现非线性系统的逆，在线网络NN2用于补偿逆误差和系统的动态特性变化，对一非线性系统的仿真结果表明，神经网络自适应逆控制能够提高系统的动态性能，并且具有较好的鲁棒性。     

一类不确定非线性系统的鲁棒自适应控制

针对一类MIMO不确定非线性系统的输出跟踪问题, 基于自适应反步法和滑模控制为其设计了鲁棒自适应控制器. 模型包含3种不确定性: 1) 参数不确定性; 2) 输入增益的不确定性; 3) 代表系统未建模动态和干扰的不确定函数, 该函数有界. 以非完整移动机械臂的输出跟踪控制为目标, 对其进行仿真实验, 实验结果表明所提出的控制算法是正确有效的.     

针对一类双环回滞特性的建模与逆补偿控制方法研究

提出了一种新的鲁棒自适应分段隐逆补偿状态反馈控制方案,以缓解智能材料驱动器中的双环回滞现象。其中,分段隐逆补偿方案不是真正意义上的回滞逆补偿器,而是一种在线解耦机制,从设计的临时控制信号寻找近似实际的控制信号。此外,提出了一种新的双环relay算子,从而建立了一种新的双环回滞模型。最后,在介电弹性体驱动器运动控制平台上进行实验,实验结果证明了所提出的输出-反馈控制方案的有效性。     

一类不确定非线性系统的鲁棒自适应控制

针对一类不确定非线性系统 ,设计出一种新的自适应控制器 ,它克服了现有方案存在的过多参数辨识问题 ,从而降低了控制器的动态阶次 .在较弱条件下 ,该控制器不仅能保证闭环系统所有信号有界 ,而且能使跟踪误差以指数速度收敛到零的有界小邻域内     

滞环非线性系统的加权自适应控制

本文通过引入一个非线性补偿环节和一个开关量函数,解决了滞环非线性系统的自适应控制问题,并分别对滞环宽度已知和未知情况,建立了大范围渐近收敛和稳定的加权自适应控制算法.这种算法具有渐近最优的控制效果,能用于非最小相位系统.仿真结果表明,该算法具有良好的动态性能.     

连续回滞系统的模型参考自适应控制

采用Stop和Play算子表示的Prandtl-Ishlinskii回滞模型描述回滞特性,该模型便于实现控制器的设计．考虑带有未知回滞驱动且以状态空间形式表示的连续时间线性动态系统,给出了模型参考白适应控制设计方案．控制策略保证闭环系统的全局稳定性和期望的跟踪精度,有效地抑制回滞产生的不精确和振荡现象．数值仿真结果表明了控制算法的有效性．     

一类不确定非线性系统自适应模糊滑模控制

针对一类带有未知外部扰动的不确定非线性系统,建立自适应模糊滑模控制器。基于Lyapunov稳定性理论,设计系统可调参数的自适应规则,控制器的设计过程中无需知道系统的具体模型及未知非线性函数的先验知识。数值仿真的结果也验证了该方法的有效性。     

一类高阶非线性不确定系统自适应稳定控制

研究了一类高阶非线性不确定性系统的自适应稳定控制设计问题.因该系统的非线性程度高,其控制系数不等同、符号已知、但数值未知,故在此之前其稳定控制设计问题没有得到解决.本文应用自适应技术,结合设计参数的适当选取,从而得到了设计该类非线性系统状态反馈稳定控制器的新方法,并基于反推技术,给出了稳定控制器的设计步骤.所设计的状态反馈控制器使得闭环系统的状态全局渐近收敛于零,其余闭环信号一致有界.最后通过一个仿真例子说明了控制设计方法的有效性.     

一类非线性不确定系统的自适应积分滑模控制

针对一类具有不确定参数的复杂非线性系统,提出了一种自适应积分滑模控制方法。控制器的设计分两步进行：首先,基于被控对象模型构造一个简化子系统,设计出该子系统的一个全局渐近稳定控制律;然后构造一个积分滑模面,设计自适应积分滑模补偿器以处理系统中含有不确定参数的部分,保证了滑模面的可达性和原系统的闭环稳定性。补偿后,系统的完整自适应控制律由简化子系统的控制律加补偿控制器两部分组成。所提设计方法简单,便于工程实现。最后,通过仿真结果验证了设计方案的有效性。     

Adaptive fuzzy observer backstepping control for a class of uncertain nonlinear systems with unknown time-delay

In this paper, a new adaptive fuzzy backstepping control approach is developed for a class of nonlinear systems with unknown time-delay and unmeasured states. Using fuzzy logic systems to approximate the unknown nonlinear functions, a fuzzy state observer is designed for estimating the unmeasured states. On the basis of the state observer and applying the backstepping technique, an adaptive fuzzy observer control approach is developed. The main features of the proposed adaptive fuzzy control approach not only guarantees that all the signals of the closed-loop system are semiglobally uniformly ultimately bounded, but also contain less adaptation parameters to be updated on-line. Finally, simulation results are provided to show the effectiveness of the proposed approach.     

具有Preisach类型磁滞输入的不确定非线性系统 自适应神经网络控制

本文针对一类执行器受Preisach磁滞约束的不确定非线性系统, 提出一种基于神经网络的直接自适应控制 方案, 旨在解决系统的预定精度轨迹跟踪问题. 由于Preisach算子与系统动态发生耦合, 导致算子输出信号不可测 量, 给磁滞的逆补偿造成了困难. 为解决此问题, 本文首先将Preisach模型进行分解, 以提取出控制命令信号用于 Backstepping递归设计, 并在此基础上融合一类降阶光滑函数与直接自适应神经网络控制策略, 形成对磁滞非线性 和被控对象非线性的强鲁棒性能, 且所设计方案仅包含一个需要在线更新的自适应参数, 同时可保证Lyapunov函数 时间导数的半负定性. 通过严格数学分析, 已证明该方案不仅保证闭环系统所有信号均有界, 而且输出跟踪误差随 时间渐近收敛到用户预定区间. 基于压电定位平台的半物理仿真实验进一步验证了所提出控制方案的有效性.     

Zero-error tracking control of uncertain nonlinear systems in the presence of actuator hysteresis

In this paper, the problem of adaptive tracking control is addressed for a class of nonlinear systems with unknown constant parameters and unknown actuator nonlinearity. The actuator nonlinearity is modelled as the backlash-like hysteresis, which is described by a differential model. The prior knowledge on the control gain sign is not required, and only the assumption on the reference signal is made. By combining the adaptive backstepping technique with the Nussbaum gain approach, an adaptive compensation controller design approach is developed. It is proved that the proposed control approach can guarantee that all the signals in the closed-loop system are bounded, and the tracking error can converge to zero asymptotically despite the presence of the actuator hysteresis. Two simulation examples are included to illustrate the effectiveness of the proposed approach.     

MLP-based adaptive neural control of nonlinear time-delay systems with the unknown hysteresis

In this note, the authors study the tracking problem for uncertain nonlinear time-delay systems with unknown non-smooth hysteresis described by the generalised Prandtl–Ishlinskii (P-I) model. A minimal learning parameters (MLP)-based adaptive neural algorithm is developed by fusion of the Lyapunov–Krasovskii functional, dynamic surface control technique and MLP approach without constructing a hysteresis inverse. Unlike the existing results, the main innovation can be summarised as that the proposed algorithm requires less knowledge of the plant and independent of the P-I hysteresis operator, i.e. the hysteresis effect is unknown for the control design. Thus, the outstanding advantage of the corresponding scheme is that the control law is with a concise form and easy to implement in practice due to less computational burden. The proposed controller guarantees that the tracking error converges to a small neighbourhood of zero and all states of the closed-loop system are stabilised. A simulation example demonstrates the effectiveness of the proposed scheme.     

Adaptive neural network tracking control for a class of switched stochastic pure-feedback nonlinear systems with backlash-like hysteresis

In this paper, an adaptive neural network tracking control approach is proposed for a class of switched stochastic pure-feedback nonlinear systems with backlash-like hysteresis. In the design procedure, an affine variable is constructed, which avoids the use of the mean value theorem, and the additional first-order low-pass filter is employed to deal with the problem of explosion of complexity. Then, a common Lyapunov function and a state feedback controller are explicitly obtained for all subsystems. It is proved that the proposed controller that guarantees all signals in the closed-loop system are semi-globally uniformly ultimately bounded and the tracking error remains an adjustable neighbourhood of the origin. Finally, simulation results show the effectiveness of the presented control design approach.     

Adaptive variable structure control of a class of nonlinear systems with unknown Prandtl-Ishlinskii hysteresis

Control of nonlinear systems preceded by unknown hysteresis nonlinearities is a challenging task and has received increasing attention in recent years due to growing industrial demands involving varied applications. In the literature, many mathematical models have been proposed to describe the hysteresis nonlinearities. The challenge addressed here is how to fuse those hysteresis models with available robust control techniques to have the basic requirement of stability of the system. The purpose of the note is to show such a possibility by using the Prandtl-Ishlinskii (PI) hysteresis model. An adaptive variable structure control approach, serving as an illustration, is fused with the PI model without necessarily constructing a hysteresis inverse. The global stability of the system and tracking a desired trajectory to a certain precision are achieved. Simulation results attained for a nonlinear system are presented to illustrate and further validate the effectiveness of the proposed approach.     

Adaptive neural control for a class of uncertain nonlinear systems with unknown time delay

A neural network (NN)‐based robust adaptive control design scheme is developed for a class of nonlinear systems represented by input–output models with an unknown nonlinear function and unknown time delay. By approximating on‐line the unknown nonlinear functions with a three‐layer feedforward NN, the proposed approach does not require the unknown parameters to satisfy the linear dependence condition. The control law is delay independent and possible controller singularity problem is avoided. It is proved that with the proposed neural control law, all the signals in the closed‐loop system are semiglobally bounded in the presence of unknown time delay and unknown nonlinearity. A simulation example is presented to demonstrate the method. Copyright © 2008 John Wiley & Sons, Ltd.     

一类不确定非线性系统的预定性能自适应控制

本文研究了一类不确定严格反馈非线性系统的预定性能控制问题.为保证系统预定性能,引入了一个简单的障碍型Lyapunov函数.结合反推设计法,给出了一种新的自适应控制算法.理论与实验结果表明,所得控制器不仅保证了系统预定性能,且使得闭环系统所有信号有界.     

A robust adaptive dynamic surface control for a class of nonlinear systems with unknown Prandtl–Ishilinskii hysteresis

In this paper, a robust adaptive dynamic surface control for a class of uncertain perturbed strict‐feedback nonlinear systems preceded by unknown Prandtl–Ishlinskii hysteresis is proposed. The main advantages of our scheme are that the explosion of complexity problem can be eliminated when the hysteresis is fused with backstepping design and, by introducing an initialization technique, the ??_∞ performance of system tracking error can be achieved. It is proved that the new scheme can guarantee semi‐global uniform ultimate boundedness of all closed‐loop signals and make the convergence of the tracking error to an arbitrarily small residual set. Simulation results are presented to demonstrate the efficiency of the proposed scheme. Copyright © 2010 John Wiley & Sons, Ltd.