输入时滞非线性系统的新型动态面Funnel控制

本文针对一类带有输入时滞的不确定非线性系统, 提出了新型动态面Funnel控制方案. 首先设计补偿动态 变量将输入时滞系统转换成无时滞的系统, 仅需在递归控制的最后一步补偿, 从而优化了控制器设计过程. 其次, 构造Funnel函数, 使系统的瞬态和稳态跟踪误差被限制在给定边界内. 最后, 提出新型非线性动态面控制方法, 不仅 避免了自适应反推控制中的“微分爆炸”问题, 而且消除了边界层误差, 使得系统的跟踪误差最终渐近收敛到零. 理 论分析表明该闭环系统的所有信号一致最终有界, 仿真结果验证了该控制方案的有效性.     

基于控制性能的MIMO非线性系统动态面控制

针对一类不确定非线性MIMO(multiple-input multiple-output)系统,在动态面控制方法的基础上,提出了自适应跟踪控制方案.通过引入性能函数和输出误差转换,保证输出信号具有指定的跟踪速度、跟踪误差、最大超调量.为了避免控制奇异问题,采用神经网络直接逼近期望控制信号.该方案无需估计神经网络的权值,仅对1个参数进行自适应律设计.理论证明了闭环系统所有信号有界,仿真结果验证了所提方案的有效性.     

具有未建模动态的自适应神经网络动态面控制

对一类具有未建模动态的严格反馈非线性系统,提出一种自适应神经网络动态面控制方案.该方案将动态面控制方法扩展到具有未建模动态的严格反馈非线性系统的控制器设计中,拓展了动态面控制方法的应用范围.利用动态面控制方法引入的紧集来处理未建模动态对于系统的影响.利用Young's不等式,提出两种自适应参数调节方案.与现有研究结果相比,有效地减少了可调参数的数目,放宽了动态不确定性的假设,无需虚拟控制增益系数导数的信息.通过理论分析,证明了闭环控制系统是半全局一致终结有界的,且跟踪误差收敛到原点的一个小邻域内.     

面向控制性能的一类非线性关联系统输出反馈动态面控制

针对一类含有完全未知关联项的多输入/多输出非线性系统,提出了输出反馈动态面自适应控制方案,克服了反推控制中的微分爆炸问题;利用神经网络逼近系统中的未知关联项,对于每个子系统只需对一个参数设计自适应律;引入性能函数和输出误差变换,跟踪误差信号的收敛速率、最大超调量和稳态误差等控制性能指标均可得到保证.理论证明了闭环系统的所有信号半全局一致有界,仿真结果验证了所提方案的有效性.     

一类非线性时滞系统的自适应动态面控制

本文对于一类含不确定输入时滞和干扰的非线性系统的跟踪控制问题提出了一种自适应动态面控制方案. 利用动态面控制方法避免了传统的后推设计中存在的复杂度爆炸问题. 分别构造了一个滤波器和一个虚拟观测器来产生辅助信号. 采用神经网络来逼近未知的连续函数. 跟踪误差被证明最终收敛到一个足够小的紧集. 给出了一个数字仿真示例验证了理论结果.     

基于观测器的机器人动态面控制

针对带干扰项的刚性机械臂系统,提出一种基于观测器与动态面技术的控制器设计方案.该方案只需测量机械臂位置,而无需测量角速度.首先应用状态观测器估计不可测量的关节角速度,然后把这一观测量引入动态面设计过程中,从而得到了系统控制律.与基于后推的机器人控制比较,所设计的控制器结构更为简单.应用Lyapunov方法,分析了观测误差和系统跟踪误差的有界性与收敛性,并且证明了闭环系统所有信号一致终结有界.仿真结果表明该控制方案具有良好的跟踪性能.     

非线性多智能体磁滞系统的分布式输出反馈渐近一致控制

针对一类具有磁滞输入且状态未知的非线性多智能体系统, 本文提出了一种基于领导者–跟随者的分布式 输出反馈渐近一致自适应控制方案. 首先, 构造了具有动态高增益的K-滤波器以估计多智能体系统的未知状态. 然 后, 采用一种新型的动态面控制策略设计控制器. 不同于传统动态面控制策略所采用的一阶低通滤波器, 本文设计 了含正时变积分函数的非线性滤波器, 该滤波器不仅能解决“微分爆炸”问题、降低计算负担, 而且能补偿传统动态 面的边界层误差, 使跟踪误差收敛到零. 理论分析表明: 该控制方案能有效地消除未知磁滞的影响, 确保整个闭环系 统的稳定性, 并使跟踪误差达到渐近收敛的目标. 最后, 通过仿真对所提出控制方案的有效性进行了分析和验证.     

一类非线性切换系统的自适应神经动态面控制

针对一类严格反馈型不确定非线性切换系统,提出了一种鲁棒自适应神经动态面跟踪控制方案.该方案在基于共同Lyapunov函数的后推法设计中引入动态面控制(dynamic surface control,DSC)技术,利用径向基神经网络逼近构造的未知共同上界函数,并将滤波器输出导数取代传统中间变量作为神经网络输入,降低了网络输入维度;同时利用Young’s不等式技术有效减少了神经网络控制器的可调参数数目.此外,理论证明了该控制方案可以保证在任意切换下闭环系统所有信号半全局一致终结有界,且跟踪误差在有限时间收敛到零的小邻域内.实验结果表明了所提方法达到了很好的跟踪性能.     

飞翼无人机复合连续非奇异终端滑模姿态跟踪容错控制

本文针对受多源干扰和舵面故障影响的飞翼无人机系统姿态跟踪控制问题进行研究, 提出了一种基于高阶滑模观测器的复合连续非奇异终端滑模主动抗干扰容错控制算法, 在实现姿态跟踪误差有限时间收敛的同时, 保证了控制量的连续. 并且针对控制力矩的具体实现问题, 结合飞翼无人机气动舵面冗余特性, 给出了基于加权伪逆算法的舵面分配方案, 该方案在满足舵面约束的情况下, 保证了舵面偏转角度的最优. 仿真结果表明, 所提控制方案显著提升了飞翼无人机姿态跟踪精度和跟踪误差的收敛速度, 并且保证了所有舵面满足偏角约束.     

一类严反馈块控非线性MIMO系统的动态面控制

针对一类具有未知不确定性的严反馈块控非线性多输入多输出(MIMO)系统,提出一种满足L∞跟踪性能的动态面鲁棒控制律设计方法.通过非线性阻尼项对未知不确定性进行补偿,动态面控制方法消除了反向递推(backstepping)设计方法中由于对虚拟控制反复求导而导致的复杂性问题.基于李亚普诺夫稳定性定理证明了闭环系统的所有信号半全局一致最终有界,通过适当选择设计参数及初始化动态面变量,跟踪误差可收敛到原点的一个任意小邻域内,且可以保证系统各个输出跟踪误差的L∞性能.数值仿真验证了方法的有效性.     

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.     

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.     

Robust adaptive tracking control of MIMO nonlinear systems in the presence of actuator hysteresis

Adaptive tracking control of a class of MIMO nonlinear system preceded by unknown hysteresis is investigated. Based on dynamic surface control, an adaptive robust control law is developed and compensators are designed to mitigate the influences of both the unknown bounded external uncertainties and the unknown Prandtl–Islinskii hysteresis. By adopting the low-pass filters, the explosion of complexity caused by tedious computation of the time derivatives of the virtual control laws is overcome. With the proposed control scheme, the closed-loop system is proved to be semi-globally ultimately bounded by the Lyapunov stability theory, and the output of the controlled system can track the desired trajectories with an arbitrarily small error. Finally, numerical simulations are given to verify the effectiveness of the proposed approach.     

Adaptive Pseudo Inverse Control for a Class of Nonlinear Asymmetric and Saturated Nonlinear Hysteretic Systems

This paper aims at eliminating the asymmetric and saturated hysteresis nonlinearities by designing hysteresis pseudo inverse compensator and robust adaptive dynamic surface control (DSC) scheme. The “pseudo inverse” means that an on-line calculation mechanism of approximate control signal is developed by applying a searching method to the designed temporary control signal where the true control signal is included. The main contributions are summarized as: 1) to our best knowledge, it is the first time to compensate the asymmetric and saturated hysteresis by using hysteresis pseudo inverse compensator because the construction of the true saturated-type hysteresis inverse model is very difficult; 2) by designing the saturated-type hysteresis pseudo inverse compensator, the construction of true explicit hysteresis inverse and the identifications of its corresponding unknown parameters are not required when dealing with the saturated-type hysteresis; 3) by combining DSC technique with the tracking error transformed function, the “explosion of complexity” problem in backstepping method is overcome and the prespecified tracking performance is achieved. Analysis of stability and experimental results on the hardware-in-loop platform illustrate the effectiveness of the proposed adaptive pseudo inverse control scheme.      

迟滞非线性动态系统神经网络自适应控制

为了减轻非线性动态系统中未知迟滞(Hysteresis)的不良影响，该文提出了一类Backlash型迟滞模型。将有限数量不同宽度的Backlash(Matlab／Simulink)算子进行叠加，来仿真执行器中的迟滞非线性动态。用此模型，提出了基于径向基函数神经网络的自适应控制方案，以控制伴有未知迟滞的非线性动态系统。该方案采用了动态逆的思想及伪控制的概念。利用Lyapunov稳定理论，设计了两个鲁棒控制项，保证动态系统的稳定性、系统中所有信号有界和误差收敛到起点的领域内。通过Matlab／Simulink仿真实验，证明了所提出方案的有效性。     

Modeling and inverse adaptive control of asymmetric hysteresis systems with applications to magnetostrictive actuator

When uncertain systems are actuated by smart material based actuators, the systems exhibit hysteresis nonlinearities and corresponding control is becoming a challenging task, especially with magnetostrictive actuators which are dominated by asymmetric hystereses. The common approach for overcoming the hysteresis effect is inverse compensation combining with robust adaptive control. Focusing on the asymmetric hysteresis phenomenon, an asymmetric shifted Prandtl–Ishlinskii (ASPI) model and its inverse are developed and a corresponding analytical expression for the inverse compensation error is derived. Then, a prescribed adaptive control method is applied to mitigate the compensation error and simultaneously guaranteeing global stability of the closed loop system with a prescribed transient and steady-state performance of the tracking error without knowledge of system parameters. The effectiveness of the proposed control scheme is validated on a magnetostrictive actuated platform.     

未知控制方向的迟滞非线性系统预设自适应控制

针对一类含有迟滞特性的未知控制方向严反馈非线性系统,设计了基于误差变换的反步自适应控制器.首先提出动态迟滞算子来扩展输入空间建立神经网络迟滞模型.然后利用径向基函数(RBF)神经网络逼近未知函数,并引入Nussbaum型函数来解决系统未知控制方向问题.最后采用误差变换将误差限定在预设的范围内,并利用反步法设计自适应控制器.该控制方案不仅能够保证跟踪精度,还可以提高系统暂态和稳态性能.仿真结果表明了控制方案的可行性.     

Design and experimental evaluation of a dynamical adaptive backstepping-sliding mode control scheme for positioning of an antagonistically paired pneumatic artificial muscles driven actuating system

Pneumatic artificial muscles (PAMs) are a class of pneumatic drives that have received considerable attention for applications related to bio-inspired robotics. Nevertheless, servo control of PAMs is challenging due to the compressibility and nonlinear flow characteristics of air, hysteresis behaviour as well as uncertainties present. In this paper, positioning of an antagonistically paired PAM with mass flow rate of compressed air regulated by a 5/3-way proportional directional valve and driven by a dynamical adaptive backstepping-sliding mode control (DAB-SMC) scheme is investigated. Implemented for the first time on a PAM-driven actuating system, derivation of this model-based nonlinear control scheme is presented first followed by experimental evaluation. Positioning performance is studied using a sinusoidal trajectory with tracking frequencies of 0.05, 0.1, 0.2 and 0.5 Hz, and a multiple-step polynomial input having step sizes of 0.7°, 1.4°, 2.9° and 5.7°. Over various operating conditions, average root mean square error value of 0.16° and steady-state error value of 0.04° are achieved for position tracking and regulating, respectively. The adaptive LuGre friction observer embedded in the control scheme effectively compensates hysteresis behaviour of the PAMs and helps to improve the performance. The proposed DAB-SMC scheme outperforms the classical SMC scheme by 33% in accuracy. The control scheme has also demonstrated a robust performance towards the uncertainties including loading. In addition, a slight performance compromise in both tracking and regulating tasks was observed, when the 5/3-way proportional valve is replaced by cost-effective 2/2-way pulse width modulation controlled on–off valves.     

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.     

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.