Composite learning fuzzy synchronization for incommensurate fractional‐order chaotic systems with time‐varying delays

This paper presents a composite learning fuzzy control to synchronize two different uncertain incommensurate fractional‐order time‐varying delayed chaotic systems with unknown external disturbances and mismatched parametric uncertainties via the Takagi‐Sugeno fuzzy method. An adaptive controller together with fractional‐order composite learning laws is designed based on both a parallel distributed compensation technology and a fractional Lyapunov criterion. The boundedness of all variables in the closed‐loop system and the Mittag‐Leffler stability of tracking error can be guaranteed. T‐S fuzzy systems are provided to tackle unknown nonlinear functions. The distinctive features of the proposed approach consist in the following: (1) a supervisory control law is designed to compensate the lumped disturbances; (2) both the prediction error and the tracking error are used to estimate the unknown fuzzy system parameters; (3) parameter convergence can be ensured by an interval excitation condition. Finally, the feasibility of the proposed control strategy is demonstrated throughout an illustrative example.     

为提高轮廓加工精度采用DOB和ZPETC的直线伺服鲁棒跟踪控制

为了提高轮廓加工精度,本文针对高精度直线伺服系统,提出了一种将零相位误差跟踪控制器(ZPETC)和干扰观测器(DOB)相结合的鲁棒跟踪控制策略.ZPETC作为前馈跟踪控制器,保证了快速性,使系统实现准确跟踪;基于DOB的鲁棒反馈控制器补偿了外部扰动、未建模动态、系统参数变化和机械非线性等,保证了系统的强鲁棒性能.仿真结果表明了所提出的控制方案是有效的,既能实现完好跟踪,又有较强的鲁棒性能.从而有效地减小了轮廓误差,提高了轮廓加工精度.     

Online data‐driven composite adaptive backstepping control with exact differentiators

This paper presents an online data‐driven composite adaptive backstepping control for a class of parametric strict‐feedback nonlinear systems with mismatched uncertainties, where both tracking errors and prediction errors are utilized to update parametric estimates. Hybrid exact differentiators are applied to obtain the derivatives of virtual control inputs such that the complexity problem of integrator backstepping can be avoided. Closed‐loop tracking error equations are integrated in a moving‐time window to generate prediction errors such that online recorded data can be utilized to improve parameter adaptation. Semiglobal asymptotic stability of the closed‐loop system is rigorously established by the time‐scales separation and Lyapunov synthesis. The proposed composite adaptation can not only avoid the application of identification models and linear filters resulting in a simpler control structure, but also suppress parametric uncertainties and external perturbations via the time‐interval integral. Simulation results have demonstrated that the proposed approach possesses superior control performances under both noise‐free and noisy‐measurement environments. Copyright © 2015 John Wiley & Sons, Ltd.     

基于RFNN补偿的直线伺服系统反推控制

针对永磁直线同步电机(PMLSM)伺服系统,在分析影响直线伺服跟踪精度因素的基础上,采用智能反推控制策略对该伺服系统进行有效的补偿控制。考虑参数变化、外部负载扰动和摩擦力等不确定因素对系统伺服性能的影响,设计基于递归模糊神经网络(RFNN)的反推控制器,利用了递归神经网络具有捕获系统动态信息的优点,可实时补偿不确定因素对跟踪性能的影响。仿真结果表明,控制策略明显降低了不确定因素对系统性能的影响,从而显著提高了直线伺服系统的位置跟踪精度。     

Adaptive backstepping repetitive learning control design for nonlinear discrete‐time systems with periodic uncertainties

This paper addresses a tracking problem for uncertain nonlinear discrete‐time systems in which the uncertainties, including parametric uncertainty and external disturbance, are periodic with known periodicity. Repetitive learning control (RLC) is an effective tool to deal with periodic unknown components. By using the backstepping procedures, an adaptive RLC law with periodic parameter estimation is designed. The overparameterization problem is overcome by postponing the parameter estimation to the last backstepping step, which could not be easily solved in robust adaptive control. It is shown that the proposed adaptive RLC law without overparameterization can guarantee the perfect tracking and boundedness of the states of the whole closed‐loop systems in presence of periodic uncertainties. In addition, the effectiveness of the developed controller is demonstrated by an implementation example on a single‐link flexible‐joint robot. Copyright © 2014 John Wiley & Sons, Ltd.     

Adaptive robust output‐feedback motion control of hydraulic actuators

Most previous advanced motion control of hydraulic actuators used full‐state feedback control techniques. However, in many cases, only position feedback is available, and thus, there are imperious demands for output‐feedback control for hydraulic systems. This paper firstly transforms a hydraulic model into an output feedback–dependent form. Thus, the K‐filter can be employed, which provides exponentially convergent estimates of the unmeasured states. Furthermore, this observer has an extended filter structure so that online parameter adaptation can be utilized. In addition, it is a well‐known fact that any realistic model of a hydraulic system suffers from significant extent of uncertain nonlinearities and parametric uncertainties. This paper constructs an adaptive robust controller with backstepping techniques, which is able to take into account not only the effect of parameter variations coming from various hydraulic parameters but also the effect of hard‐to‐model nonlinearities such as uncompensated friction forces, modeling errors, and external disturbances. Moreover, estimation errors that come from initial state estimates and uncompensated disturbances are dealt with via certain robust feedback at each step of the adaptive robust backstepping design. After that, a detailed stability analysis for the output‐feedback closed‐loop system is scrupulously checked, which shows that all states are bounded and that the controller achieves a guaranteed transient performance and final tracking accuracy in general and asymptotic output tracking in the presence of parametric uncertainties only. Extensive experimental results are obtained for a hydraulic actuator system and verify the high‐performance nature of the proposed output‐feedback control strategy.     

Adaptive neural network output feedback stabilization of nonlinear non‐minimum phase systems

This paper presents an adaptive output feedback stabilization method based on neural networks (NNs) for nonlinear non‐minimum phase systems. The proposed controller comprises a linear, a neuro‐adaptive, and an adaptive robustifying parts. The NN is designed to approximate the matched uncertainties of the system. The inputs of the NN are the tapped delays of the system input–output signals. In addition, an appropriate reference signal is proposed to compensate the unmatched uncertainties inherent in the internal system dynamics. The adaptation laws for the NN weights and adaptive gains are obtained using Lyapunov's direct method. These adaptation laws employ a linear observer of system dynamics that is realizable. The ultimate boundedness of the error signals are analytically shown using Lyapunov's method. The effectiveness of the proposed scheme is shown by applying to a translation oscillator rotational actuator model. Copyright © 2009 John Wiley & Sons, Ltd.     

Adaptive enhanced sliding mode control for permanent magnet synchronous motor drives

An adaptive enhanced sliding mode control (AESMC) scheme for the position tracking control of permanent magnet synchronous motor drives is proposed in this paper. The AESMC system is composed of three controllers: the adaptive model compensation controller, which is used to compensate for the parameter perturbations to achieve perfect tracking; the hitting controller, which is considered to attenuate the effect of external load disturbance and the compensation error; and the robust feedback controller, which is used to enhance the stability of the closed‐loop system and to improve the transient performance while the AESMC is in the learning process. Moreover, the bound of the lumped disturbance is assumed to be unknown, and an adaptive mechanism is investigated to estimate this bound. Simulation results show that the proposed AESMC scheme has a favorable tracking performance in spite of various model uncertainties. Copyright © 2015 John Wiley & Sons, Ltd.     

永磁直线同步电机的智能增量滑模控制

针对永磁直线同步电机直接驱动伺服系统的位置跟踪精度易受参数变化、外部扰动、端部效应等不确定性因素的影响,提出了一种将小波神经网络(wavelet neural network,WNN)和增量滑模控制器相结合的智能增量滑模控制方法。利用系统先前的状态信息和控制动作作为反馈量,同时选择饱和函数作为切换函数来设计增量滑模控制器,不仅削弱了抖振,而且提高了系统的跟踪性能;利用WNN实时观测和补偿参数变化和外部扰动等影响,并采用改进的粒子群优化算法在线调整WNN的学习率,对不确定因素进行实时估计。从理论上分析证明了此控制器可以保证系统收敛,提高了直线伺服系统的控制性能。通过系统实验,证明了所提出方案的有效性,与滑模控制(sliding mode control,SMC)相比,系统具有强鲁棒性和良好的位置跟踪精度,明显地削弱了抖振现象。     

基于ZPETC和DOB的永磁直线同步电机的鲁棒跟踪控制

针对高精度永磁直线同步电机直接驱动伺服系统，提出了一种将零相位误差跟踪控制器(ZPETC)和干扰观测器(DOB)相结合的鲁棒跟踪控制策略，以提高系统的跟踪性能和鲁棒性能。ZPETC作为前馈跟踪控制器，保证了快速性，使系统实现准确跟踪；基于DOB的鲁棒反馈控制器补偿了外部扰动、未建模动态、系统参数变化和机械非线性等，保证了系统的强鲁棒性。仿真结果表明，所提出的控制方案在保证系统实现完好跟踪的同时，又具有较强的鲁棒性，从而改善了数控机床进给系统的定位精度，进而提高了轮廓加工精度。     

An adaptive feedback linearized model predictive controller design for a nonlinear multi-input multi-output system

In this work, an adaptive feedback linearized model predictive control (AFLMPC) scheme is proposed to compensate system uncertainty for a class of nonlinear multi-input multi-output system. Initially, a feedback linearization technique is used to transform the nonlinear dynamics into an exact linear model, thereafter, a model predictive control scheme is designed to obtain the desired tracking performance. A suitable constraint mapping algorithm has been developed to map input constraints to the new virtual input of the proposed control scheme. The proposed control scheme utilizes multiple estimation model and the concept of second-level adaptation technique Pandey et al. (2014) to handle the parametric uncertainty in real-time. Hence, the adaptive term in the control scheme is used to counteract the effect of model uncertainties and parameter adaptation errors. The effectiveness of the proposed AFLMPC control algorithm has been verified successfully in simulation as well as the experimental setup of the TRMS model. The unavailable states of the nonlinear system have been estimated using an extended Kalman filter based state observer. The performance of the proposed control algorithm has been compared with other existing nonlinear control techniques in simulation and experimental validation.     

直驱式伺服系统的神经网络自适应滑模控制

针对直接驱动(DDV)伺服系统中由于参数变化、齿槽效应以及液动力负载扰动所造成的跟踪性能降低的问题,提出一种神经网络自适应滑模控制策略,采用径向基函数神经网络(RBFNN)取代滑模切换控制部分,利用其在线学习功能,对系统的不确定因素进行自适应补偿,并通过与比例微分算法(PD)的并行控制,改善神经网络参数的收敛,降低局部极小现象发生的可能性,增强系统的稳定性.仿真结果表明该方法不仅使系统具有良好的跟踪性能和强的鲁棒性,还有效地消除了高频抖振现象.     

Adaptive motion/force tracking control of holonomic constrained mechanical systems: a unified viewpoint

This paper proposes a robust adaptive motion/force tracking controller for holonomic constrained mechanical systems with parametric uncertainties and disturbances. First, two types of well‐known holonomic systems are reformulated as a unified control model. Based on the unified control model, an adaptive scheme is then developed in the presence of pure parametric uncertainty. The proposed controller guarantees asymptotic motion and force tracking without the need of extra conditions. Next, when considering external disturbances, control gains are designed by solving a linear matrix inequality (LMI) problem to achieve prescribed robust performance criterion. Indeed, arbitrary disturbance/parametric error attenuation with respect to both motion and force errors along with control input penalty are ensured in the L₂‐gain sense. Finally, applications are carried out on a two‐link constrained robot and two planar robots transporting a common object. Numerical simulation results show the expected performances. Copyright © 2006 John Wiley & Sons, Ltd.     

基于迭代滑模和扰动观测器的永磁同步电机转速控制

为了提高永磁同步电机（PMSM）控制系统的转速跟踪精度和鲁棒性,抑制其周期性转矩脉动,提出了一种基于积分滑模控制和迭代学习方法的PMSM单环控制策略。控制器采用单环滑模控制策略替代了传统的转速-电流级联控制,简化了控制系统的结构,提高了系统动态响应,通过引入迭代学习控制有效抑制了因电流谐波而导致的转矩脉动,提高了转速稳态控制精度。此外,针对系统存在的外部负载扰动、模型和参数不确定性等,设计了双重扰动观测器估计系统扰动量,提高了系统的鲁棒性。最后,针对所提复合控制策略进行了试验验证。试验结果表明,所提出的控制方法具有良好的动态性能、抗干扰能力和稳态控制精度。     

Discrete-time modified repetitive sliding mode control for uncertain linear systems

This article presents a design strategy and stability analysis of modified repetitive sliding mode controller for uncertain linear systems. A modified repetitive controller is adopted to simultaneously track and reject periodic signals. A discrete-time sliding mode controller is combined to compensate the slow response of repetitive control and to provide robustness against plant parameters uncertainties. Stability analysis is provided to prove boundedness of the proposed control law and the convergence of sliding function and the tracking error. Comparative simulation results demonstrate that the proposed method is able to accurately track reference signal and to reject disturbance with fast transient response. The results also indicate that the closed-loop system remains stable in the presence of plant parameter changes.     

二阶自抗扰控制器在三电机同步系统中的应用

分析了三电机同步控制系统数学模型,结合自抗扰控制理论特点,提出了一种新的基于二阶自抗扰控制器(ADRC)的三电机同步系统控制方案。设计了三个二阶ADRC分别对速度控制回路和两张力控制回路进行控制,实现了系统速度和张力之间的动态解耦。在二阶ADRC中,扩张状态观测器将系统模型内扰、外扰以及速度张力之间的耦合影响统一视为系统总扰动,对系统总扰动进行实时观测和补偿。结合西门子S7?300 PLC构建了实验平台,进行了解耦特性、跟踪性能和抗负载扰动能力测试实验。结果表明:二阶ADRC控制器不仅实现了三电机同步系统中速度和张力的解耦控制,还提高了系统的抗干扰能力,使系统具有较强的鲁棒性。     

含有未知扰动的阀控电液回转系统MFA-SM控制

针对阀控电液回转系统在围岩钻进过程中, 由于的参数不确定、未知负载以及外部扰动等非线性因素影响难以精确控制输出轴转速的问题, 设计了基于RBFNN扰动观测器的MFA-SM控制方案. 首先, 通过改进的动态线性化方法将电液系统等价线性化为仅与系统I/O数据相关的增量模型, 而未知负载及外部扰动则被合并为一个未知非线性时变项; 然后, 设计了RBFNN扰动观测器对该非线性项进行在线实时估计, 并根据系统的I/O数据来估计系统时变伪梯度参数;最后, 给出了相应的控制器设计。 仿真实验结果表明, 所设计的MFA-SM控制器能够对未知负载及外部干扰进行有效补偿, 相较于其他方法,该方案使得系统调节时间缩短了约10至15s, 最大超调量降低了7.4%左右, 且转速跟踪误差能够收敛到零。     

基于 GMS 摩擦力补偿和干扰观测器的高精度速度控制

为了降低机械轴系摩擦力扰动对于伺服控制器在低速运动控制精度的影响,进一步提高传统伺服控制器对于稳定平 台的控制能力,提出了一种基于广义 Maxwell 滑动(generalized Maxwell-slip,GMS) 摩擦力模型前馈和干扰观测器的高精度 摩擦力补偿方案。首先在传统控制基础上引入GMS 摩擦模型前馈补偿对摩擦扰动进行初步的补偿;然后,通过加入干扰观 测器,对残余扰动及其他扰动进行第2次的抑制。利用实物平台对控制方法的低速运动性能进行了测试,对比设计的控制算 法和传统 PI 控制器的控制结果,验证提出的控制策略抑制摩擦扰动的效果。结果表明,基于 GMS 摩擦力前馈和干扰观测器 的控制方案有效的补偿了摩擦非线性、模型不确定性等因素对于控制系统的影响。新方法可将稳定平台低速运动时的控制 误差降低到0.015°/s, 在实际工程中具有较高的应用价值。     

Neural network adaptive robust control with application to precision motion control of linear motors

In this paper, neural networks (NNs) and adaptive robust control (ARC) design philosophy are integrated to design performance‐oriented control laws for a class of single‐input–single‐output (SISO) nth‐order non‐ linear systems. Both repeatable (or state dependent) unknown non‐linearities and non‐repeatable unknown non‐linearities such as external disturbances are considered. In addition, unknown non‐linearities can exist in the control input channel as well. All unknown but repeatable non‐linear functions are approximated by outputs of multi‐layer neural networks to achieve a better model compensation for an improved performance. All NN weights are tuned on‐line with no prior training needed. In order to avoid the possible divergence of the on‐line tuning of neural network, discontinuous projection method with fictitious bounds is used in the NN weight adjusting laws to make sure that all NN weights are tuned within a prescribed range. By doing so, even in the presence of approximation error and non‐repeatable non‐linearities such as disturbances, a controlled learning is achieved and the possible destabilizing effect of on‐line tuning of NN weights is avoided. Certain robust control terms are constructed to attenuate various model uncertainties effectively for a guaranteed output tracking transient performance and a guaranteed final tracking accuracy in general. In addition, if the unknown repeatable model uncertainties are in the functional range of the neural networks and the ideal weights fall within the prescribed range, asymptotic output tracking is also achieved to retain the perfect learning capability of neural networks in the ideal situation. The proposed neural network adaptive control (NNARC) strategy is then applied to the precision motion control of a linear motor drive system to help to realize the high‐performance potential of such a drive technology. NN is employed to compensate for the effects of the lumped unknown non‐linearities due to the position dependent friction and electro‐magnetic ripple forces. Comparative experiments verify the high‐performance nature of the proposed NNARC. With an encoder resolution of 1 µm, for a low‐speed back‐and‐forth movement, the position tracking error is kept within ±2 µm during the most execution time while the maximum tracking error during the entire run is kept within ±5.6 µm. Copyright © 2001 John Wiley & Sons, Ltd.     

基于ESO的PMSM无差拍预测电流控制研究

在永磁同步电机调速系统中,优良的电流控制效果对系统的控制性能至关重要.为解决数字控制中存在的采样、滤波等因素带来的控制延迟,基于永磁同步电机在同步旋转坐标系下的数学模型,提出一种基于最小电流误差的无差拍预测电流控制策略.同时,为解决预测电流控制存在的对模型参数尤其是电机电感的参数鲁棒性较低的问题,结合自抗扰控制技术,在...