执行器非线性超低空空投航迹倾角自适应控制

针对超低空空投下滑阶段执行器非线性、外界不确定性大气扰动以及模型存在未知非线性等因素干扰轨迹精确跟踪问题,提出一种鲁棒自适应神经网络动态面跟踪控制方法。建立了含执行器输入非线性的超低空空投载机纵向非线性模型,采用神经网络逼近模型中未知非线性函数,引入非线性鲁棒补偿项消除了执行器非线性建模误差和外界扰动。应用Lyapunov稳定性理论证明了闭环系统所有信号均是有界收敛的。仿真验证了所提方法既保证了轨迹跟踪的精确性又具有较强的鲁棒性。     

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.     

Adaptive neural prescribed performance output feedback control of pure feedback nonlinear systems using disturbance observer

In this study, an adaptive output feedback control with prescribed performance is proposed for unknown pure feedback nonlinear systems with external disturbances and unmeasured states. A novel prescribed performance function is developed and incorporated into an output error transformation to achieve tracking control with prescribed performance. To handle the unknown non-affine nonlinearities and avoid the algebraic loop problem, the radial basis function neural network (RBFNN) is adopted to approximate the unknown non-affine nonlinearities with the help of Butterworth low-pass filter. Based on the output of the RBFNN, the coupled design between sate observer and disturbance observer is presented to estimate the unmeasured states and compounded disturbances. Then, the adaptive output feedback control scheme is proposed for unknown pure feedback nonlinear systems, where a first-order filter is introduced to tackle with the issue of “explosion of complexity” in the traditional back-stepping approach. The boundedness and convergence of the closed-loop system are proved rigorously by utilizing the Lyapunov stability theorem. Finally, simulation studies are worked out to demonstrate the effectiveness of the proposed scheme.     

Composite adaptive finite-time fuzzy control for switched nonlinear systems with preassigned performance

This article investigates the composite adaptive fuzzy finite-time prescribed performance control issue of switched nonlinear systems subject to the unknown external disturbance and performance requirement. First, by utilizing the compensation and prediction errors, the piecewise switched composite parameter update law is employed to improve the approximation accuracy of the unknown nonlinearity. Then, the improved fractional-order filter and error compensation signal are introduced to cope with the influences caused by the explosive calculation and filter error, respectively. Meanwhile, the effect of the compound disturbances consisting of the unknown disturbances and approximation errors is reduced appropriately by designing the piecewise switched nonlinear disturbance observer. Moreover, stability analysis results prove that the proposed preassigned performance control scheme not only ensures that all states of the closed-loop system are practical finite-time bounded, but also that the tracking error converges to a preassigned area with a finite time. Ultimately, the simulation examples are given to demonstrate the effectiveness of the proposed control strategy.     

Robust nonlinear adaptive backstepping excitation controller design for rejecting external disturbances in multimachine power systems

This paper proposes a new approach to design a robust adaptive backstepping excitation controller for multimachine power systems in order to reject external disturbances. The parameters which significantly affect the stability of power systems (also called stability sensitive parameters) are considered as unknown and the external disturbances are incorporated into the power system model. The proposed excitation controller is designed in such a way that it is adaptive to the unknown parameters and robust to external disturbances. The stability sensitive parameters are estimated through the adaptation laws and the convergences of these adaptation laws are obtained through the negative semi-definiteness of control Lyapunov functions (CLFs). The proposed controller not only provides robustness property against external disturbances but also overcomes the over-parameterization problem of stability sensitive parameters which usually appears in some conventional adaptive methods. Finally, the performance of the proposed controller is tested on a two-area four machine 11-bus power system by considering external disturbances under different scenarios and is compared to that of an existing nonlinear adaptive backstepping controller. Simulation results illustrate the robustness of the proposed controller over an existing one in terms of rejecting external disturbances.     

Adaptive recurrent Chebyshev neural network control for PM synchronous motor servo‐drive electric scooter with V‐belt continuously variable transmission

Because of unknown nonlinearity and time‐varying characteristics of electric scooter with V‐belt continuously variable transmission (CVT) driven by permanent magnet synchronous motor (PMSM), its accurate dynamic model is difficult to establish for the design of the linear controller in whole system. In order to conquer this difficulty and raise robustness, an adaptive recurrent Chebyshev neural network (NN) control system is proposed to control for PMSM servo‐drive electric scooter with V‐belt CVT under lumped nonlinear external disturbances in this study. The adaptive recurrent Chebyshev NN control system consists of a recurrent Chebyshev NN control and a compensated control with estimation law. In addition, the online parameters tuning methodology of the recurrent Chebyshev NN and the estimation law of the compensated controller can be derived by using the Lyapunov stability theorem. Moreover, the two optimal learning rates of the recurrent Chebyshev NN based on a discrete‐type Lyapunov function are proposed to guarantee the convergence of tracking error. Finally, comparative studies are demonstrated by experimental results in order to show the effectiveness of the proposed control scheme. Copyright © 2014 John Wiley & Sons, Ltd.     

Distributed cooperative neural control of a class of nonlinear multi-agent systems with unknown time-varying control coefficient

This article studies the leader–follower cooperative tracking problem of a class of multi-agent systems with unknown nonlinear dynamics. As the load of the following agent may be changing throughout the whole work process, we consider the control coefficient of the following agent to be time-varying and nonlinear instead of constant, which is more practical. All agents are connected by the directed communication graph with weighted topology. The followers can have unknown nonidentical nonlinear dynamics and external disturbances. The nonautonomous leader generates the reference trajectory for only part of the followers and others can only receive the information from their neighbors. To achieve the ultimate synchronization of all following agents to the leader, the novel cooperative adaptive control protocols are designed based on the neural approximation and adaptive updating mechanism. A novel singularity-avoided adaptive updating law is proposed to estimate the control coefficient and compensate for the unknown dynamics online. Lyapunov theory is used to prove the ultimate boundedness of the synchronization tracking error. The correctness and effectiveness of the presented control scheme are demonstrated by two simulations in SISO and MIMO cases, respectively.     

一类非线性系统的自组织模糊神经网络控制

针对一类MIMO不确定非线性有干扰且控制增益符号未知的系统进行跟踪控制的问题,提出了一种在线自组织模糊神经网络的改进算法,用以克服参数选择困难的问题,并基于该算法给出了一种自适应鲁棒控制方法。首先基于主导输入的概念将MIMO系统分解为多个SISO系统构成的系统,然后结合自组织模糊神经网络在线对系统中的未知函数进行逼近,对网络结构和参数实现在线调节,再利用Nussbaum函数来克服控制增益符号未知,并且引入鲁棒项及复合误差的估计来补偿复合误差。最后基于Lyapunov稳定性理论证明了整个闭环系统半全局一致最终有界。理论和仿真结果表明提出方法的有效性。     

Adaptive model predictive control with extended state observer for multi-UAV formation flight

This article studies the adaptive model predictive control with extended state observers (ESO) to deal with multiple unmanned aerial vehicles formation flight in presence of external disturbances and system uncertainties. Specifically, to deal with the mismatch of predictive model caused by external disturbances and system uncertainties, ESOs are introduced to estimate the lumped disturbances, where the ultimately bounded property of observer system can be guaranteed by using the Lyapunov stability theorem. With these observations, the distributed adaptive model predictive controller is designed to achieve trajectory tracking and disturbance rejection simultaneously for multiple unmanned aerial vehicles, as well as taking the state and input saturation into account. Moreover, the stability of proposed model predictive controller is analyzed. Finally, the simulation examples are provided to illustrate the validity of the proposed control scheme.     

Fixed-time fault-tolerant control of uncertain MIMO switched interconnected systems subject to state delay,unknown control directions,and quantized nonlinear inputs

This paper investigates design of an adaptive fixed-time fault-tolerant decentralized controller for a class of uncertain multi-input multi-output (MIMO) switched large-scale non-strict interconnected systems under arbitrary switching subject to unknown control directions, quantized nonlinear inputs, actuator failures unknown external disturbances, and unmodeled dynamics. In addition to interconnected terms, time-varying delayed interconnected terms have been considered in the system model which makes it more general than previous works in the literature. The proposed controller can handle switched systems with unknown switching signal and different types of input nonlinearities including, saturation, backlash, and dead-zone. The singularity problem in designing the fixed time controller has been solved. The quantizer and actuators fault parameters are assumed to be unknown. The Razumikhin lemma has been used to deal with the delayed interconnected terms. To cope with the system unknown dynamics, neural networks (NNs) have been applied and by updating the maximum norms of the networks weight vectors the computational load has been reduced. The explosion of complexity occurring in the traditional back-stepping technique has been avoided by applying dynamic surface control (DSC). Finally, by defining an appropriate common Lyapunov function (CLF), fixed-time convergence of system outputs and the closed-loop system stability have been established. The effectiveness of the proposed controller has been shown via simulation study.     

可控串联补偿的神经滑模控制器设计

为克服电力系统可控串联补偿装置非线性及外部扰动影响,应用反馈线性化方法和径向基神经滑模变结构控制理论,设计了可控串联补偿的神经滑模控制器。通过状态反馈方法对非线性模型精确线性化,运用径向基神经网络的非线性映射和自学习能力自适应调整滑模控制律,使得设计的可控串联补偿控制规律简洁,鲁棒性好。仿真结果表明,与传统的控制方式相比,设计的神经滑模控制器能有效地阻尼系统振荡,增强系统的暂态稳定性,对运行点变化也具有较好的适应性。     

Observer-based robust adaptive control of PMSM with initial rotor position uncertainty

In this paper, a mathematical model of permanent-magnet synchronous motor (PMSM) with initial rotor position uncertainty is derived and its control methodology is proposed. Based on Lyapunov stability theory, an observer-based robust adaptive position and speed-tracking control system for the PMSM is developed given that all parameters including load inertia and motor parameters are unknown, acceleration is not measurable and friction and external disturbances are bounded. An incremental encoder which provides relative position of the rotor is used along with stator current signals to achieve stable control. The simulation and experimental results have proven the stability and efficacy of the proposed control law.     

基于超螺旋滑模观测器的永磁同步直线电机无模型控制

针对永磁同步直线电机(permanent magnet linear synchronous motor, PMLSM)在运行过程中因参数失配和外部扰动导致控制性能下降的问题,提出一种基于超螺旋滑模观测器的永磁同步直线电机无模型控制策略。根据PMLSM在dq旋转坐标系下参数摄动时的数学模型建立电机对应的新型超局部模型,以避免参数失配。基于该新型超局部模型,结合滑模控制设计了无模型滑模速度控制器,通过Lyapunov稳定性理论证明该控制器的稳定性。同时,为削弱传统扩展滑模观测器对新型超局部模型中未知量观测的抖振,提高控制精度,设计超螺旋滑模观测器(super-twisting sliding mode observer, STSMO)对未知量进行在线辨识并实现前馈补偿。最后,将传统滑模控制、基于传统扩展滑模观测器的无模型控制算法与所提方法进行仿真和硬件在环实验对比,结果表明所提方法改善了PMLSM控制系统的动态响应性能,具有较强的鲁棒性。     

不确定性时滞关联大系统的分散变结构自适应控制

针对一类具有系统参数摄动、时滞关联和外界干扰的线性大系统，提出了一种基于Lyapunov稳定性理论的分散变结构自适应控制方案。通过引入积分滑模和能在线估计不确定性扰动与时滞关联的界的自适应算法，保证了闭环系统的渐近稳定性，实现了系统的鲁棒自适应控制。该方案具有较强的工程实用性，算例仿真的结果表明了该控制方案的可行性。     

高精度定位系统的摩擦力自适应前馈补偿

为了有效抑制机电系统摩擦力等外部扰动对系统动态性能的影响,针对直驱伺服系统中往复定位存在的摩擦力,提出了一种基于自适应前馈控制器的摩擦力补偿策略,此方法能够有效利用参考模型与被控对象的位置跟踪误差等信息,在线实时确定自适应控制率,在保证系统稳定的条件下,能够有效克服系统摩擦力及模型慢时变等引起的系统动态性能异常。针对直驱伺服系统建立其数学模型,根据数学模型确定自适应补偿环节的数学形式,并利用Lyapunov函数证明了自适应控制率的稳定性。最后通过试验表明,在跟踪正弦位置指令时,基于自适应前馈补偿的方法动态跟踪误差的均方根值为4.8μm,与PID无摩擦补偿控制方法相比,提高了47.3%,与传统模型参考自适应控制方法相比,提高了17.9%。综上所述,所提方法可以有效抑制系统摩擦力干扰,提高系统动态跟踪精度。     

Decentralized adaptive terminal sliding mode control design for nonlinear connected system in the presence of external disturbance

In this article, a decentralized adaptive integral terminal sliding mode control is presented for a class of nonlinear connected systems. It is assumed that the system is also confronted by unknown disturbances, while the interconnections between subsystems are assumed unknown. An integral terminal sliding surface for each subsystem is locally considered to guarantee the stability of the closed-loop system, and to increase the convergence speed during a tracking task. The unknown interconnections between subsystems are estimated using adaptive rules. An appropriate Lyapunov candidate is chosen to perform global stability analysis. In this regard, design parameters are chosen such that the closed-loop stability is ensured. Performance of the proposed method for a mechanical connected system, including two chaotic subsystems, is shown through simulations.     

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.     

Predictor‐based iterative neural dynamic surface control for three‐phase voltage source PWM rectifier

This paper addresses the control problem of a three‐phase voltage source pulse width modulation rectifier in the presence of parametric uncertainties and external time‐varying disturbances. An adaptive controller is designed by combining a modified dynamic surface control method and a predictor‐based iterative neural network control algorithm. Especially, neural networks with iterative update laws based on prediction errors are employed to identify the lumped uncertainties. Besides, a finite‐time‐convergent differentiator, instead of a first‐order filter, is used to obtain the time derivative of the virtual control law. Using a Lyapunov–Krasovskii functional, it is proved that all signals in the closed‐loop system are ultimately uniformly bounded. Both simulation and experimental studies are provided to show the effectiveness of the proposed approach. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Reliable nonfragile tracking control for switched systems with external disturbances

With the assistance of reliable control technique, the nonfragile tracking problem has been proposed in this paper for a class of switched systems with external disturbances under the aegis of modified repetitive controller. Notably, the designed repetitive controller is used to improve the tracking performance of the addressed switched systems. Preciously, the influence of external disturbances are estimated through the improved equivalent-input-disturbance strategy, wherein the effect caused by the external disturbances to the output channel are reduced by the aid of modified repetitive control strategy. The fundamental intention of this control synthesis is that the output of the system precisely tracks the reference signal even in the presence of external disturbances and gain fluctuations. For that cause, Lyapunov stability technique in conjunction with average dwell time approach is implemented to obtain adequate conditions in the shape of linear matrix inequalities which insists the exponential stability for the addressed system. Ultimately, the efficiency and supremacy of the proposed control schemes are justified via two numerical examples, wherein it is exposed to view that the developed control strategy is capable of good tracking performance and also estimates the external disturbances efficiently.