Novel model reference adaptive control architecture using semi‐initial excitation‐based switched parameter estimator

This paper is a generalization of the recently developed techniques of initial excitation (IE)–based adaptive control with an introduction to the definition of semi‐initial excitation (semi‐IE), a still more relaxed notion than IE. Classical adaptive controllers typically ensure Lyapunov stability of the extended error dynamics (tracking error + parameter estimation error) and asymptotic tracking, while requiring a stringent condition of persistence of excitation (PE) for parameter convergence. Of late, the authors have proposed a new adaptive control architecture, which guarantees parameter convergence under the online‐verifiable IE condition leading to exponential stability of the extended error dynamics. In earlier works, it has been established that the IE condition is significantly milder than the classical PE condition. The current work further slackens the excitation condition by proposing the concept of semi‐IE. The proposed adaptive controller is proved to ensure convergence of the parameter estimation error to a lower‐dimensional manifold under the weaker semi‐IE condition, while the stronger condition of IE guarantees convergence of the parameter estimation error to zero. The designed algorithm is shown to improve transient response of tracking error sufficiently in contrast to conventional adaptive controllers.     

Unknown piecewise constant parameters identification with exponential rate of convergence

The scope of this research is the identification of unknown piecewise constant parameters of linear regression equation under the finite excitation condition. Compared to the known methods, to make the computational burden lower, only one model to identify all switching states of the regression is used in the developed procedure with the following two-fold contribution. First of all, we propose a new truly online estimation algorithm based on a well-known DREM approach to detect switching time and preserve time alertness with adjustable detection delay. Second, despite the fact that a switching signal function is unknown, the adaptive law is derived that provides global exponential convergence of the regression parameters estimates to their true values in case the regressor is finitely exciting somewhere inside the time interval between two consecutive parameters switches. The robustness of the proposed identification procedure to the influence of external disturbances is analytically proved. Its effectiveness is demonstrated via numerical experiments, in which both abstract regressions and a second-order plant model are used.     

基于连续时间方法的励磁系统参数辨识

详细分析了用于励磁系统参数辨识的连续时间辨识方法——分段线性多项式函数(PLPF)法．指出PLPF法本质上是在整个时间区间内采用复化梯形公式的数值积分方法,并讨论了PLPF法的初始条件影响问题,在此基础上,引入了另一种连续时间辨识方法,线性积分滤波(LIF)法,使用LIF法可以完全消除积分方程初始条件的影响,即在非零初始条件下无需辨识初始条件依然能得到准确的结果。仿真实验中,就PLPF法及LIF法在初始条件问题和测量噪声影响方面进行了比较讨论,结果表明LIF法与PLPF法相比有一定的优越性。     

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.     

Robust adaptive control of robotic manipulators without the regressor matrix

A new adaptive controller is presented here for rigid-body robotic manipulators. It is stable and robust with respect to a class of external disturbances. The robustness of the adaptive controller is established without the ‘slow-varying’ assumption and the computationally demanding regressor matrix. The control law consists of a non-adaptive PD control part and an adaptive control part. It uses two adaptive matrices to compensate two uniformly bounded coefficient matrices derived from the original dynamics. A α σ|q?|-modified adaptive law is designed to adjust the adaptive matrices. A Lyapunov-type stability analysis indicates that the closed-loop system is uniformly ultimately bounded. The tracking error and compensation error will eventually converge into a closed region, which can be made arbitrarily small by adjusting the controller parameters. Simulation results are included to demonstrate the performance of the proposed controller.     

On‐line parameter estimation for a class of time‐varying continuous systems with bounded disturbances

In this paper, we proposed an on‐line parameter estimation algorithm for a class of time‐varying continuous systems with bounded disturbance. In this method, a novel polynomial approximator with a bounded regressor vector is constructed and utilized to approximate the time‐varying parameters. The direct least‐squares algorithm is employed to acquire the on‐line estimates, so that several useful properties of the direct estimation, such as fast convergence and robustness to the bounded disturbance, are reflected in our method. We have proved that the estimation error of this method is bounded. Furthermore, the bound on the Euclidean norm of the estimation error is derived. The simulation results demonstrate that this method can provide accurate estimates of time‐varying parameters even under the influence of bounded disturbance. Copyright © 2010 John Wiley & Sons, Ltd.     

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.     

Algorithms of adaptive tracking of unknown multisinusoidal signals in linear systems with arbitrary input delay

The problem of adaptive tracking control is addressed for the class of linear time‐invariant plants with known parameters and arbitrary known input delay. The reference signal is a priori unknown and is represented by a sum of biased harmonics with unknown amplitudes, frequencies, and phases. Asymptotic tracking is provided by predictive adjustable control with parameters generated by one of three designed adaptation algorithms. The first algorithm is based on a gradient scheme and ensures zero steady‐state tracking error with all signals bounded. The other two algorithms additionally involve the scheme with fast parametric convergence improving the closed‐loop system performance. In all the algorithms, the problem of delay compensation is resolved by special augmentation of tracking error. The adjustable control law proposed do not require identification of the reference signal parameters.     

Compound conditions event-triggered tracking control for nonlinear multiagent systems with nonlinear output faults

This article is devoted to investigating the compound conditions event-triggered prescribed performance tracking problem for strict-feedback nonlinear multiagent systems with nonlinear output faults and unknown disturbances. A simplified event-triggered sampling condition is designed to successfully reduce the number of state triggered design parameters. Besides, a compound conditions event-triggered mechanism is constructed, which integrates state triggered and controller triggered simultaneously to reduce communication burden. Furthermore, by using disturbance observers and prescribed performance functions, unknown external disturbances are compensated and the tracking errors can converge into the prescribed boundary, respectively. Moreover, all the signals of the closed-loop system are semiglobally uniformly ultimately bounded. Finally, the availability of the proposed control strategy is testified via simulation results.     

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.     

Robust adaptive dynamic surface control for linear time‐varying systems

In this paper, robust output‐feedback tracking control is considered for a class of linear time‐varying plants whose time‐varying parameters are unknown bounded with bounded derivatives and output is affected by unknown bounded additive disturbances. Using adaptive dynamic surface control technique, the proposed scheme possesses the following advantages: (1) the design procedure is simple and the control law is easy to be implemented, and (2) by introducing an initialization technique, together with adjusting some design parameters, the performance of system tracking error can be guaranteed regardless of the time variation. It is proved that with the proposed scheme, all the closed‐loop signals are semi‐globally uniformly ultimately bounded. Simulation results are presented to demonstrate the effectiveness of the proposed scheme. Copyright © 2013 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.     

Robust adaptive steering control for unmanned surface vehicle with unknown control direction and input saturation

A robust adaptive steering control method is proposed to solve the control problem of the unmanned surface vehicle (USV) with uncertainties, unknown control direction, and input saturation. In the controller design process, the adaptive fuzzy system is incorporated into dynamic surface control (DSC) to approximate the uncertainty term induced by external environmental disturbances and model parameters. Then, the Nussbaum function is used to eliminate the requirement for a priori knowledge of the control direction. Besides, to handle the input saturation, the adaptive fuzzy DSC is extended by a second‐order nonlinear filter and antisaturation auxiliary function to compensate for the magnitude and rate saturation of the rudder. All signals of the closed‐loop system are proven to be uniformly ultimately bounded (UUB) by Lyapunov theorem and the Lemma of Nussbaum gain, and the course error can converge to a small neighborhood of zero through choosing design parameters appropriately. Finally, simulation results and comprehensive comparisons are shown for the USV course system, which is demonstrative of the proposed controller's effectiveness and robustness.     

Reinforcing robustness of adaptive dynamic surface control

Adaptive dynamic surface control (ADSC) design was proposed as an alternative to adaptive backstepping, capable of curing the ‘explosion of complexity’ problem, caused by the repeated differentiations of the so called intermediate control signals. However, as it is clearly demonstrated in this work, ADSC schemes are sensitive to modeling uncertainties and/or additive external disturbances. In fact, it is shown that a uniformly bounded exogenous perturbation of unknown upper bound may easily destabilize the closed‐loop system. Subsequently, a constructive methodology based on the recently developed by the authors prescribed performance control technique, is proposed, which combined with an ADSC design, results in a modified scheme possessing significantly increased robustness properties. Simulation studies illustrate the approach. Copyright © 2012 John Wiley & Sons, Ltd.     

一种新颖的永磁同步电动机起动策略的研究

永磁同步电动机起动时如果知道转子的初始位置,起动将会十分简便.但是,永磁同步电动机的转子初始位置确定或估计一直是个难题.在研究电动机静起动转矩的基础上提出了一种新的起动策略,较好地解决了在转子初始位置未知情况下电动机的起动.此方法不需要依赖电机的任何参数,而且方法简单.理论分析和实验结果均表明此方法是可行的.     

水轮机尼科尔斯比例–积分–微分调速器设计

根据闭环系统谐振峰值 Mr 与系统响应最大峰值 Mp之间的关系,构建一个与系统参数及控制器参数都相关的优化问题,通过该问题的求解获得控制器参数与系统参数之间的数学关系.针对线性水轮机模型,设计了基于尼科尔斯曲线的 PID 控制器.系统负荷干扰及参数摄动的仿真结果表明:所设计的 PID 控制器对系统参数摄动及外部有界干扰信号具有很好的鲁棒性能,系统过渡过程性能明显优于传统PID 调节器结果.     

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.     

不确定混沌系统的鲁棒自适应容错同步控制

针对一类带有执行器故障、非线性不确定性以及外界干扰的混沌系统,设计一个鲁棒自适应容错控制器,来实现主从混沌系统之间的同步容错控制。不需要知晓准确的故障信息以及外界干扰的上界,根据自适应律在线估计未知量。结合自适应控制技术,设计鲁棒自适应容错控制器,使得无论执行器是否有故障发生,主从两个混沌系统都能够实现同步。以经典的蔡氏混沌电路系统为例进行数值仿真,仿真结果验证了所设计控制器的有效性和可行性。     

基于ADRC的发电机励磁系统控制研究

针对带有参数不确定及外部扰动的励磁系统数学模型,采用自抗扰控制技术(ADRC)设计了励磁控制器。该方法不需要知道系统的内部模型,能有效克服系统的不确定性,系统响应快,算法简单,易于实现。仿真结果表明,这种控制方法能实时地估计与补偿系统内外扰动,有效提高了电压的控制精度,而且解决了励磁控制的快速性与超调性之间的矛盾,提高了系统的动态性能。     

A dual filtering scheme for nonlinear active noise control

Active noise control problems are often affected by nonlinear effects such as distortion and saturation of measurement and actuation devices, which call for suitable nonlinear models and algorithms. The active noise control problem can be interpreted as an indirect model identification problem, due to the secondary path dynamics that follow the control filter block. This complicates the weight update mechanism in the nonlinear case, in that the error gradient depends on the secondary path gradient through nonlinear recursions. A simpler and computationally less demanding approach is here proposed that employs the updating scheme of the standard filtered‐x least mean squares (LMS) or filtered‐u LMS algorithm. As in those schemes, the calculation of the error gradient requires a signal filtering through an auxiliary system, here obtained through a secondary adaptation loop. The resulting dual filtering LMS algorithm performs the adaptation of the controller parameters in a direct identification mode and can therefore be easily coupled with adaptive model structure selection schemes to provide online tuning of the model structure, for improved model robustness. Copyright © 2013 John Wiley & Sons, Ltd.