STATCOM的自适应逆控制方法

为了研究静止同步补偿器STATCOM运行过程中存在的非线性特性对补偿特性的影响,在考虑了电压源型逆变桥中IGBT的通态损耗、开关损耗以及直流侧的电容损耗基础上,建立了基于该逆变桥的STATCOM数学模型,并根据其数学模型的非线性特性,提出了STATCOM的自适应逆控制方法和逆系统模型。根据该模型对STATCOM进行直接控制,并根据系统参数的不可预知以及其时变的特点,对系统模型中的参数在运行过程进行在线识别,并进行了仿真。结果表明,自适应逆控制能够有效地克服运行过程中系统参数的变化对补偿效果的影响,从而提高了控制系统的鲁棒性和装置的控制精确度;给定系统参数的初值准确与否,对装置最终的控制精确度没有影响。该控制方法具有实用性,而且随着处理器计算能力的不断增强该方法存在广泛的应用前帚。     

Adaptive output feedback dynamic surface control of stochastic nonlinear systems with state and input unmodeled dynamics

In this paper, an adaptive neural output feedback control scheme is investigated for a class of stochastic nonlinear systems with unmeasured states and four kinds of uncertainties including uncertain nonlinear function, dynamic disturbance, input unmodeled dynamics, and stochastic inverse dynamics. The unmeasured states are estimated by K‐filters, and stochastic inverse dynamics is dealt with by constructing a changing supply function. The considered input unmodeled dynamic subsystem possesses nonlinear feature, and a dynamic normalization signal is introduced to counteract the unstable effect produced by the input unmodeled dynamics. Combining dynamic surface control technique with stochastic input‐to‐state stability, small‐gain condition, and Chebyshev's inequality, the designed robust adaptive controller can guarantee that all the signals in the closed‐loop system are bounded in probability, and the error signals are semi‐globally uniformly ultimately bounded in mean square or the sense of four‐moment. Simulation results are provided to verify the effectiveness of the proposed approach. Copyright © 2015 John Wiley & Sons, Ltd.     

Adaptive control of systems with unknown non-smooth non-linearities

In this paper we unify our recent results in adaptive control of systems with unknown non-smooth non-linearities such as dead-zone, backlash and hysteresis characteristics at the input or output of a linear dynamics. Our adaptive inverse approach employs an adaptive controller structure consisting of an adaptive inverse for cancelling the effect of an unknown non-linearity and a fixed (or adaptive) linear control law for a known (or unknown) linear dynamics. Despite the bilinear dependence on the unknown parameters, a linearly parametrized error system is constructed which enables us to design robust adaptive laws for updating the controller parameters to ensure closed loop signal boundedness and improve system tracking performance. © 1997 by John Wiley & Sons, Ltd.     

Adaptive controller design and disturbance attenuation for SISO linear systems with zero relative degree under noisy output measurements

In this paper, we present robust adaptive controller design for SISO linear systems with zero relative degree under noisy output measurements. We formulate the robust adaptive control problem as a nonlinear H^∞‐optimal control problem under imperfect state measurements, and then solve it using game theory. By using the a priori knowledge of the parameter vector, we apply a soft projection algorithm, which guarantees the robustness property of the closed‐loop system without any persistency of excitation assumption of the reference signal. Owing to our formulation in state space, we allow the true system to be uncontrollable, as long as the uncontrollable part is stable in the sense of Lyapunov, and the uncontrollable modes on the jω‐axis are uncontrollable from the exogenous disturbance input. This assumption allows the adaptive controller to asymptotically cancel out, at the output, the effect of exogenous sinusoidal disturbance inputs with unknown magnitude, phase, and frequency. These strong robustness properties are illustrated by a numerical example. Copyright © 2009 John Wiley & Sons, Ltd.     

过热汽温自适应逆控制方案研究

针对火电厂锅炉过热汽温对象,将神经网络辨识技术和自适应逆控制技术相结合,提出了一种过热汽温自适应逆控制方案.该方案首先利用RBF神经网络在线辨识被控对象模型获得其Jacobian信息,在此基础上利用对角回归神经网络(DRNN)在线辨识获得被控对象的逆模型作为控制器,串联在控制对象前面构成自适应逆控制系统.通过对超临界600 MW机组过热汽温对象进行仿真研究表明,该控制方案能很好地适应过热汽温对象特性的变化,并且可以有效克服对象的大惯性和非线性,获得良好的控制品质.     

Reduced‐order robust adaptive control design of uncertain SISO linear systems

In this paper, a stability and robustness preserving adaptive controller order‐reduction method is developed for a class of uncertain linear systems affected by system and measurement noises. In this method, we immediately start the integrator backstepping procedure of the controller design without first stabilizing a filtered dynamics of the output. This relieves us from generating the reference trajectory for the filtered dynamics of the output and thus reducing the controller order by n, n being the dimension of the system state. The stability of the filtered dynamics is indirectly proved via an existing state signal. The trade‐off for this order reduction is that the worst‐case estimate for the expanded state vector has to be chosen as a suboptimal choice rather than the optimal choice. It is shown that the resulting reduced‐order adaptive controller preserves the stability and robustness properties of the full‐order adaptive controller in disturbance attenuation, boundedness of closed‐loop signals, and output tracking. The proposed order‐reduction scheme is also applied to a class of single‐input single‐output linear systems with partly measured disturbances. Two examples are presented to illustrate the performance of the reduced‐order controller in this paper. Copyright © 2007 John Wiley & Sons, Ltd.     

带有逆向力补偿的Stewart平台自适应鲁棒控制

针对Stewart平台复杂的动力学特性以及参数不确定性,提出了带有逆向力补偿自适应鲁棒控制方法.依据对平台动力学特性的分析,采用Lagrange方法建立了上平台的前向动力学模型,同时利用Newton-Euler法对六连杆部分的动态特性进行逆向力补偿,得到了具有机械系统物理特征,且形式简单的误差动态系统.针对上平台参数摄动、逆向力补偿残量以及未建模动态等各种不确定性,依据耗散性理论设计自适应鲁棒控制器.由于平台动力学方程中形式最为复杂的六连杆动态特性被有效地加以补偿,从而控制器中动力学计算部分得以简化.平台动力学方程中上平台部分物理特征明显,可以方便地构造出合适的李雅普诺夫函数.仿真结果验证了该方法的有效性和分析的正确性.     

Adaptive control of pure-feedback nonlinear systems with full-state time-varying constraints and unmodeled dynamics

This paper focuses on the problem of adaptive control for a class of pure-feedback nonlinear systems with full-state time-varying constraints and unmodeled dynamics. By introducing a one-to-one nonlinear mapping, the constrained pure-feedback nonlinear system with state and input unmodeled dynamics is transformed into unconstrained pure-feedback system. The controller design based on the transformed novel system is proposed by using a modified dynamic surface control method. Dynamic signal and normalization signal are designed to handle dynamical uncertain terms and input unmodeled dynamics, respectively. By adding nonnegative normalization signal into the whole Lyapunov function and using the introducing compact set in the stability analysis, all signals in the whole system are proved to be semiglobally uniformly ultimately bounded, and all states can obey the time-varying constraint conditions. A numerical example is provided to demonstrate the effectiveness of the proposed approach.     

Fuzzy adaptive control of a class of nonlinear systems with unmodeled dynamics

This paper investigates the tracking control problem for a class of pure‐feedback systems with unmodeled dynamics. The useful properties of the fuzzy basis functions and membership are explored to be used for stability analysis, and an alternative Lyapunov function depending on both control input and system state is utilized. Then, an adaptive fuzzy controller is designed to ensure that the tracking error is within a small adjustable neighborhood of the origin, where some conventional assumptions imposed on the unmodeled dynamics have been relaxed. Finally, simulation results are given to validate the theoretical results.     

A robust adaptive control with unmodeled dynamic for HVDC transmission systems

Utilizing the feature of quick response of HVDC to improve the performance of AC/DC system has become the emphasis to be researched. This paper introduces firstly the principle of the robust adaptive control of nonlinear systems with unmodeled dynamics, then developed the robust adaptive additional control of HVDC with unmodeled dynamics of generator in order to improve stability of power system. The additional control of HVDC with unmodeled dynamics only uses the local signals and its design is simple, furthermore it can obviously improve the stability of power system in different operational conditions. Experimental results using the presented concepts obtained on single machine infinite bus model are also included. These results prove the efficiency of the control scheme. The design process of controller provided a new idea to design controller by use of simplified model. __________ Translated from Journal of Sichuan University (Engineering Science Edition), 2005, 37(5): 154–158 (in Chinese)     

Adaptive neural dynamic surface control of MIMO stochastic nonlinear systems with unknown control directions

In this paper, an adaptive neural output‐feedback control approach is considered for a class of uncertain multi‐input and multi‐output (MIMO) stochastic nonlinear systems with unknown control directions. Neural networks (NNs) are applied to approximate unknown nonlinearities, and K‐filter observer is designed to estimate unavailable system's states. Due to utilization of Nussbaum gain function technique in the proposed approach, the singularity problem and requirement to prior knowledge about signs of high‐frequency gains are removed, simultaneously. Razumikhin functional method is employed to deal with unknown state time‐varying delays, so that the offered control approach is free of common assumptions on derivative of time‐varying delays. Also, an adaptive neural dynamic surface control is developed; hence, explosion of complexity in conventional backstepping method is eliminated, effectively. The boundedness of all the resulting closed‐loop signals is guaranteed in probability; meanwhile, convergence of the tracking errors to adjustable compact set in the sense of mean quartic value is also proved. Finally, simulation results are shown to verify and clarify efficiency of the offered approach. Copyright © 2016 John Wiley & Sons, Ltd.     

Adaptive control of non‐linear systems with unknown virtual control coefficients

This paper deals with state feedback adaptive control of parametric‐strict‐feedback (triangular) non‐linear systems with unknown virtual control coefficients. A priori knowledge of the signs of the virtual coefficients is not required, and control signals and adaptive laws are smooth. Asymptotic tracking of smooth reference signals is achieved while all the variables remain bounded. The proposed algorithms make use of backstepping and tuning functions, and enlarge the class of non‐linear systems with unknown parameters for which asymptotic output tracking can be achieved. Copyright © 2000 John Wiley & Sons, Ltd.     

Robust adaptive control of nonlinearly parameterized systems with unmodeled dynamics

Many physical systems such as biochemical processes and machines with friction are of nonlinearly parameterized systems with uncertainties. How to control such systems effectively is one of the most challenging problems. This paper presents a robust adaptive controller for a significant class of nonlinearly parameterized systems. The controller can be used in cases where there exist parameter and nonlinear uncertainties, unmodeled dynamics and unknown bounded disturbances. The design of the controller is based on the control Lyapunov function method. A dynamic signal is introduced and adaptive nonlinear damping terms are used to restrain the effects of unmodeled dynamics, nonlinear uncertainties and unknown bounded disturbances. The backstepping procedure is employed to overcome the complexity in the design. With the proposed method, the estimation of the unknown parameters of the system is not required and there is only one adaptive parameter no matter how high the order of the system is and how many unknown parameters there are. It is proved theoretically that the proposed robust adaptive control scheme guarantees the stability of nonlinearly parameterized system. Furthermore, all the states approach the equilibrium in arbitrary precision by choosing some design constants appropriately. Simulation results illustrate the effectiveness of the proposed robust adaptive controller. __________ Translated from Journal of Sichuan University (Engineering Science Edition), 2005, 37(5): 148–153 (in Chinese)     

Adaptive fault-tolerant tracking control for nonlinear systems with unknown control coefficient and input saturation

In this article, the tracking control problem is investigated for a class of nonlinear systems in the presence of unknown disturbance, input saturation, actuator fault, and unknown control coefficient. A novel disturbance observer-based adaptive fault-tolerant tracking control strategy is proposed with regard to nonlinear systems. Based on the Gaussian error function, the auxiliary dynamic system is designed to offset effects caused by the input saturation. Moreover, the Nussbaum-type function is employed to avert control singularity and deal with the unknown control coefficient. A theoretical analysis indicates that the boundedness of all signals in the closed-loop system can be guaranteed. Finally, two examples with one concerning the dynamic point-the-bit rotary steerable drilling tool system are given to confirm the validity of the method.     

Adaptive control of Wiener-type nonlinear systems using neural networks

This paper proposes new adaptive control schemes with neural networks for Weiner-type nonlinear systems which have output nonlinearity. First, by adopting a robust adaptive control law and a functional link network (FLN), we present an adaptive linearizing scheme as a primary step for a model reference control scheme, where the FLN compensates the output nonlinearity. Second, we analyze the stability of the adaptive linearizing scheme by using a robust adaptive control technique, and demonstrate that all of the parameters are bounded and that the boundedness of all of the signals in the closed loop is guaranteed under some reasonable conditions. Third, based on the linearizing scheme, we present a new direct model reference adaptive control scheme by choosing the reference output appropriately. The stability of the system is guaranteed under several conditions in a similar manner. Finally, we illustrate the effectiveness of the proposed scheme through some numerical examples. © 1998 Scripta Technica. Electr Eng Jpn, 122(1): 37–48, 1998     

Adaptive regulation of MIMO linear systems against unknown sinusoidal exogenous inputs

This paper deals with the adaptive regulation problem in linear multi‐input multi‐output systems subject to unknown sinusoidal exogenous inputs, where the frequencies, amplitudes, and phases of the sinusoids are unknown and where the number of sinusoids is assumed to be known. The design of an adaptive regulator for the system under consideration is performed within a set of Q‐parameterized stabilizing controllers. To facilitate the design of the adaptive regulator, triangular decoupling is introduced in part of the closed‐loop system dynamics. This is achieved through the proper selection of the controller state feedback gain and the structure of the Q parameter. Regulation conditions are then presented for the case where the sinusoidal exogenous input properties are known. For the case where the sinusoidal exogenous input properties are unknown, an adaptation algorithm is proposed to tune the Q parameter in the expression of the parameterized controller. The online tuning of the Q parameter allows the controller to converge to the desired regulator. Convergence results of the adaptation algorithm are presented. A simulation example involving a retinal imaging adaptive optics system is used to illustrate the performance of the proposed adaptive system. Copyright © 2008 John Wiley & Sons, Ltd.     

Adaptive homo‐backstepping tracking control for strict‐feedback systems in presence of unknown dead‐zones

An adaptive homo‐backstepping control for nonlinear strict‐feedback systems subjected to unknown actuator dead‐zone and disturbance is investigated. A sliding‐mode‐based integral filter is constructed and used to approximate the desired feedback control in the backstepping‐like recursive design technique. Subsequently, the problem of “explosion of complexity” is solved by obviating the analytic derivatives deduction for virtual control in the conventional backstepping technology. The actuator dead‐zone dynamic is modeled as the combination of a line and a disturbance‐like term, which makes the controller design simpler. The interconnected control module and filter module in the resulting closed‐loop system satisfy the input‐to‐state practically stability‐modularity condition, provided that the small‐gain theorem is exploited to ensure the stability of closed‐loop system. The proposed approach cannot only mitigate the effect of dead‐zone but also solve the problem of explosion of complexity in the previous literature. Numerical simulations performed on a manipulator with a brushed DC motor are introduced to illustrate the effectiveness of underlying control scheme.     

Adaptive LQG control of positive real systems

An adaptive control law for PR plants is proposed. The main feature of this approach is that the role of adaptation is not to stabilize the plant but to improve performance. The plant is identified using a standard least-squares algorithm and through a suitably defined parameter modification, the plant estimates are constrained to a region in the parameter space corresponding to Positive Real Observable Controllable plants. Using these estimates an adaptive LQG disipative controller is designed which trivialy stabilizes the plant. Simulation results are presented. © 1998 John Wiley & Sons, Ltd.     

Adaptive estimation and rejection of unknown sinusoidal disturbances through measurement feedback for a class of non‐minimum phase non‐linear MIMO systems

This paper develops an adaptive estimation method to estimate unknown disturbances in a class of non‐minimum phase non‐linear MIMO systems. The unknown disturbances are generated by an unknown linear exosystem. The frequencies, phases and amplitudes of the disturbances are unknown, the only available information of the disturbances is the number of distinctive frequencies. The system considered in this paper is a class of MIMO non‐linear systems in the output feedback form which can be non‐minimum phase. The proposed estimation algorithm provides exponentially convergent estimates of system states, unknown disturbances in the system and frequencies of the disturbances characterized by the eigenvalues of the exosystem. Moreover, based on the stabilization controller for the disturbance free system, the estimates of the disturbances are used to solve the disturbance rejection problem. The unknown disturbances are compensated completely with the stability of the whole closed‐loop system. Copyright © 2006 John Wiley & Sons, Ltd.     

Adaptive cooperative control for air-ground systems with actuator saturation under disturbances

In this article, an adaptive cooperative control strategy is proposed for an air-ground system with actuator saturation. The air-ground system with actuator saturation includes a ground vehicle with road bumps and a quadrotor with gust winds. The adaptive cooperative control strategy is composed of four tracking differentiators, four adaptive extended state observers and two adaptive integral SMC laws. Based on Silverman canonical transformation and pole placement, the adaptive extended state observers are designed to estimate disturbances from the road bumps and gust winds. The adaptive integral SMC laws are proposed to achieve cooperation between the ground vehicle and the quadrotor in the air-ground system with actuator saturation. Simulation results are provided to show effectiveness of the adaptive cooperative control strategy by a ground vehicle and a quadrotor.