基于Lyapunov稳定性理论的电励磁直线同步电机自适应控制

为了提高直线伺服系统的动态性能,克服电励磁直线同步电机存在的参数时变性,设计模型参考自适应速度控制器。系统内环是基于参考模型的速度跟踪控制器,外环自适应机构在线调整速度跟踪控制器的可调参数并使参考模型输出速度与控制对象输出速度之间的广义速度误差趋近于零。采用基于Lyapunov稳定性理论的模型参考自适应速度控制器设计方法,在保证广义速度跟踪误差收敛至零的同时,还保证了模型参考自适应速度控制系统具有稳定性和收敛性。采用欧拉数值积分方法,经过计算机数值仿真计算得到参考模型与控制对象的速度输出和初级交轴电流状态变量的时域响应曲线,验证了该自适应速度控制系统具有全局收敛性。     

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.     

基于最优模式智能控制自适应交流调速系统的研究

对交流调速,文章提出了一种最优模式智能自适应控制的新方案。该方案以带逻辑因子速度调节器的矢量解耦控制为基础,引入－最优模型参考系统和智能型的自适应机构。它能有效地克服电机自身参数,拖动负载参数的变化以及随机干扰的影响,进一步有效地提高了交流调速的性能,增强了系统的鲁棒性。     

基于自适应模糊滑模控制的机器人轨迹跟踪算法

针对控制参数的不确定性以及存在未知外部扰动情况下移动机器人的轨迹跟踪问题,提出一种基于光滑非线性饱和函数的自适应模糊滑模轨迹跟踪控制算法。通过建立不确定非线性移动机器人运动控制模型,利用自适应模糊逻辑系统构建自适应模糊滑模控制器。为了增强轨迹跟踪控制算法对随机不确定外部扰动适应能力的同时削弱滑模控制算法中的输入抖振现象,利用有界输入有界输出(BIBO)稳定的方法,通过带有自适应调节算法的模糊系统对滑模控制律中非线性函数项进行自适应逼近,并设计了模糊系统中可调参数的自适应控制律,保证了控制系统的稳定与收敛。实验结果表明,所设计的控制器对系统参数不确定性和外界扰动均具有较强的轨迹跟踪性能和鲁棒性。与传统的滑模控制算法相比,该算法不仅能有效减小输入抖振而且轨迹跟踪控制精度提高了18.89%。     

Adaptive state‐feedback control with sensor failure compensation for asymptotic output tracking

This paper addresses a new adaptive output tracking problem in the presence of uncertain plant dynamics and uncertain sensor failures. A new unified nominal state‐feedback control law is developed to deal with various sensor failures, which is directly constructed by state sensor outputs. Such a new state‐feedback compensation control law is able to ensure the desired plant‐model matching properties under different failure patterns. Based on the nominal compensation control design, a new adaptive compensation control scheme is proposed, which guarantees closed‐loop signal boundedness and asymptotic output tracking. The new adaptive compensation scheme not only expands the sensor failures types that the system could tolerate but also avoids some signal processing procedures that the traditional fault‐tolerant control techniques are forced to encounter. A complete stability analysis and a representative simulation study are conducted to evaluate the effectiveness of the proposed adaptive compensation control scheme.     

A new command governor architecture for transient response shaping

In this paper, we develop a control framework for stabilization and command following of nonlinear uncertain dynamical systems. The proposed methodology consists of a new command governor architecture and an adaptive controller. The command governor is a dynamical system that adjusts the trajectory of a given command to follow an ideal reference system capturing a desired closed‐loop dynamical system behavior in transient time. Specifically, we show that the controlled nonlinear uncertain dynamical system can approach the ideal reference system by choosing the design parameter of the command governor. In addition, an adaptive element is used to asymptotically assure that the error between the controlled nonlinear uncertain dynamical system and the ideal reference system is reduced in long term. Therefore, the proposed methodology not only has closed‐loop transient and steady‐state performance guarantees but can also shape the transient response by adjusting the trajectory of the given command with the command governor. We highlight that there exists a trade‐off between the adaptive controller's learning rate and the command governor's design parameter. This key feature of our framework allows rapid suppression of system uncertainties without resorting to a high learning rate in the adaptive controller. Furthermore, we discuss the robustness properties of the proposed approach with respect to high‐frequency dynamical system content such as measurement noise and ∕ or unmodeled dynamics. A numerical example is provided to demonstrate the efficacy of the proposed architecture. Copyright © 2012 John Wiley & Sons, Ltd.     

轨迹跟踪伺服系统的鲁棒复合控制

针对控制输入饱和受限且带有未知扰动的电机伺服系统,提出一种实现曲线轨迹准确跟踪的鲁棒复合控制方案。该方案引入一个参考信号生成器和一个扩展状态观测器,其中参考信号生成器可根据目标轨迹信号构造出对应的状态量,扩展状态观测器用于对系统的状态量和扰动进行估计,采用反馈与前馈相结合构成最终的控制律。利用Lyapunov理论对闭环系统的稳定性进行了严格分析。在MATLAB中进行了仿真研究,随后在一个DSP控制的永磁直线电机二维伺服平台进行了试验验证。结果表明:所提的控制方案能在轨迹跟踪任务中取得优越的瞬态性能和稳态准确性,而且对目标轨迹和扰动的幅值差异具有较好的鲁棒性。     

基于模型预测控制的主动配电网多场景变时间尺度优化调度

针对源荷预测误差对主动配电网调度影响较大的问题,充分考虑源荷数据相关性,提出基于模型预测控制(MPC)的主动配电网多场景变时间尺度优化调度策略.在日前、日内优化阶段,采用藤Copula模型描述源荷相关性,结合场景生成与削减技术形成源荷出力场景,以多场景下配电网期望运行成本最小为目标建立优化模型,求解配电网中机组、储能、...     

A convex optimization approach to adaptive stabilization of discrete‐time LTI systems with polytopic uncertainties

This paper suggests a simple convex optimization approach to state‐feedback adaptive stabilization problem for a class of discrete‐time LTI systems subject to polytopic uncertainties. The proposed method relies on estimating the uncertain parameters by solving an online optimization at each time step, such as a linear or quadratic programming, and then, on tuning the control law with that information, which can be conceptually viewed as a kind of gain‐scheduling or indirect adaptive control. Specifically, an admissible domain of stabilizing state‐feedback gain matrices is designed offline by means of linear matrix inequality problems, and based on the online estimation of the uncertain parameters, the state‐feedback gain matrix is calculated over the set of stabilizing feedback gains. The proposed stabilization algorithm guarantees the asymptotic stability of the overall closed‐loop control system. An example is given to show the effectiveness of the proposed approach. Copyright © 2014 John Wiley & Sons, Ltd.     

Reinforcement learning based closed-loop reference model adaptive flight control system design

In this study, we present a reinforcement learning (RL)-based flight control system design method to improve the transient response performance of a closed-loop reference model (CRM) adaptive control system. The methodology, known as RL-CRM, relies on the generation of a dynamic adaption strategy by implementing RL on the variable factor in the feedback path gain matrix of the reference model. An actor-critic RL agent is designed using the performance-driven reward functions and tracking error observations from the environment. In the training phase, a deep deterministic policy gradient algorithm is utilized to learn the time-varying adaptation strategy of the design parameter in the reference model feedback gain matrix. The proposed control structure provides the possibility to learn numerous adaptation strategies across a wide range of flight and vehicle conditions instead of being driven by high-fidelity simulators or flight testing and real flight operations. The performance of the proposed system was evaluated on an identified and verified mathematical model of an agile quadrotor platform. Monte-Carlo simulations and worst case analysis were also performed over a benchmark helicopter example model. In comparison to the classical model reference adaptive control and CRM-adaptive control system designs, the proposed RL-CRM adaptive flight control system design improves the transient response performance on all associated metrics and provides the capability to operate over a wide range of parametric uncertainties.     

带零动量轮航天器的逆系统方法姿态控制

为满足带零动量轮航天器姿态响应的快速性与准确性,将航天器姿态模型和飞轮机构及其补偿器视为广义控制对象,采用广义逆系统方法与内模原理相结合设计了复合控制器。在分析动量轮的摩擦阻力效应基础上,设计了基于飞轮非线性观测器的低、高速补偿器,在改变航天器惯量与加入干扰的情况下,通过仿真分析了复合控制的鲁棒性。逆系统实现了广义控制对象的解耦,内模闭环控制器弥补了解耦的非理想性,补偿器加快了姿态响应速度。仿真结果表明,逆系统与内模控制组成的复合控制器对带零动量轮航天器姿态控制是有效的,并且该控制器对航天器惯量参数和干扰具有较强的鲁棒性。     

A decoupled adaptive control algorithm for global state feedback stabilization of a class of nonlinear systems

A decoupled adaptive control algorithm, namely the combined dynamic gain and adaptive homogeneous domination approach, is introduced to solve the global state feedback stabilization problem for a class of uncertain nonlinear systems. Compared with the conventional adaptive backstepping/tuning functions approach, the algorithm differs in the way of constructing the estimator and handling the nonlinear drifts, and allows the adaptive control law that is decoupled via a dynamic gain to be designed only by choosing some appropriate constants. The proposed adaptive controller guarantees that all the states of the closed‐loop system are globally bounded and the system solutions converge to zero asymptotically. Both physical and academic examples are provided to demonstrate the validness of the theory. Copyright © 2014 John Wiley & Sons, Ltd.     

Output‐feedback model‐reference adaptive calibration for map‐based anti‐jerk control of electromechanical automotive clutches

It is well known that the map‐based control can reduce the computational burden of the automotive on‐board controller. This paper proposes an output‐feedback model‐reference adaptive control algorithm to calibrate the map‐based anti‐jerk controller for electromechanical clutch engagement. The algorithm can be used to adaptively construct a data‐driven fuzzy rule base without resorting to manual tuning, so that it can overcome the problem of conventional knowledge‐based fuzzy logic design, which involves strenuous parameter‐tuning work in the construction of calibration maps. To accurately define the consequent of each fuzzy rule for anti‐jerk control, an output feedback law for computing the reference trajectory of clutch engagement is developed to eliminate the discontinuous slip‐stick transition, whereas an adaptive controller is designed to track the reference trajectory and compensate the nonlinearity. The convergence of the proposed output‐feedback model‐reference adaptive control algorithm is analyzed. Simulation results indicate that the proposed method can successfully reduce the excessive vehicle jerk and frictional energy dissipation during clutch engagement as compared with the conventional knowledge‐based fuzzy logic controller without fine tuning.     

Adaptive reconfigurable control of systems with time‐varying delay against unknown actuator faults

This work is concerned with the problem of delay‐dependent adaptive fault‐tolerant controller design against unknown actuator faults for linear continuous systems with time‐varying delay. Based on the online estimation of possible faults by discontinuous adaptation law, identification parameters of the adaptive state feedback controller are updated autonomously to compensate the fault effects on the delayed system. For the first time, a convex combination idea and a projection‐type adaptive approach are combined organically to derive the main results. A set of new delay‐dependent reconfigurable stabilization criteria, which guarantee the stability of closed‐loop systems in both fault‐free and faulty cases, is established in terms of linear matrix inequalities. Two numerical instances for linear delayed systems and the linearized model for the lateral motion of Boeing 747 are respectively simulated to illustrate the superiority and the effectiveness of the presented adaptive delay‐dependent results. Copyright © 2013 John Wiley & Sons, Ltd.     

Adaptive control of stochastic nonlinear systems with Markovian switching

This paper is concerned with the problem of adaptive control for a class of stochastic nonlinear systems with Markovian switching, where the upper bounds of nonlinearities of stochastic Markovian jump systems are assumed to be unknown. Firstly, an adaptation law is developed to estimate these unknown parameters. Then, a class of adaptive state feedback controller is proposed such that not only the estimated errors are bounded almost surely but also, the states of the resulting closed‐loop system are asymptotically stable almost surely. Finally, a numerical example is given to show the validity of the results.Copyright © 2012 John Wiley & Sons, Ltd.     

Stability analysis and adaptive control for nonlinear discrete‐time systems with time delays and dead zone via state‐feedback technique

This research addresses the stability analysis and adaptive state‐feedback control for a class of nonlinear discrete‐time systems with multiple interval time‐varying delays and symmetry dead zone. The multiple interval time‐varying delays and symmetry dead zone are considered in the nonlinear discrete‐time system. The multiple interval time‐varying delays are bounded by the nonlinear function with unknown coefficients, and the symmetry dead zone is considered without the knowledge of the dead zone parameters. The adaptive state‐feedback controller is designed for the nonlinear discrete‐time systems with multiple interval time‐varying delays and dead zone. The discrete Lyapunov‐Krasovskii functional is introduced, such that the solutions of the closed‐loop error system converge to an adjustable bounded region and the state errors can be rendered arbitrarily small by adjusting the adaptive parameters. The designed adaptive state‐feedback controller does not require the knowledge of maximum and minimum values for the characteristic slopes of the dead zone. Finally, three simulation examples are given to show the effectiveness of the proposed methods.     

可调速双馈水轮发电机组控制系统的稳定性分析

为了研究可调速双馈水轮发电系统静态稳定性和控制器反馈系数之间的关系，该文针对变速恒频双馈发电机运行特点和水轮机导叶开度调节结构，首先推导了具有刚性水锤效应的可调速双馈水轮发电机组的数学模型。其次，结合基于动态同步轴系下的双馈发电机双通道励磁控制策略以及有功功率参与反馈控制的水轮机调节系统模型，建立了以有功、无功和转差率为系统调节变量的闭环控制系统的状态方程。最后，利用李雅普诺夫函数概念和方法得到了闭环控制系统具有大域渐近稳定性的充分条件。分析结果表明，可调速双馈水轮发电机组的静态稳定性与控制器的反馈系数密切相关。     

一类非线性系统渐近稳定控制方法及其应用

研究了某一类非线性系统渐近稳定控制问题,首先对非线性系统模型的状态变量进行非线性变换,然后采用非线性状态反馈方法将原非线性系统变换为一渐近稳定的线性系统,得到了相应的非线性控制律。把本文研究的结果应用于某飞行器的姿态控制系统中,得到渐近稳定姿态控制律。最后通过仿真证明了本控制方法的有效性。     

Parameter‐dependent Riccati equation–based output feedback adaptive control

A parameter‐dependent Riccati equation approach is proposed to design and analyze the stability properties of an output feedback adaptive control law design. The adaptive controller is intended to augment an existing fixed‐gain observer‐based output feedback control law. Although the formulation is in the setting of model reference adaptive control, the realization of the adaptive controller does not require implementing the reference model. In this regard, the increased complexity of implementing the adaptive controller, above that of a fixed‐gain control law, is less than that of other methods. The error signals are shown to be uniformly ultimately bounded, and an estimate for the ultimate bound is provided. The issue of sensor noise is addressed by introducing an error filter. The control design process and the theoretical results are illustrated using a model for wing rock dynamics.     

Fuzzy bilinear state feedback control design based on TS fuzzy bilinear model for DC–DC converters

This paper proposes a fuzzy bilinear state feedback controller based on Tagaki–Sugeno (TS) fuzzy bilinear model for DC–DC converters. The DC–DC converters can be approximated to bilinear model via Taylor series expansion. For achieving the output voltage regulation, we formulate the extended system of bilinear DC–DC converter model, one of whose state variables is the error between the output voltage and the reference output voltage. The extended system can be described to TS fuzzy bilinear model. We design a fuzzy bilinear state feedback controller to track the reference output voltage. The closed loop system is globally asymptotically stable via the proposed controller. Lyapunov theory is employed to guarantee the stability of the closed loop system. Numerical examples are given to demonstrate the validity of our proposed control approach with comparative results.