执行器饱和不确定线性系统的分析和设计

为探讨含饱和状态反馈单输入系统的稳定性问题,依据lyapunov函数方法,给出了该系统是全局渐近稳定还是区域渐近稳定的充分性条件,且对区域渐近稳定的情况计算其不变吸引椭球,运用Ricatti方程叠代法设计控制器以使所得系统不变吸引椭球尽量大,并将这种分析和设计的方法被推广到控制饱和不确定系统。通过仿真算例说明了该方法的有效性。     

基于域方法的饱和励磁控制器设计

同步发电机励磁系统受到物理结构的限制,励磁电压的输出含有饱和环节,其具有非线性特征。基于域方法,提出饱和励磁控制器的设计思路。首先,利用扩张状态观测器,对模型内部不确定性和外部扰动进行动态补偿,实现非线性系统的反馈线性化;然后,引入饱和指示器,将含饱和环节的线性化模型转化为多面体模型,并将饱和系统的二次稳定性条件用不等式表示出来;引入初始状态域,将初始状态域含于椭球吸引域内的几何关系表示为不等式;最后,为使系统在给定初始状态域、吸引域和线性矩阵不等式区域内满足二次稳定条件,将控制器设计问题转化为双线性矩阵不等式的求解问题。算例研究表明:设计的控制器是有效的,具有优良的控制性能。     

一种饱和电力系统稳定器控制效果的判断方法

针对饱和线性系统，借助二次型Lyapunov函数，估计稳定域的一个严格子集。同时为了减少保守性，利用矩阵的Schur补性质，将如何选取该二次型Lyapunov函数的问题转化为标准的LMI凸优化问题。将该方法应用于电力系统，提出一种判断饱和PSS控制是否有效的方法。其主要思路是估计出饱和系统的稳定域，通过分析预想扰动的状态量是否位于估计的稳定域内，从而为判断此饱和PSS控制是否能有效镇定预想的扰动提供判据，利用算例验证了方法的有效性。     

运用静态反馈分析设计离散饱和线性系统

给出了控制饱和单输入状态反馈系统是全局渐近稳定还是区域渐近稳定的充分性条件,且对区域渐近稳定的情况计算其不变吸引椭球,运用Ricatti方程迭代法设计控制器以使所得椭球尽量大;对多输入控制饱和系统输出反馈系统,给出其吸引域的描述;最后用算例说明方法的有效性。     

考虑输入饱和的PMSM命令滤波离散控制

结合命令滤波和神经网络技术设计了永磁同步电机离散位置跟踪控制方法。使用欧拉公式建立永磁同步电机离散模型,用神经网络技术来逼近非线性函数,应用命令滤波技术解决传统反步法中的"计算复杂性"问题,引入补偿机制成功地解决滤波误差问题。仿真结果表明,该控制器有良好的位置跟踪性能,能够解决输入饱和的影响,且对外部负载扰动有较强的鲁棒性。     

考虑饱和影响的电力系统广域阻尼控制器设计

广域阻尼控制器是抑制电力系统区间低频振荡的一种有效方法,但实际系统中执行器一般存在饱和现象。为此提出一种抗饱和补偿器设计方法。首先通过引入饱和函数建立考虑执行器饱和的电力系统模型;其次采用现代抗饱和技术,分2步进行抗饱和补偿器设计。第一步不考虑饱和环节,采用鲁棒控制理论对闭环电力系统进行H∞ 控制器设计,以确保系统的稳定性;第二步针对饱和环节,基于Lyapunov稳定理论提出抗饱和补偿器设计方法,设计抗饱和补偿器进行补偿,以抑制执行器饱和对控制器性能的影响。最后,将该抗饱和控制器应用到2区4机电力系统模型。仿真结果表明,相比不考虑执行器饱和的线性控制器,根据所提方法设计的抗饱和补偿器可以有效抑制饱和环节对线性控制器性能的影响,能够显著提高电力系统对饱和的抑制能力。     

基于饱和系统理论的电力系统稳定器性能分析方法

利用二次型Lyapunov函数估计了含扰动饱和线性系统的实用稳定域和可承受的最大扰动量，同时通过求解多目标优化问题来减少估计的保守性。在此基础上结合矩阵的Schur补性质化简此优化问题，证明其Pareto最优解的一些性质并提出求解方法。进一步将该方法应用于电力系统，分析含扰动饱和PSS系统的控制性能。3机5节点的算例验证了该方法简单、容易实现且严格可靠。     

励磁控制器的抗饱和鲁棒控制研究

发电机励磁控制中,控制器的超调会使调节系统进入强非线性区,影响控制性能.鲁棒控制分析表明,在强干扰下,控制器性能指标越好,越容易进入超调状态.为此,采用增益矩阵反馈抗饱和理论,设计包含抗饱和补偿环节励磁控制器.通过线性矩阵不等式求解,得到满足限幅约束的可行解,从而得到补偿环节的增益矩阵.当控制器过调时,补偿环节会加以修正.仿真分析表明,在大扰动下,附加抗饱和环节的励磁控制器能有效地抑制控制器超调,提高发电机稳定控制能力.     

无刷直流电机调速系统的IMC-PID抗饱和控制

针对无刷直流电流电机双闭环调速系统转速调节器存在的非线性饱和特性,提出一种IMC-PID抗饱和控制方案。首先根据内模控制原理设计出转速环和电流环的IMC-PID控制器,然后引入可行性参考输入的概念,设计出基于条件技术的转速环IMC-PID抗饱和控制器,最后为验证IMC-PID抗饱和控制器的可行性,将该控制器应用于无刷直流电机双闭环调速系统中。仿真结果表明:该控制器不仅具有的良好的抗干扰性和鲁棒性,而且改善了系统的动态性能,有效抑制了饱和现象的影响。     

新型抗饱和PI控制器及其在异步电动机调速系统中的应用

提出了一种新型抗饱和PI控制器的设计方法,通过引入反向计算系数减小了积分饱和对系统的影响,给出了新型抗饱和PI控制器的系统框图,并且证明了该算法的稳定性、为了提高异步电动机调速系统的动态性能,把这种抗积分饱和的Pl控制器应用到了PWM逆变器供电的异步电动机矢量控制系统中.利用Matlab软件进行了仿真实验,仿真实验结果验证了这种算法的有效性,表明这种新型PI控制器可以有效地降低调速系统的超调量,缩短系统的稳定时间,同时还可以提高系统对负载扰动的鲁棒性.     

Neural adaptive robust output feedback control of wheeled mobile robots with saturating actuators

This paper addresses the output feedback tracking control problem of electrically driven wheeled mobile robots subjected to actuator constraints. The main drawback of previously proposed controllers is the actuator saturation problem, which degrades the transient performance of the closed‐loop control system. In order to alleviate this problem, a saturated tracking controller has been proposed using the hyperbolic tangent function. A new nonlinear observer is introduced in order to leave out the velocity sensors in the robot system to decrease the cost and weight of the system for practical applications. A dynamic surface control strategy is effectively used to reduce the design complexity when considering actuator dynamics. In addition, neural network approximation capabilities and adaptive robust techniques are also adopted to improve the tracking performance in the presence of uncertain nonlinearities and unknown parameters. Semi‐global stability of the closed‐loop system is presented using direct Lyapunov method. Simulation results are provided to illustrate the effectiveness of the proposed control system for a differential drive mobile robot in practice. Copyright © 2014 John Wiley & Sons, Ltd.     

A solution to leader following of underactuated surface vessels with actuator magnitude and rate limits

This article investigates the leader following of underactuated surface vessels that operate in the presence of environmental disturbances, model uncertainties, and actuator saturation. What makes the design of controller challenging is that the sway cannot be directly controlled. Furthermore, the actuator is formulated with magnitude saturation and rate saturation. To express the effect of magnitude saturation and rate saturation, a continuous function is employed to model actuator. An auxiliary dynamic system is used to solve the problem of magnitude saturation and rate saturation. A disturbance observer is designed to provide estimation of the environmental disturbances and model uncertainties, and achieve finite time convergence of estimation error. A control law is designed based on disturbance observer, backstepping technique, and auxiliary dynamic system. The uniform ultimate boundedness of all signals in the closed-loop control system is proved. Simulations demonstrate the performance of the proposed controller.     

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.     

Design of disturbance observer based on adaptive-neural control for large-scale time-delay systems in the presence of actuator fault and unknown dead zone

This article presents an adaptive neural compensation scheme for a class of large-scale time delay nonlinear systems in the presence of unknown dead zone, external disturbances, and actuator faults. In this article, the quadratic Lyapunov–Krasovskii functionals are introduced to tackle the system delays. The unknown functions of the system are estimated by using radial basis function neural networks. Furthermore, a disturbance observer is developed to approximate the external disturbances. The proposed adaptive neural compensation control method is constructed by utilizing a backstepping technique. The boundedness of all the closed-loop signals is guaranteed via Lyapunov analysis and the tracking errors are proved to converge to a small neighborhood of the origin. Simulation results are provided to illustrate the effectiveness of the proposed control approach.     

Neural networks-based adaptive finite-time prescribed performance fault-tolerant control of switched nonlinear systems

In this article, the adaptive finite-time fault-tolerant control problem is considered for a class of switched nonlinear systems in nonstrict-feedback form with actuator fault. The problem of finite-time fault-tolerant control is solved by introducing a finite-time performance function. Meanwhile, the completely unknown nonlinear functions exist in the switched system are identified by the neural networks. Based on the common Lyapunov function method with adaptive backstepping technique, the finite-time fault-tolerant controller is designed. The proposed control strategy can guarantee that the tracking error converges to a prescribed zone at a finite-time and all system variables remain semiglobally practical finite-time stable. Numerical examples are offered to verify the feasibility of the theoretical result.     

Observer-based single-network incremental adaptive dynamic programming for fault-tolerant control of nonlinear systems with actuator faults

A model-free incremental adaptive fault-tolerant control (FTC) scheme is proposed for a class of nonlinear systems with actuator faults. To deal with actuator faults and guarantee the approximate optimal performance of the nominal nonlinear system without any prior knowledge of system dynamics, a single-network incremental adaptive dynamic programming (SIADP) algorithm based on incremental neural network observer is developed to design an active fault-tolerant control (AFTC) policy. An approximate linear time-varying system is obtained by incremental nonlinear technique, in which the relevant matrix parameters are identified by recursive least square estimation. Then, a SIADP algorithm-based fault-tolerant controller is developed. Based on the redundancy characteristic and function of actuators, a grouping scheme of actuators is introduced. An incremental neural network observer is designed to approximate the actuator faults. The novel SIADP scheme is constructed with a simplified single critic neural network to shorten the learning time and decrease the computational burden in the control process, in which the norm of the weight estimations of critic neural network is updated. Moreover, based on the Lyapunov theorem, the uniformly ultimately bounded stability of the closed-loop incremental system is proved. Finally, simulations are given to verify the effectiveness of the proposed FTC scheme.     

Adaptive neural control for nonstrict‐feedback stochastic nonlinear time‐delay systems with input and output constraints

This paper investigates an adaptive neural tracking control for a class of nonstrict‐feedback stochastic nonlinear time‐delay systems with input saturation and output constraint. First, the Gaussian error function is used to represent a continuous differentiable asymmetric saturation model. Second, the appropriate Lyapunov‐Krasovskii functional and the property of hyperbolic tangent functions are used to compensate the time‐delay effects, the neural network is used to approximate the unknown nonlinearities, and a barrier Lyapunov function is designed to ensure that the output parameters are restricted. At last, based on Lyapunov stability theory, a robust adaptive neural control method is proposed, and the designed controller decreases the number of learning parameters and thus reduces the computational burden. It is shown that the designed neural controller can ensure that all the signals in the closed‐loop system are 4‐Moment (or 2 Moment) semi‐globally uniformly ultimately bounded and the tracking error converges to a small neighborhood of the origin. Two examples are given to further verify the effectiveness of the proposed approach.     

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.     

Attitude Control of Quadrotor in Consideration of the Effects of a Pole Based on Limited Pole Placement

This paper introduces an attitude control technique for a quadrotor aircraft. Considering that the nonlinear characteristics of the aircraft makes it difficult to stabilize, a quadrotor controlled with an adaptive algorithm. Accordingly, we proposed a quadrotor application with backstepping based on the Lyapunov function. Furthermore, we designed a separate actuator control to be mounted on the aircraft for the control of the quadrotor. This approach is often used in industrial equipment. In particular, the limited pole placement (LPP) method is applied to design the controller considering the characteristics of the actuator. The representative simulation results are presented and discussed.