输入通道有干扰多变量MRAC系统的全局稳定化控制

对具有未建模动态并且输入通道存在干扰的动态不确定多输入多输出(MIMO)模型参考自适应控制(MRAC)系统,应用输出反馈给出了一种变结构模型跟踪控制器设计．系统的已建模部分有大于1的任意相对阶且已建模部分阶的上界是未知的．通过引入辅助信号和带有记忆功能的正规化信号,以及适当选择控制器参数,保证了闭环系统的全局稳定性,且跟踪误差可调整到任意小．     

输入通道有干扰多变量MRAC系统全局稳定化控制

对具有未建模动态且输入通道存在干扰的动态不确定多输入多输出(MIMO)模型参考自适应控制(MRAC) 系统，仅应用系统的输入输出量测数据给出了一种变结构模型跟踪控制器设计机制.通过辅 助信号和带有记忆功能的正规化信号,并适当选择控制器参数, 所提出的变结构控制 (VSC)能保证闭环系统的全局稳定性,且跟踪误差可调整到任意小.     

南极大型望远镜跟踪系统的鲁棒自适应控制研究

提出一种鲁棒自适应控制器的设计方案,尤其适用于南极大型望远镜这样含有未知有界扰动,未建模动态的非线性系统.文章基于Lyapunov函数法,通过引入动态信号抑制未建模参量的影响,并采用自适应阻尼抑制各种不确定性.理论证明,所提出的鲁棒自适应控制器可保证跟踪系统的稳定性,通过选择合适的参数能达到控制精度的要求.仿真结果表明,本鲁棒自适应控制器的控制效果有很大改善.     

具有类反斜线回滞执行器的非线性系统的自适应控制

针对类反斜线回滞非线性系统,设计了自适应控制器,保持系统稳定并实现对参考信号的任意精度跟踪.在控制器设计中,通过构造足够光滑的非线性函数作为符号函数的逼近,从而解决了传统自适应控制器设计中由于符号函数的引入而导致控制器不连续的问题,避免了因此而产生的抖震现象.在系统模型中充分考虑未建模动态,使模型更具一般性.最后应用MATLAB软件进行仿真实验,结果进一步验证了该控制器的有效性.     

多变量系统的基于矩阵$K_{P}=LDU$分解的鲁棒模型参考自适应控制

本文针对具有未建模动态的多变量系统,研究了基于高频增益矩阵KP=LDU分解的鲁棒直接型模型参考自适应控制问题,严格地分析了闭环系统的稳定性和鲁棒性．     

功率放大器非线性特性及预失真建模研究

针对无记忆功率放大器的非线性特性及预失真建模的问题,首先建立了多项式模型、极坐标Saleh模型和基于正交三角函数的模型并利用MATLAB对其进行了求解,然后给出了无记忆多项式预失真处理器特性函数表达式及最小二乘解.针对记忆功率放大器的非线性特性及预失真建模的问题,首先建立了记忆多项式模型并对其进行了求解,然后建立了相应的有记忆多项式预失真模型并利用最小二乘法进行了求解,并提出了联合功率放大器特性和输入信号幅值范围的有记忆功放自适应预失真模型.最后求出所给输入信号、输出信号以及加入预失真后线性系统的输出信号的功率谱密度,并计算和比较了信道的带外失真参数ACPR;结果显示,加入预失真后大大提升了系统的性能,线性特性明显加强.     

舵角输入饱和下的导弹自动驾驶仪系统自适应控制研究

针对一类导弹自动驾驶仪系统,从实际出发考虑系统舵角输入具有饱和非线性特性,在系统建模中利用多项式模型充分描述系统的气动参数不确定性,在此基础上深入研究了系统自适应控制器的设计方法.在控制器设计中,通过引入二阶辅助信号系统,对饱和非线性输入进行精确补偿.针对系统中未知气动参数,通过设计参数的自适应估计率实现对未知参数的精确估计,进而补偿了未知气动参数不确定性影响.自适应控制器在保障系统稳定的同时也实现了系统输出对指令信号的良好跟踪效果.     

基于Bouc-Wen模型的迟滞非线性系统自适应控制

智能执行器中的迟滞特性严重影响控制系统的精度和稳定性.文章采用Bouc-Wen模型来描述迟滞特性,并融合该模型设计自适应控制器.对于Bouc-Wen模型的解特性未知,影响控制器的设计.首先通过傅立叶变换得到该模型的近似解,然后提出了预设自适应控制方法来保证系统跟踪误差的暂态和稳态性能.仿真结果验证了该方法的有效性.     

自适应蚁群算法及其在多约束多目标柔性Job-Shop调度中的应用

实际生产系统的车间作业调度一般是多约束多目标柔性Job-Shop调度,比经典的Job-Shop调度更复杂,存在多约束、多目标、动态柔性、建模复杂等特性.建立了多约束多目标柔性Job-Shop调度模型,提出了一种自适应蚁群算法,采用自适应机制和遗传原理防止算法过早停滞和加快收敛速度.西安航空发动机(集团)有限公司制造单元调度实例表明,提出的自适应蚁群算法是求解多约束多目标柔性Job-Shop调度的有效方法.     

基于模糊与神经网络技术的复杂非线性跟踪控制

针对一类具有不确定性、多重时延和状态未知的复杂非线性系统,把模糊T-S模型和RBF神经网络结合起来,提出了一种基于观测器的跟踪控制方案.首先,应用模糊T-S模型对非线性系统建模,设计观测器用来观测系统状态,并由线性矩阵不等式得到模糊模型的控制律;其次,构建了自适应RBF神经网络,应用自适应RBF神经网络作为补偿器来补偿建模误差和不确定非线性部分.证明了闭环系统满足期望的跟踪性能.示例仿真结果表明了该方案的有效性.     

A Hybrid Robust Adaptive Sliding Mode Controller for partially modelled systems: Discrete-time Lyapunov stability analysis and application

This work presents a new discrete-time Hybrid Robust Adaptive Sliding Mode Controller, developed from the union of a Robust Model Reference Adaptive Controller, an Adaptive Sliding Mode Controller, and an Adaptive One Sample Ahead Preview Controller in an unique control structure. Robust Model Reference Adaptive Controller is an adequate direct adaptive control strategy to control partially known plants, but can present slow closed-loop response to ensure global stability. Therefore, an adaptive One Sample Ahead Preview controller is incorporated to accelerate transient regimes, once it tries to track reference signal in one sample. Furthermore, an adaptive Sliding Mode Controller is also merged in the controller structure to help controller performance in transient regime and it also improves relevantly the steady state response in a scenario of several unmodelled dynamics Stability analysis of this controller using Lyapunov criterion and its robustness proof are provided, considering the plant subjected to unmodelled dynamics, which provides controller design constraints. These proofs show the controller is globally stable, and the tracking error tends to a residual set in steady state, even in the presence of matched and unmatched dynamics. Numerical simulations of the Hybrid Robust Adaptive Sliding Mode Controller applied on an unstable nonminimum-phase plant are presented, where only part of the overall plant is take into consideration for controller design. Results corroborate the feasibility and robustness of the developed control strategy and the performance superiority when compared to an adaptive One Sample Ahead Preview controller, with a 75% tracking error reduction in a scenario of several unmodelled dynamics.     

Model-Reference Adaptive Control--Stability, Parameter Convergence, and Robustness

We study stability, parameter convergence, and robustness aspectsof single input-single output model-reference adaptive systems.We begin by establishing a framework for studying parametrizableand unparametrizable uncertainty in the plant to be controlled.Using the standard assumptions on the parametrizable part ofthe plant dynamics we rederive a modified proof (of Narendra,Lin, and Valavani) of the stability of the nominal adaptivescheme. Next, we give conditions on the exogenous input to theadaptive loop—the reference signal—to guaranteeexponential parameter and error convergence. Using our frameworkfor studying unmodelled (unparametrized) dynamics; we show howthe model should be chosen, and the update law modified (bya deadzone in the update law) to preserve stability of the adaptiveloop in the presence of output disturbances and unmodelled dynamics.Finally, we compare adaptive and non-adaptive control and listdirections of continuing research.     

Adaptive type-2 fuzzy backstepping control of uncertain fractional-order nonlinear systems with unknown dead-zone

A new problem of adaptive type-2 fuzzy fractional control with pseudo-state observer for commensurate fractional order dynamic systems with dead-zone input nonlinearity is considered in presence of unmatched disturbances and model uncertainties; the control scheme is constructed by using the backstepping and adaptive technique. To avoid the complexity of backstepping design process, the dynamic surface control is used. Also, Interval type-2 Fuzzy logic systems (IT2FLS) are used to approximate the unknown nonlinear functions. By using the fractional adaptive backstepping, fractional control laws are constructed; this method is applied to a class of uncertain fractional-order nonlinear systems. In order to better control performance in reducing tracking error, the PSO algorithm is utilized for tuning the controller parameters. Stability of the system is proven by the Mittag–Leffler method. It is shown that the proposed controller guarantees the boundedness property for the system and also the tracking error can converge to a small neighborhood of the origin. The efficiency of the proposed method is illustrated with simulation examples.     

Based on interval type-2 fuzzy-neural network direct adaptive sliding mode control for SISO nonlinear systems

In this paper, a novel direct adaptive interval type-2 fuzzy-neural tracking control equipped with sliding mode and Lyapunov synthesis approach is proposed to handle the training data corrupted by noise or rule uncertainties for nonlinear SISO nonlinear systems involving external disturbances. By employing adaptive fuzzy-neural control theory, the update laws will be derived for approximating the uncertain nonlinear dynamical system. In the meantime, the sliding mode control method and the Lyapunov stability criterion are incorporated into the adaptive fuzzy-neural control scheme such that the derived controller is robust with respect to unmodeled dynamics, external disturbance and approximation errors. In comparison with conventional methods, the advocated approach not only guarantees closed-loop stability but also the output tracking error of the overall system will converge to zero asymptotically without prior knowledge on the upper bound of the lumped uncertainty. Furthermore, chattering effect of the control input will be substantially reduced by the proposed technique. To illustrate the performance of the proposed method, finally simulation example will be given.     

Adaptive tuning of the fuzzy controller for robots

An adaptive tuning algorithm of the fuzzy controller is developed for a class of serial-link robot arms. The algorithm can on-line tune parameters of premise and consequence parts of fuzzy rules of the fuzzy basis function (FBF) controller. The main part of the fuzzy controller is a fuzzy basis function network to approximate unknown rigid serial-link robot dynamics. Under some mild assumptions, a stability analysis guarantees that both tracking errors and parameter estimate errors are bounded. Moreover, a robust technique is adopted to deal with uncertainties including approximation errors and external disturbances. Simulations of the proposed controller on the PUMA-560 robot arm demonstrate the effectiveness.     

Robust control of multiple robots handling a constrained load

Multiple-robot systems are usually confronted with uncertainties,such as uncertainties in the manipulators and load parameters,and unmodelled dynamics. In this paper, the problem of controllingmultiple manipulators handling a constrained load is addressed.A reduced-order dynamic model of the system is first derived,and several properties of this model are established. Usingthe reduced-order model, a robust control law is proposed. Thiscontroller guarantees the uniform ultimate boundedness of theposition error, the internal-force error, and the constraint-forceerror. The proposed control law requires only the bounds onthe uncertainties of the parameters. Simulation results of twoplanar robots moving a load along a horizontal plane are givento illustrate the theoretical developments.     

A robust discrete adaptive control algorithm involving linear and/or adaptive inputs

Summary In this paper, a discrete-time adaptive control algorithm is considered. The algorithm has a forgetting factor and is applicable to industrial plants because it has stable inverses. The adaptive scheme presents stability and robusteness properties in the presence of unmodelled dynamic and input saturations.     

Tracking control of high-performance robots via stabilizing controllers for uncertain systems

The development of flexible manufacturing systems calls for industrial robots characterized by robustness of performance with regard to the variations of the loads and real time specification of the trajectory in the work space. In this paper, the design of a feedback controller guaranteeing such performance is considered. At first, the manipulator dynamics are embedded into a larger class of uncertain dynamical systems and a class of feedback controls is proposed that guarantees uniform ultimate boundedness of the tracking error. Successively, the methodology is specialized for the case of robotic manipulators to track trajectories described in task-oriented coordinates; the proposed control algorithm operates without requiring any explicit coordinate transformation.     

Speed and current regulation of a permanent magnet synchronous motor via nonlinear and adaptive backstepping control

This paper proposes a new speed and current control scheme for a Permanent Magnet Synchronous Motor (PMSM) by means of a nonlinear and adaptive backstepping design. All the parameters in both PMSM and load dynamics are considered unknown. It is assumed that all state variables are measurable and available for feedback in the controller design. The final control and parameter estimation laws are derived by the design of the virtual control inputs and the Lyapunov function candidate. The overall control system is asymptotically stable according to stability analysis results based on Lyapunov stability theory. Simulation results clearly show that the controller guarantees tracking of a time varying reference speed owing to the fact that the speed and current tracking errors asymptotically converge to zero despite all the parameter uncertainties/perturbations and load torque disturbance variation. Numerical simulations reveal the performance and feasibility of the proposed controller.