柔性机器人基于自适应模糊动态面的实用稳定控制

针对单连杆柔性机器人系统,提出了一种基于自适应模糊动态面的实用稳定控制方法。对于未知或时变函数,采用自适应模糊逼近,进而予以补偿。通过动态面控制避免了Backstepping设计带来的计算膨胀问题,简化了控制器的结构。所设计的控制器保证了闭环系统的实用稳定性。仿真结果验证了所提出控制器的有效性。     

电力机车永磁同步电机自适应模糊滑模控制

为研究牵引工况下电力机车永磁同步电机(PMSM)的转速控制精度,考虑轮轨接触不平顺及车体静载荷在轮对径向产生的未知时变负载转矩,建立了机车PMSM在d-q坐标系下的数学模型;针对该耦合非线性系统中存在的负载转矩,设计非线性转矩观测器对其实际值进行估计,对观测误差采用自适应模糊逻辑系统进行逼近;依据Lyapunov稳定性理论,构造了基于转矩观测器的自适应模糊滑模控制器。理论分析及仿真结果表明,机车PMSM闭环转速控制系统跟踪误差一致有界,转矩观测误差收敛于0。     

大时滞过程自适应智能模糊-Smith控制研究

为进一步提高模糊-Smith控制的稳态精度及自适应能力,提出自适应智能模糊-Smith控制策略,其基本控制级采用模糊-Smith控制,协调级采用自适应机制实时调整控制器量化因子和比例因子,智能积分器根据偏差及偏差变化率实时调整积分作用。仿真结果表明,该控制策略具有更好的动静态特性及更强的鲁棒性。     

起重臂动态面自适应模糊滑模控制联合仿真研究

转载车上的起重臂俯仰机构是常见的机电液系统。为了克服转载时系统存在非线性特性和干扰等问题,提出了一种动态面自适应模糊滑模控制的方法。基于反演滑模控制设计了李亚普诺夫函数和虚拟控制量,分别引入动态面控制计算虚拟控制量的导数、自适应控制对参数进行在线估计和模糊控制对切换项进行模糊化。为了更准确地模拟转载作业过程和验证控制器的性能,提出了基于AMESim和Simulink联合仿真的方法,利用AMESim的元件库建立系统的机械-液压模型,并通过软件接口与Simulink模型相连构建联合仿真模型。经仿真验证,所设计的滑模控制器具有较高的控制精度和较强的鲁棒性,并消除了抖振。     

高阶非线性液压辊缝系统的Backstepping动态面控制

针对某四辊冷轧机的液压辊缝系统,考虑系统干扰的情况下,建立了高阶非线性系统数学模型,利用Backstepping动态面算法设计了辊缝闭环控制器。该方法由于在逆递推设计过程中结合使用了一阶低通滤波器,无需对模型的非线性部分重复微分,从而避免了因"微分项的膨胀"而引起的算法复杂性。理论分析和仿真结果均表明所设计的控制器能够保证系统的半局稳定性,输出渐近跟踪期望轨迹,使系统对于给定位置跟踪具有良好的动态性能,对外部干扰也有较强的鲁棒性。     

基于自适应内模控制的时滞系统的仿真研究

工业过程中很多的控制对象都存在着大迟延现象,而常规的PID控制器难以解决,IMC是一种实用性很强的控制方法,对大迟延系统有较好的控制效果,现采用自适应与内模相结合的方案,避免模型失配仿真表明控制效果良好。     

利用模糊自适应PID控制电机调速系统研究

一般对于自动控制系统的设计与整定都是要求基于精准数学建模的基础上的,但是随着系统的结构越来越复杂,而且往往系统参数随时间变化而变化,所使用控制器的参数也应随之变化。引入了模糊自适应PID控制算法,实时整定PID参数,为不能准确建模以及时变系统的控制提供了更加合适的控制算法。     

电动独轮车的模糊自适应控制

针对电动独轮车稳态性能差和抗负载转矩扰动能力差的问题,将自适应模糊PD控制技术应用到电动独轮车的姿态控制中。该系统是以加速度计、陀螺仪为姿态传感器,永磁无刷直流电机为执行机构的单轮平衡车,在分析骑行过程中电动独轮车姿态变化的基础上,推导了系统对应的动态模型,建立了电动独轮车的姿态信息与电机输出转矩之间的关系,并结合传统PD控制和模糊控制的相关理论,提出采用自适应模糊PD控制的方法实现电动独轮车的姿态控制。在实验室搭建的电动独轮车上,分别采用传统PD控制和自适应模糊PD控制方法进行了试验比较。研究结果表明,相比于传统的PD控制器,自适应模糊PD控制在不同的运行环境都可以获得良好的控制效果,可以提高系统的动静态性能。     

时滞约束系统的神经动态优化模型预测控制

将时滞约束系统的模型预测控制优化问题描述为一个带约束的二次规划问题进行处理,并采用一个对偶神经网络进行在线求解。该神经动态优化方法充分发挥了神经网络并行、分布式处理的优点,优化速度快,能够用来求解各种复杂的带约束优化问题。实验研究表明,该方法具有较高的优化精度和优化速度,提高了模型预测控制的在线优化能力,能够扩展模型预测控制的应用领域。     

大纯滞后系统一种新预估模糊控制

提出了一种大纯滞后系统新预估控制方法,对大纯滞后系统进行了有效的模糊控制.该控制方法与纯滞后时间τ无关,方法简单,易于工程实现,使常规模糊控制在大纯滞后系统的控制中如同对无纯滞后系统的控制一样有效.文中证明了该方法使系统的稳定性与系统的纯滞后无关,并经MATLAB仿真验证,该法具有良好的控制品质,且能适应对象参数和结构有一定变化的时滞系统.     

模糊离散事件系统的自适应监督控制

针对基于模糊自动机的模糊离散事件系统模型在实际工程应用中的不足,从产生式规则出发,建立了该类系统的模糊规则化模型.通过定义结构化的模糊因子并以其构成模糊规则,实现了对模糊状态转移函数的描述.提出了一种包含模糊监控器和模糊控制器的系统闭环监控结构,并针对模糊推理环节给出了具体的实现方法.通过定义一种可动态调整的模糊规则允许发生度,实现了系统的自适应监控机制.借助电动汽车电池均衡管理系统对方法进行了验证.结果表明,系统的运行过程符合预定的目标.     

Adaptive fuzzy predictive sliding control of uncertain nonlinear systems with bound-known input delay

In this paper, a new Adaptive Fuzzy Predictive Sliding Mode Control (AFP-SMC) is presented for nonlinear systems with uncertain dynamics and unknown input delay. The control unit consists of a fuzzy inference system to approximate the ideal linearization control, together with a switching strategy to compensate for the estimation errors. Also, an adaptive fuzzy predictor is used to estimate the future values of the system states to compensate for the time delay. The adaptation laws are used to tune the controller and predictor parameters, which guarantee the stability based on a Lyapunov-Krasovskii functional. To evaluate the method effectiveness, the simulation and experiment on an overhead crane system are presented. According to the obtained results, AFP-SMC can effectively control the uncertain nonlinear systems, subject to input delays of known bound.     

初态学习下时滞非线性系统的迭代学习控制

针对在有限时间区间上重复运行的一类状态时滞非线性系统,研究了一种带有初态学习的迭代学习控制算法。基于算子谱理论证明了系统在任意初态条件下经过迭代学习后,其输出能够完全跟踪期望轨迹,得到了该算法谱半径形式的收敛条件,比范数形式的收敛条件放宽了。并通过对系统初态的迭代学习,放松了迭代学习控制算法对初始状态函数的要求。仿真结果验证了所提算法的有效性。最后,将该算法应用到含有状态时滞的非线性间歇过程,结果也表明了该算法的有效性和可靠性。     

基于初态矩阵控制的时延系统预测补偿算法研究

针对工业过程控制中难以避免的时延问题,分析了其产生的机理.对于系统等效时延,采用预测思想,阐述了以动态矩阵控制的预测状态空间形式,并在此基础上,提出了时延补偿算法.引入补偿因子和过渡因子在线整定预测控制量和时延控制量,使控制作用快速平稳过渡,对于突变频繁的时延系统亦能迅速跟踪输入.仿真结果表明,该改进算法的时延补偿性能良好,保证了控制的稳定性和快速性.     

Adaptive sensor fault-tolerant control for a class of multi-input multi-output nonlinear systems: Adaptive first-order filter-based dynamic surface control approach

This paper is concerned with the adaptive fault-tolerant control (FTC) problem for a class of multivariable nonlinear systems with external disturbances, modeling errors and time-varying sensor faults. The bias, drift, loss of accuracy and loss of effectiveness faults can be effectively accommodated by this scheme. The dynamic surface control (DSC) technique and adaptive first-order filters are brought together to design an adaptive FTC scheme which can reduce significantly the computational burden and improve further the control performance. The adaptation laws are constructed using novel low-pass filter based modification terms which enable under high learning or modification gains to achieve robust, fast and high-accuracy estimation without incurring undesired high-frequency oscillations. It is proved that all signals in the closed-loop system are uniformly ultimately bounded and the tracking-errors can be made arbitrary close to zero. Simulation results are provided to verify the effectiveness and superiority of the proposed FTC method.     

Finite-time decentralized adaptive neural constrained control for interconnected nonlinear time-delay systems with dynamics couplings among subsystems

The problem of finite-time decentralized neural adaptive constrained control is studied for large-scale nonlinear time-delay systems in the non-affine form. The main features of the considered system are that 1) unknown unmatched time-delay interactions are considered, 2) the couplings among the nested subsystems are involved in uncertain nonlinear systems, 3) based on finite-time stability approach, asymmetric saturation actuators and output constraints are studied in large-scale systems. First, the smooth asymmetric saturation nonlinearity and barrier Lyapunov functions are used to achieve the input and output constraints. Second, the appropriately designed Lyapunov-Krasovskii functional and the property of hyperbolic tangent functions are used to deal with the unknown unmatched time-delay interactions, and the neural networks are employed to approximate the unknown nonlinearities. Note that, due to unknown time-delay interactions and the couplings among subsystems, the controller design is more meaningful and challenging. At last, based on finite-time stability theory and Lyapunov stability theory, a decentralized adaptive controller is proposed, which decreases the number of learning parameters. It is shown that the designed controller can ensure that all closed-loop signals are bounded and the tracking error converges to a small neighborhood of the origin. The simulation studies are presented to show the effectiveness of the proposed method.     

非线性不确定系统的自适应反馈控制方法

针对具有模型不确定且参数未知的单输入单输出的非线性系统的特点,文中提出了一种自适应反馈控制方法。该方法中,假设系统被调整量最高阶导数的理想值为已知。由于方法仅需要对被控对象的调整量的输出进行反馈,而不要求状态量的具体值,所以降低了算法实现的难度。通过数值仿真验证了方法的可行性和有效性。     

Adaptive fuzzy prescribed performance control for MIMO nonlinear systems with unknown control direction and unknown dead-zone inputs

In this study, an adaptive fuzzy prescribed performance control approach is developed for a class of uncertain multi-input and multi-output (MIMO) nonlinear systems with unknown control direction and unknown dead-zone inputs. The properties of symmetric matrix are exploited to design adaptive fuzzy prescribed performance controller, and a Nussbaum-type function is incorporated in the controller to estimate the unknown control direction. This method has two prominent advantages: it does not require the priori knowledge of control direction and only three parameters need to be updated on-line for this MIMO systems. It is proved that all the signals in the resulting closed-loop system are bounded and that the tracking errors converge to a small residual set with the prescribed performance bounds. The effectiveness of the proposed approach is validated by simulation results.