基于T-S模型的PID控制器的设计

针对普通PID调节器参数调节复杂,当被控系统性能发生变化时PID调节器的参数不能自适应地进行调整的缺点,提出了一种基于Takagi和Sugeno模糊控制模型的PID控制器.这种控制器将模糊控制器和PID控制器的优点有效地结合起来,它的前件采用模糊推理的方法将基于语言变量的专家经验转化为T-S模型的模糊规则,它的后件采用PID形式输出.通过对一个带有延时环节的一阶系统进行仿真,并将仿真结果与常规PID以及普通模糊调节器进行比较,结果表明,该控制器对系统的静态和动态性能有很大的改进.     

基于T-S模型模糊PID控制器的设计

由于被控对象的复杂性使得PID的控制精度和鲁棒性较差。为了取得满意的控制效果,提出了一种新型基于T-S模型的模糊PID控制器。     

基于T-S模糊模型的混沌系统脉冲控制

给出了一种基于T-S模糊模型的混沌系统模糊脉冲控制方法.首先给出了基于T-S模糊模型对非线性系统精确建模的原理,得到与混沌系统等价的T-S模糊系统.然后根据建模得到的T-S模糊系统,采用模糊脉冲控制技术来实现控制.最后,以控制Ndolschi混沌系统为例,证明了这种方法的有效性.     

在线自校正模糊PID控制器的研究

该文先设计一个基于T—S模型的模糊PID控制器,为提高模糊控制的适应性,在分析量化因子和比例因子对系统性能影响的基础上,又制定了基于T—S模型的在线调整量化因子和比例因子的模糊调整规则,实现了模糊PID控制器在线自校正。其算法简单,系统实时性、鲁棒性好。对大纯滞后对象的仿真表明,该控制器明显改善系统的动态性能。     

自调整模糊PID位置控制器设计

针对一种新型的随动系统-数字交流随动系统的位置控制器的控制方式进行研究,将模糊控制理论和自调整控制与PID控制有机的结合起来,重点讨论了自调整模糊PID位置控制器的原理及设计,采用模糊控制理论对控制过程的进行控制,应用自调整控制对系统参数进行在线调整,以保证控制过程的平滑性,并结合PID控制方法,试验结果表明.该控制既能保证系统的平稳过渡,又具有较好的跟随性和快速性.     

基于T-S模型的模糊神经网络PID控制

针对在非线性、时变不确定系统中,常规PID控制器难以获得满意效果的问题,仿照传统PID控制器结构,设计了一种基于T-S模型的模糊神经网络PID控制器。该控制器基于T-S模糊模型,将PID结构融入模糊控制中,充分发挥了模糊系统非线性、可解释性的特点;然后又利用神经网络的学习算法,实现了对模糊控制器的参数调整,使控制器具有了适应时变、不确定系统的自学习和自组织能力。针对非线性、时变系统,将此控制器与传统PID控制器对比进行了仿真研究,并应用于啤酒发酵领域,其结果表明,该控制器取得了令人满意的效果。     

模糊PID控制器的设计

传统的PID控制由于其独特的优势被广泛应用到了工业控制领域,但它需要知道被控对象的数学模型,这就使其在复杂控制领域的应用受到了限制,而模糊控制是建立在专家知识、经验上的一种控制策略,不需要知道被控对象的数学模型。本文就将这两种方法的优点有效地结合在了一起,设计了一种新的控制器。     

基于改进QGA的T-S模糊控制器设计

利用基于量子位测量的二进制量子遗传算法(QGA)对连续问题进行优化时,频繁的解码运算严重降低了优化效率。针对该问题,提出一种基于量子位相位编码的QGA。该算法直接采用量子位的相位对染色体进行编码,利用量子旋转门实现染色体上相位的更新,通过Pauli-Z门实现染色体的变异,由于优化过程统一在 空间进行,因此对不同尺度空间的优化问题具有良好的适应性。以单级倒立摆T-S模糊控制器参数的优化设计为例进行仿真,证明该算法在搜索能力和优化效率方面的优势。     

用继电自整定实现模糊PID智能控制

从提高控制器的智能化水平出发，文中提出了模糊PID自适应控制与继电自整定相结合构成PID双模智能控制器的方法。即用继电自整定法整定出PID控制的初始参数，然后切换到模糊PID自适应控制，完成模糊PID智能控制。将该算法应用于一温控系统中，得到了令人满意的效果。     

一种改进的模糊免疫反馈PID控制器

针对P型免疫反馈控制器不能克服动态干扰和消除静态误差的问题,利用模糊控制系统的非线性逼近能力,提出了一种将P型免疫反馈控制器同常规PID控制器进行混合联结的模糊免疫PID控制器的设计方法。该控制器通过免疫反馈控制规律和模糊控制规则在线调整控制器的参数。为了选择一组较优的控制器参数,用免疫算法在全局范围内对控制参数进行离线优化。仿真结果表明,该控制器较常规控制器具有更好的动态、静态特性。     

基于T-S模糊模型的状态反馈预测控制

将T-S模糊模型和状态反馈预测控制相结合,提出了一种基于T-S模糊模型的预测控制算法.该算法把T-S模糊模型作为预测模型得到状态和输出的预估值,并利用可测的过程变量对输出预估值进行反馈修正,然后利用最优控制理论,由修正后的预估值和给定值计算出控制整个系统的控制律.本文还对串级CSTR控制系统的不同的初态、设定值及干扰情况下进行了仿真,仿真结果表明了该方法的有效性和可行性.     

Statistic PID tracking control for non-Gaussian stochastic systems based on T-S fuzzy model

A new robust proportional-integral-derivative （PID） tracking control framework is considered for stochastic systems with non-Gaussian variable based on B-spline neural network approximation and T-S fuzzy model identification. The tracked object is the statistical information of a given target probability density function （PDF）, rather than a deterministic signal. Following B-spline approximation to the integrated performance function, the concerned problem is transferred into the tracking of given weights. Different from the previous related works, the time delay T-S fuzzy models with the exogenous disturbances are applied to identify the nonlinear weighting dynamics. Meanwhile, the generalized PID controller structure and the improved convex linear matrix inequalities （LMI） algorithms are proposed to fulfil the tracking problem. Furthermore, in order to enhance the robust performance, the peak-to-peak measure index is applied to optimize the tracking performance. Simulations are given to demonstrate the efficiency of the proposed approach.     

Adaptive sliding mode controller design based on T-S fuzzy system models

An adaptive sliding mode control (ASMC) technique based on T-S fuzzy system models is proposed in this paper for a class of perturbed nonlinear MIMO dynamic systems in order to solve tracking problems. A T-S fuzzy model is firstly formed by utilizing fuzzy theorem to amalgamate a set of linearized dynamic equations. The adaptive sliding mode controller is then designed based on this fuzzy model with perturbations. The proposed control scheme can drive the dynamics of controlled system into a designated sliding surface in finite time, and guarantee the property of asymptotical stability. It is also shown that the information of upper bound of modeling errors as well as perturbations, except the information of upper bound of input uncertainty, is not required when using the proposed controller.     

基于模糊双曲模型的可靠控制器设计

考虑执行器出现故障,提出一种基于模糊双曲模型(FHM)的可靠保性能控制策略.首先用模糊双曲模型表述一类离散非线性系统,建立基于模糊双曲模型的控制器;然后通过LMI方法设计该控制器,以保证系统在正常状况和执行器出现故障时都是渐近稳定的,并通过求解一个基于LMI的优化问题,得到最优的控制器增益矩阵,使得保性能指标的上界最小.仿真结果表明了该方法的有效性.     

Takagi-Sugeno fuzzy model based indirect adaptive fuzzy observer and controller design

This paper proposes the design scheme of the alternative adaptive observer and controller based on the Takagi-Sugeno (T-S) fuzzy model. The T-S fuzzy modeling and the state feedback control technique are adopted for the simple structure. The proposed method maintains consistent performance in the presence of parameter uncertainties and incorporates linguistic fuzzy information from human operators. In addition, with the simple adaptive state feedback controller, it solves the singularity problem, which occurs in the inverse dynamics based on the feedback linearization method. Using Lyapunov theory and Lipschitz condition, the stability analysis is conducted, and the adaptive law is derived. The proposed method is applied to the stabilization problem of a flexible joint manipulator in order to guarantee its performance.     

基于T-S 模型和小世界优化算法的广义非线性预测控制

提出一种新型的基于T-S模糊模型和小世界优化算法的广义非线性预测控制策略.采用基于混沌遗传算法的T-S模糊模型描述复杂非线性系统的动态特性,构成模糊多步预报器.同时,针对现有基于二进制和十进制编码小世界优化算法运行时间长等缺点,提出一种新型的基于实数编码的小世界优化算法,函数测试和应用于非线性预测控制的滚动优化反映了其较强的寻优能力.最后,将其应用于基于实际数据的T-S模糊模型的广义非线性预测控制,满足了系统实时性和快速稳定性的要求.     

基于MATLAB的模糊自整定PID参数控制器的设计与仿真

针对在复杂系统中实现自整定参数的PID控制问题,介绍了一种基于模糊控制原理的PID参数自整定控制器的设计,并把MATLAB中的FuzzyToolbox和SIMULINK有机结合起来,方便的实现了该模糊自整定PID参数控制系统的计算机仿真。     

Controller design of uncertain nonlinear systems based on T-S fuzzy model

A robust control for uncertain nonlinear systems based on T-S fuzzy model is discussed in this paper. First, a T-S fuzzy system is adopted to model the uncertain nonlinear systems. Then, for the system with input variables adopting standard fuzzy partitions, the efficient maximal overlapped-rules group （EMORG） is presented, and a new sufficient condition to check the stability of T-S fuzzy system with uncertainty is derived, which is expressed in terms of Linear Matrix Inequalities. The derived stability condition, which only requires a local common positive definite matrix in each EMORG, can reduce the conservatism and difficulty in existing stability conditions. Finally, a simulation example shows the proposed approach is effective.     

Event-triggered controller design of nonlinear discrete-time networked control systems in T-S fuzzy model

This article is concerned with event-triggered fuzzy control design for a class of discrete-time nonlinear networked control systems (NCSs) with time-varying communication delays. Firstly, a more general mixed event-triggering scheme (ETS) is proposed. Secondly, considering the effects of the ETS and communication delays, based on the T-S fuzzy model scheme and time delay system approach, the original nonlinear NCSs is reformulated as a new event-triggered networked T-S fuzzy systems with interval time-varying delays. Sufficient conditions for uniform ultimately bound (UUB) stability are established in terms of linear matrix inequalities (LMIs). In particular, the quantitative relation between the boundness of the stability region and the triggering parameters are studied in detail. Thirdly, a relative ETS is also provided, which can be seen as a special case of the above proposed mixed ETS. As a difference from the preceding results, sufficient conditions on the existence of desired fuzzy controller are derived to ensure the asymptotic stability of the closed-loop system with reduced communication frequency between sensors and controllers. Moreover, a co-design algorithm for simultaneously determining the gain matrices of the fuzzy controller and the triggering parameters is developed. Finally, two illustrative examples are presented to demonstrate the advantage of the proposed ETS and the effectiveness of the controller design method.     

基于模糊控制器的自适应广义通用模型控制

广义通用模型控制(GCMC)方法是一般模型控制(GMC)的改进,适用于相对阶大于1的复杂多输入多输出系统,该控制器参数具有明显的物理意义,但鲁棒性不够强。将模糊控制与广义通用模型控制相结合,构成模型参考自适应控制系统,从而加强了系统的鲁棒性,仿真实验证明了该策略的有效性。