非线性系统的模糊免疫PSD控制与仿真

针对模糊免疫PID控制算法中微分与积分增益不能根据系统特性自动调整的问题,提出了一种模糊免疫PSD(Proportional Summation Derivative)控制算法。该方法将自适应PSD算法与模糊免疫PID算法相结合,利用自适应PSD控制算法根据过程误差的几何特性建立的PSD控制规律,使得模糊免疫PID控制算法中的微分和积分增益可以随比例增益的变化而自适应调整,从而进一步提高控制算法的自适应性能。仿真实验表明,采用该算法可以提高非线性、时变系统的控制性能,并能减少参数调整的工作量。     

非线性PID自学习控制方法研究

针对非线性、多变量、强耦合系统的控制问题,提出一种基于双曲函数的非线性PID自学习控制方法。在PID控制过程中,用双曲函数构造比例、积分、微分3个增益参数分别随误差变化的规律曲线,将传统线性PID控制律转化为非线性PID控制律的控制思想,并使用自学习算法实时调整3个非线性增益函数的增益系数,实现基于双曲函数的非线性PID自学习控制。仿真实验结果表明,与其他控制方法相比,该方法具有更强的鲁棒稳定性和抗扰动能力,是一种有效的控制方法,在非线性控制领域具有重要的应用价值。     

基于最优预测的神经元模糊自整定PID控制

针对工业过程中普遍存在的时滞、非线性、对象参数时变等特性，提出了一种基于最优预测的神经元模糊自整定PID控制算法。该算法利用最优预测克服时滞，利用神经元学习功能和模糊控制调节神经元增益克服系统的非线性和时变问题。仿真结果表明，该控制算法鲁棒性强、响应速度快，具有工业控制应用价值。     

飞机燃油系统地面模拟试验引气箱压力控制

飞机燃油系统地面模拟试验的引气箱压力控制系统是电、液气压控制系统;由于气体的可压缩性及控制过程中气体压力、体积、温度三者之间的变化,系统具有很强的非线性和时变特性;针对系统这些特点,提出了基于模糊自适应PID的控制策略,将模糊控制器响应快、抑制扰动能力强的优点与PID控制器鲁棒性强的特性融为一体;仿真分析表明该方法有效地提高了非线性时变系统的动、静态特性。     

永磁同步电机模糊PID控制建模与仿真

永磁同步电机（PMSM）作为一种非线性、强耦合、参数时变的被控对象,传统PID对其控制效果欠佳。为此在PID的基础上,结合模糊控制的优点,设计了一种PMSM的模糊自适应PID控制策略,并对系统建模仿真。结果表明,与PID控制相比,该模糊PID控制方案具有自适应强、超调量小等特点。     

SR电机模糊自整定PID控制器设计与仿真

针对开关磁阻电机的非线性、时变和强耦合性,提出了模糊自整定PID的调速控制策略.在分析SR电机非线性数学模型的基础上,将速度偏差和偏差变化率作为控制器输入,PID的三个控制参数为输出.利用模糊规则对PID参数进行在线修改,实现PID参数的自动最佳调整.通过将MATLAB中的Fuzzy Toolbox和SIMULINK有机结合,实现了基于模糊自整定PID控制器的开关磁阻电机调速系统仿真,并进行了传统PID控制与模糊自整定PID控制的仿真比较.结果表明:模糊自整定PID控制器能够明显改善系统的调速性能,具有较强的鲁棒性,并且计算量少,易于实现,便于工程应用.     

柴油机电液调速系统控制的研究

车用柴油机电液调速系统的控制,具有非线性、时变、非稳定等特点,柴油机调速控制采用采用模糊自整定PID控制和采用传统PID控制两种控制算法,都难以达到满意的控制效果.本文采用非线性控制器来控制其效果非常明显.     

柴油机电液调速系统控制的研究

车用柴油机电液调速系统的控制,具有非线性、时变、非稳定等特点,柴油机调速控制采用采用模糊自整定PID控制和采用传统PID控制两种控制算法,都难以达到满意的控制效果。本文采用非线性控制器来控制其效果非常明显。     

液压位置伺服系统的模糊PID控制研究

针对液压位置伺服系统中参数时变和非线性等特点,使用模糊PID控制算法实现对PID参数的在线自调整。Matlab仿真表明,与传统PID控制相比,模糊PID控制系统具有超调小、稳态精度高、鲁棒性强等特点。     

液压位置伺服系统的模糊PID控制研究

针对液压位置伺服系统中参数时变和非线性等特点,本文使用模糊PID控制算法实现对PID参数的在线自调整。Matlab仿真表明,与传统PID控制相比,模糊PID控制具有超调小、稳态精度高、鲁棒性强等特点。     

Real-time control of a water-gas shift reactor by a model-based fuzzy gain scheduling technique

A model-based fuzzy gain scheduling technique is proposed. Fuzzy gain scheduling is a form of variable gain scheduling which involves implementing several linear controllers over a partitioned process space. A higher-level rule-based controller determines which local controller is executed. Unlike conventional gain scheduling, a controller with fuzzy gain scheduling uses fuzzy logic to dynamically interpolate controller parameters near region boundaries based on known local controller parameters. Model-based fuzzy gain scheduling (MFGS) was applied to PID controllers to control a laboratory-scale water-gas shift reactor. The experimental results were compared with those obtained by PID with standard fuzzy gain scheduling, PID with conventional gain scheduling, simple PID and a nonlinear model predictive control (NMPC) strategy. The MFGS technique performed comparably to the NMPC method. It exhibited excellent control behaviour over the desired operating space, which spanned a wide temperature range. The other three PID-based techniques were adequate only within a limited range of the same operating space. Due to the simple algorithm involved, the MFGS technique provides a low cost alternative to other computationally intensive control algorithms such as NMPC.     

Hybrid fuzzy control of robotics systems

This paper presents a new approach towards optimal design of a hybrid fuzzy controller for robotics systems. The salient feature of the proposed approach is that it combines the fuzzy gain scheduling method and a fuzzy proportional-integral-derivative (PID) controller to solve the nonlinear control problem. The resultant fuzzy rule base of the proposed controller can be decomposed into two layers. In the upper layer, the gain scheduling method is incorporated with a Takagi-Sugeno (TS) fuzzy logic controller to linearize the robotics system for a given reference trajectory. In the lower layer, a fuzzy PID controller is derived for all the locally linearized systems by replacing the conventional PI controller by a linear fuzzy logic controller, which has different gains for different linearization conditions. Within the guaranteed stability region, the controller gains can be optimally tuned by genetic algorithms. Simulation studies on a pole balancing robot and a multilink robot manipulator demonstrate the effectiveness and robustness of the proposed approach.     

甲醇/水二元间歇精馏塔的建模与先进控制

应用动态物料平衡和相平衡等机理建模法,建立了一套甲醇／水二元间歇精馏塔实验装置的数学模型。以Matlab为平台,开发了该模型计算软件。设计了塔顶甲醇浓度比例积分反馈推断控制、多个温度联合加醛反馈控制、塔顶甲醇浓度反馈增益自调整控制等控制策略。并应用基于状态空间模型的模型预测控制算法,推导了间歇精馏塔多个浓度反馈时的模型预测控制策略。对这些先进控制策略进行了仿真试验研究,仿真结果被用于指导间歇精馏控制实验,获得了满意的控制结果。     

Un-symmetric input temperature control by using fuzzy sliding mode controller with gain auto-tuning

In order to obtain the desired product quality, temperature is an important control parameter in chemical and semiconductor manufacturing processes. Generally, the temperature control system has nonlinear time-varying, slow response speed, time-delay and un-symmetric control input dynamic characteristics. It is difficult to accurately establish the dynamic model for designing a general purpose temperature controller to achieve good control performance. Here a model-free fuzzy sliding mode control strategy is employed to design an intelligent temperature controller with gain-scheduling scheme or gain auto-tuning algorithm for a closed chamber with heater one-way input only. The concept of gain scheduling is employed to adjust the mapping ranges of the input and output fuzzy membership functions during the control process for improving the transient and steady-state control performances. The experimental results show that the steady state error of the step input response is always less than 0.2°C without overshoot by using this intelligent control schemes. It is suitable for industrial temperature control systems.     

基于模糊控制的IPMSM速度控制器及电流控制器设计

本文在IPMSM矢量控制的基础上,利用模糊控制实现对IPMSM系统的自适应控制,运用FLC分析和设计了速度和电流控制器,给出了模糊规则和隶属函数。仿真结果表明,与采用传统的PID控制相比,采用模糊控制可以很好地克服系统的非线性、时变、强耦合等因素的不良影响,使系统具有较好的动态特性和鲁棒性,提高了系统的性能。     

异步电机软启动的变论域模糊自适应控制策略

异步电机软启动系统是一个时变、非线性的高阶系统,将变论域模糊自适应控制方法应用于异步电机的软启动控制系统中,并建立电机软启动控制系统的Matlab/Simulink的仿真模型,给出了变论域自适应模糊控制方法的仿真结果,与传统PID控制和常规模糊软启动控制方法进行了比较。仿真结果表明,该方法具有无需精确建模、响应快速、精度高、鲁棒性好、适应性强等优点,切实可行,并优于其他控制方法。     

模糊免疫PID算法在温度控制系统中的应用研究

该文针对温度控制系统非线性、大滞后、参数时变的特征,设计了模糊免疫PID控制器。该控制器结合了模糊逻辑、免疫机理以及PID调节的各种优点,既具有模糊控制的非线性作用,又具有免疫控制的自适应能力,同时还具有PID控制的广泛适用性。文章介绍了模糊免疫PID控制器的控制原理和设计方法,在Matlab中编写函数仿真,结果表明该控制器能够实现持续干扰情况下的闭环鲁棒稳定,并使系统呈现良好的动态和静态性能。     

多变量模糊系统控制设计及其在并行混合电动汽车中的应用

利用矩阵半张量积方法研究了多变量模糊系统模糊逻辑控制器的设计,并得到了若干新的结果.首先给出了模糊规则新的表示形式,基于该表示形式,构造了模糊逻辑控制器的结构矩阵,将复杂的模糊推理转变成了简单的代数等式.然后当模糊控制规则不完全时,建立了最小入度控制算法;当模糊控制规则不一致时,给出了相应的处理方法.最后将得到的结果应用到并行混合电动汽车(PHEV)能量管理和控制策略的模糊控制器设计.     

Decentralized adaptive tracking control of nonaffine nonlinear large-scale systems with time delays

This paper addresses the problem of decentralized tracking control of large-scale systems with uncertain nonaffine nonlinear isolated subsystems and nonlinear interconnections with time-varying delays. Based on Lyapunov-Krasovskii functional approach and implicit function theorem, a delay-independent decentralized tracking controller is proposed. Due to functional approximation capability of fuzzy logic systems (FLS), neither strict structure restrictions on the isolated subsystems nor a priori knowledge of the strong interconnections with time-varying delays is required in our control design. Furthermore, transient performance of the resulting closed-loop system is also addressed under an analytical framework. Finally, two numerical examples are provided to show the effectiveness of the proposed controller.     

未知多变量非线性系统自适应模糊预测控制

对一类未知多变量非线性系统提出了直接自适应模糊预测控制方法,此方法首先对被控对象提出了线性时变子模型加非线性子模型的预测模型,然后直接用模糊逻辑系统组成的向量来设计预测控制器,并基于时变死区函数对控制器中的未知向量和广义误差估计值中的未知矩阵进行自适应调整．文中证明了此方法可使广义误差向量估计值收敛到原点的一个邻域内．