基于GLBest-PSO算法的CSTR系统鲁棒PID控制

连续搅拌反应釜(CSTR)系统是一类具有多变量、强非线性和多工作点的复杂工业过程,对外界扰动及内部参数变化较为敏感。针对常规PID控制器参数整定困难,难以取得满意效果,本文提出了一种基于改进粒子群优化算法的CSTR系统鲁棒PID控制方法。通过对优化目标的分析,将鲁棒PID控制器的参数整定问题转化成一个求解最大-最小问题,在对粒子群优化算法进行改进的基础上,引入合作进化思想对该最大-最小问题进行求解,获得了基于优化性能指标最优的鲁棒PID控制器参数。针对实例的仿真结果表明,利用此方法整定得到的鲁棒PID控制器具有良好的鲁棒性,性能指标优于其它方法得到的鲁棒PID控制器,当过程对象操作范围发生大的变化时,利用本文方法设计得到的鲁棒PID控制器能获得满意的结果。     

参数不确定时滞系统的鲁棒P ID 控制

提出一种简单而有效的参数不确定时滞系统鲁棒PID控制器设计方法．通过在kp-ki平面上绘制稳定边界线,确定稳定的PID控制器参数区域;推导了一阶不稳定时滞系统PI控制器和PID控制器的存在性条件;基于推广到时滞系统的棱边定理,确定所有鲁棒PID控制器参数集．仿真实例表明了该方法的优越性．     

基于模糊自适应PID的剪板机控制系统研究

针对剪板机伺服控制系统的实际需求,在分析了剪板机常用伺服控制方法的基础上,设计了模糊自适应PID控制器.采用模糊控制和PID相结合,克服了传统PID控制的一些缺点.通过对控制系统建模、仿真,其结果表明,和模糊控制、传统PID相比较,该控制器在超调量、调节时间、抗干扰、鲁棒稳定性等方面有更好的品质.     

基于DSP鲁棒PID控制器的电液位置伺服系统电模拟仿真研究

设计了一种基于灵敏度函数的鲁棒PID控制器,并以DSPTMS320F2812加以实现。为了验证该控制器的鲁棒性能,根据电液位置伺服系统的数学模型,设计了研究对象的电模拟仿真器。通过鲁棒PID控制器对电液位置伺服系统进行电模拟仿真实验。仿真实验结果表明：该鲁棒PID控制器在电模拟仿真器电路参数发生一定的变化时,具有很好的鲁棒性能。     

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。     

过热汽温二级模糊鲁棒自调整PID控制器设计

许多过程控制对象的模型结构及参数常常具有随设备运行状态改变而产生较大幅度 变化的特性，或者具有某些不确定性，文章探讨了采用增益配置型模糊PID控制器的参数自 调整的鲁棒设计问题，提出利用二级模糊推理结合系统特征在线辨识实现PID参数动态调整 的控制器设计方法，实例仿真表明该控制器能较好地适应对象动态模型的大幅度变化，保持 较优的鲁棒调节性能．     

基于状态观测器的一类非线性系统的模糊鲁棒控制

利用T－S模型对一类非线性不确定系统进行模糊建模,在此基础上研究模糊鲁棒观测器及模糊状态鲁棒控制器的设计,并证明所设计的模糊鲁棒观测器和模糊状态鲁棒控制器具有全局渐近稳定性质。     

连续搅拌反应釜过程的闭环增益成形PID控制器设计

针对连续搅拌反应釜（CSTR）系统控制问题,设计了一种基于闭环增益成形算法的PID控制器,以提高PID控制器设计的简洁性和鲁棒性。首先假设期望闭环回路传递函数有一阶形式,同时将受控对象的一阶传递函数和PID控制器构成实际闭环回路传递函数。然后,比较期望闭环回路传递函数和实际闭环回路传递函数,即可确定PID参数。最后,以某CSTR系统为例,利用该方法设计了PID控制器,并通过仿真结果比较,检验了该方法所得PID控制器的良好鲁棒稳定性和动态品质。     

一类非自衡过程预测PID控制器的设计及鲁棒稳定性的分析研究*

针对一类非自衡过程,为了提高系统的整体性能,提出了预测PID控制器的设计方法;利用Kharitonov 定理和边缘理论分析此系统在参数不确定情况下输入、输出的鲁棒稳定性,并给出了系统保持稳定的最大过程参数区间。仿真结果表明,当过程参数偏离标称值时,此预测PID控制器的设计方法能够使系统保持很好的鲁棒稳定性,是一种值得在实际工程中推广应用的新型控制器。     

船舶横向运动鲁棒PID控制及优化

针对船舶横向运动控制特点,为达到航向舵控制航向同时达到减横摇的目的,提出了一种基于闭环增益成形和模糊优化算法的鲁棒PID控制器设计方法。根据具有工程实际意义的横摇和艏摇带宽直接构造出横摇、艏摇鲁棒PID控制器,并基于横摇与艏摇频谱,组合成横向控制器。由于用舵来减摇不可避免地增加了舵机的工作负担造成较大的舵机损耗,为此提出对横摇和艏摇控制器输出加入权重分配,采用模糊算法优化横向控制器权系数的方法,达到系统性能综合最优。仿真结果表明所设计的控制器具有较强的鲁棒性,合理的权系数选择,不但可以达到降低舵机损耗的目的,而且提高了航向控制精度。该控制器设计简单,易于船舶横向运动控制的实际工程应用,具有一定的研究价值。     

Robust fuzzy control for a plant with fuzzy linear model

A robust complexity reduced proportional-integral-derivative (PID)-like fuzzy controllers is designed for a plant with fuzzy linear model. The plant model is described with the expert's linguistic information involved. The linguistic information for the plant model is represented as fuzzy sets. In order to design a robust fuzzy controller for a plant model with fuzzy sets, an approach is developed to implement the best crisp approximation of fuzzy sets into intervals. Then, Kharitonov's Theorem is applied to construct a robust fuzzy controller for the fuzzy uncertain plant with interval model. With the linear combination of input variables as a new input variable, the complexity of the fuzzy mechanism of PID-like fuzzy controller is significantly reduced. The parameters in the robust fuzzy controller are determined to satisfy the stability conditions. The robustness of the designed fuzzy controller is discussed. Also, with the provided definition of relative robustness, the robustness of the complexity reduced fuzzy controller is compared to the classical PID controller for a second-order plant with fuzzy linear model. The simulation results are included to show the effectiveness of the designed PID-like robust fuzzy controller with the complexity reduced fuzzy mechanism.     

Design of a hybrid fuzzy logic proportional plus conventionalintegral-derivative controller

Presents approaches to the design of a hybrid fuzzy logic proportional plus conventional integral-derivative (fuzzy P+ID) controller in an incremental form. This controller is constructed by using an incremental fuzzy logic controller in place of the proportional term in a conventional PID controller, By using the bounded-input/bounded-output “small gain theorem”, the sufficient condition for stability of this controller is derived. Based on the condition, we modify the Ziegler and Nichols' approach to design the fuzzy P+ID controller. In this case, the stability of a system remains unchanged after the PID controller is replaced by the fuzzy P+ID controller without modifying the original controller parameters. When a plant can be described by any modeling method, the fuzzy P+ID controller can be determined by an optimization technique. Finally, this controller is used to control a nonlinear system. Numerical simulation results demonstrate the effectiveness of the fuzzy P+ID controller in comparison with the conventional PID controller, especially when the controlled object operates under uncertainty or in the presence of a disturbance     

Online tuning fuzzy PID controller using robust extended Kalman filter

Fuzzy PID controllers have been developed and applied to many fields for over a period of 30 years. However, there is no systematic method to design membership functions (MFs) for inputs and outputs of a fuzzy system. Then optimizing the MFs is considered as a system identification problem for a nonlinear dynamic system which makes control challenges. This paper presents a novel online method using a robust extended Kalman filter to optimize a Mamdani fuzzy PID controller. The robust extended Kalman filter (REKF) is used to adjust the controller parameters automatically during the operation process of any system applying the controller to minimize the control error. The fuzzy PID controller is tuned about the shape of MFs and rules to adapt with the working conditions and the control performance is improved significantly. The proposed method in this research is verified by its application to the force control problem of an electro-hydraulic actuator. Simulations and experimental results show that proposed method is effective for the online optimization of the fuzzy PID controller.     

A Novel Robust Pid Controller Design By Fuzzy Neural Network

In this paper, we propose a robust PID controller tuning method for parametric uncertainty systems (or interval plant family) using fuzzy neural networks (FNNs). This robust controller is based on robust gain and phase margin (GM/PM) specifications that satisfy user requirements. Here, the FNN system is used to identify the relation between the PID controller parameters and robust GM/PM. We can use the trained FNN system to determine the parameters of the PID controllers in order to satisfy robust GM/PM specifications that guarantee robustness and performance. Simulation results are shown to illustrate the effectiveness of the robust controller scheme.     

Relay feedback tuning of robust PID controllers with iso-damping property.

A new tuning method for proportional-integral-derivative (PID) controller design is proposed for a class of unknown, stable, and minimum phase plants. We are able to design a PID controller to ensure that the phase Bode plot is flat, i.e., the phase derivative w.r.t. the frequency is zero, at a given frequency called the "tangent frequency" so that the closed-loop system is robust to gain variations and the step responses exhibit an iso-damping property. At the "tangent frequency," the Nyquist curve tangentially touches the sensitivity circle. Several relay feedback tests are used to identify the plant gain and phase at the tangent frequency in an iterative way. The identified plant gain and phase at the desired tangent frequency are used to estimate the derivatives of amplitude and phase of the plant with respect to frequency at the same frequency point by Bode's integral relationship. Then, these derivatives are used to design a PID controller for slope adjustment of the Nyquist plot to achieve the robustness of the system to gain variations. No plant model is assumed during the PID controller design. Only several relay tests are needed. Simulation examples illustrate the effectiveness and the simplicity of the proposed method for robust PID controller design with an iso-damping property.     

A robust stabilization problem of fuzzy control systems and itsapplication to backing up control of a truck-trailer

A robust stabilization problem for fuzzy systems is discussed in accordance with the definition of stability in the sense of Lyapunov. We consider two design problems: nonrobust controller design and robust controller design. The former is a design problem for fuzzy systems with no premise parameter uncertainty. The latter is a design problem for fuzzy systems with premise parameter uncertainty. To realize two design problems, we derive four stability conditions from a basic stability condition proposed by Tanaka and Sugeno: nonrobust condition, weak nonrobust condition, robust condition, and weak robust condition. We introduce concept of robust stability for fuzzy control systems with premise parameter uncertainty from the weak robust condition. To introduce robust stability, admissible region and variation region, which correspond to stability margin in the ordinary control theory, are defined. Furthermore, we develop a control system for backing up a computer simulated truck-trailer which is nonlinear and unstable. By approximating the truck-trailer by a fuzzy system with premise parameter uncertainty and by using concept of robust stability, we design a fuzzy controller which guarantees stability of the control system under a condition. The simulation results show that the designed fuzzy controller smoothly achieves backing up control of the truck-trailer from all initial positions     

Sugeno fuzzy PID tuning,by genetic-neutral for AVR in electrical power generation

We report a novel design method for determining the optimal proportional-integral-derivative (PID) controller parameters of an automatic voltage regulator (AVR) system, using a combined genetic algorithm (GA), radial basis function neural network (RBF-NN) and Sugeno fuzzy logic approaches. GA and a RBF-NN with a Sugeno fuzzy logic are proposed to design a PID controller for an AVR system (GNFPID). The problem for obtaining the optimal AVR and PID controller parameters is formulated as an optimization problem and RBF-NN tuned by GA is applied to solve the optimization problem. Whereas, optimal PID gains obtained by the proposed RBF tuning by genetic algorithm for various operating conditions are used to develop the rule base of the Sugeno fuzzy system and design fuzzy PID controller of the AVR system to improve the system's response (∼0.005 s). The proposed approach has superior features, including easy implementation, stable convergence characteristic, good computational efficiency and this algorithm effectively searches for a high-quality solution and improve the transient response of the AVR system (7E−06). Numerical simulation results demonstrate that this is faster and has much less computational cost as compared with the real-code genetic algorithm (RGA) and Sugeno fuzzy logic. The proposed method is indeed more efficient and robust in improving the step response of an AVR system.     

基于逆系统方法的综放工作面控制系统的模糊控制器

对逆系统方法作为反馈线性化方法及其非线性本质进行深入的研究后,认知到逆系统方法建立的模糊控制器是一种变增益的非线性控制器,它与PID控制器有许多相似之处,进而设计出模糊PID复合控制器。模糊PID控制器在综放工作面中的应用的关键是模糊控制器的各个参数的整定。因此,我们用传统的方法首先设计一个PID控制器,在稳定时使模糊PID控制器的参数与PID控制器的对应参数相等,逐步调节、修改各个参数,从而可以得出模糊PID控制器的参数,模糊PID控制器在煤矿综放工作面上运用的结果令人满意。     

Robust Stable Fuzzy Control via Fuzzy Modeling and Feedback Linearization with Its Applications to Controlling Uncertain Single-link Flexible Joint Manipulators

In this paper, a robust stable fuzzy control design based on feedback linearization is presented. Takagi–Sugeno fuzzy model is used as representing the nonlinear plant model and uncertainty is assumed to be included in the model structure with known bounds. For this structured uncertainty, the closed system can be analyzed by applying the perturbation system stability analysis to the fuzzy feedback linearization systems and a sufficient condition is derived to guarantee the stability of the closed-loop system with bounded parameter uncertainties. Based on the developed analysis method, we can design a robust fuzzy controller by choosing the control parameters satisfying the robust stability condition.