A DESIGN OF MULTILOOP PID CONTROLLERS WITH A NEURAL‐NET BASED DECOUPLER

In process industries, PID control schemes have been widely used due to their simple structures and easiness of comprehending the physical meanings of control parameters. However, the good control performance cannot be obtained by simply using PID controlschemes, since most processes are considered as nonlinear multivariable systems with mutual interactions. In this paper, a design method of multiloop PID controllers neural‐net based decoupler is proposed for nonlinear multivariable systems with mutual interactions. The proposed method consists of a decoupler given by the sum of a static decoupler and a neural‐net based decoupler, and multi‐loop PID controllers. Finally, the effectiveness of the proposed control scheme is evaluated on the simulation examples.     

一类实际工程问题的解耦PID控制器设计

解耦一直是控制系统中比较难解决的实际问题,为了消除控制系统中耦合的成分,从一个实际的模型出发,通过建立系统模型的传递函数,利用多变量频率设计理论中静态解耦的思想,产生一个解耦器,从而消除系统中耦合的成分,使系统变成两个单输入单输出的模型,然后在此模型基础上,设计出一个解耦的PID控制器.仿真结果验证了该解耦器能够减少系统中耦合的成分,达到解耦的目的.该方法简单实用,可以推广到许多实际的耦合系统中.     

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。     

An Intelligent Design for a PID Controller for Nonlinear Systems

The proportional integral derivative (PID) controller is the most frequently used controller design for industrial applications because of its favorable response and simplicity of adjustment. However, PID manual tuning is traditionally based on engineering experience, and adjusting nonlinear or unknown systems is extremely difficult. In promoting an intelligent controller design theory that can be applied to the control of various systems, this paper proposes a nonlinear control design method that involves determining the optimal solution and obtaining the transfer function of an unknown system by using sequential quadratic programming. In addition, this paper presents a case study of an induction motor V/F speed control to demonstrate the effectiveness of the proposed method based on MATLAB simulation. The results prove that the design of the proposed intelligent PID controller is more robust than traditional controller designs.     

Maximum sensitivity based fractional IMC–PID controller design for non-integer order system with time delay

A simple approach with a small number of tuning parameters is a key goal in fractional order controller design. Recently there have been a number of limited attempts to bring about improvements in these areas. In this paper, a new design method for a fractional order PID controller based on internal model control (IMC) is proposed to handle non-integer order systems with time delay. In order to reduce the number of tuning parameters and mitigate the impact of time delay, the fractional order internal model control scheme is used. Considering the robustness of the control system with respect to process variations and model uncertainty, maximum sensitivity is applied to the tuning of the parameters. The resulting controller has the structure of a fractional order PID which is cascaded with a filter. This is named a fractional IMC–PID controller. Numerical results are given to show the efficiency of the proposed controller.     

Simultaneous closed-loop tuning of cascade controllers based directly on set-point step-response data

This study presents a novel closed-loop tuning method for cascade control systems, in which both primary and secondary controllers are tuned simultaneously by directly using set-point step-response data without resorting to process models. The tuning method can be applied on-line to improve the performance of existing underperforming cascade controllers by retuning controller parameters, using routine operating data. The goal of the proposed design is to obtain the parameters of two proportional-integral-derivative (PID)-type controllers, so that the resulting inner and outer loops behave as similarly as possible to the appropriately specified reference models. The tuning rule and optimization problem related to the proposed design are derived. Based on the rationale behind cascade control, the secondary controller is designed based on disturbance rejection to quickly attenuate disturbances. The primary controller is designed to accurately account for the inner-loop dynamics, without requiring an additional test. In addition, robustness considerations are included in the proposed tuning method, which enable the designer to explicitly address the trade-off between performance and robustness for inner and outer loops independently. Simulation examples show that the proposed method exhibits superior control performance compared with the previous (model-based) tuning methods, confirming the effectiveness of this novel tuning method for cascade control systems.     

Tuning and robustness analysis of event-based PID controllers under different event-generation strategies

In this article, we propose practical rules for tuning event-based PID controllers with two sampling strategies: symmetric send-on-delta (SSOD) and regular quantification (RQ). We present a detailed analysis about the effect of the derivative term of the controller when using SSOD or RQ and some guide lines are given to select the derivative filter coefficient. The two sampling strategies are compared, showing that, even when both of them lead to similar controlled output response, systems with RQ have better robustness properties than those with SSOD. The study is based on the describing function and the results are applicable to process with dynamic responses of different types: with time delays, non-minimum phase, under-damped response, etc. The rules presented here are given in terms of phase and gain margins that are measures of robustness used in the design of continuous PID controllers. This allows the application of conventional PID tuning methods to the case of event-based PID. The tuning rules are very simple and can be used for tuning PID, PI, PD and other controller structures.     

Robust self-tuning PID controller for nonlinear systems

In this paper, we propose a robust self-tuning PID controller suitable for nonlinear systems. The control system employs a preload relay (P_Relay) in series with a PID controller. The P_Relay ensures a high gain to yield a robust performance. However, it also incurs a chattering phenomenon. In this paper, instead of viewing the chattering as an undesirable yet inevitable feature, we use it as a naturally occurring signal for tuning and re-tuning the PID controller as the operating regime digresses. No other explicit input signal is required. Once the PID controller is tuned for a particular operating point, the relay may be disabled and chattering ceases correspondingly. However, it is invoked when there is a change in setpoint to another operating regime. In this way, the approach is also applicable to time-varying systems as the PID tuning can be continuous, based on the latest set of chattering characteristics. Analysis is provided on the stability properties of the control scheme. Simulation results for the level control of fluid in a spherical tank using the scheme are also presented.     

A Design Of Multiloop Predictive Self‐Tuning Pid Controllers

In this paper, a new design scheme of multiloop predictive self‐tuning PID controllers is proposed for multivariable systems. The proposed scheme firstly uses a static pre‐compensator as an approximately decoupling device, in order to roughly reduced the interaction terms of the controlled object. The static matrix pre‐compensator is adjusted by an on‐line estimator. Furthermore, by regarding the approximately decoupled system as a series of single‐input single‐output subsystems, a single‐input single‐output PID controller is designed for each subsystem. The PID parameters are calculated on‐line based on the relationship between the PID control and the generalized predictive control laws. The proposed scheme is numerically evaluated on a simulation example.     

An alternative structure for next generation regulatory controllers: Part I: Basic theory for design, development and implementation

Even though employed widely in industrial practice, the popular PID controller has weaknesses that limit its achievable performance, and an intrinsic structure that makes tuning not only more complex than necessary, but also less transparent with respect to the key attributes of the overall controller performance, namely: robustness, set-point tracking, and disturbance rejection. In this paper, we propose an alternative control scheme that combines the simplicity of the PID controller with the versatility of model predictive control (MPC) while avoiding the tuning problems associated with both. The tuning parameters of the proposed control scheme are related directly to the controller performance attributes; they are normalized to lie between 0 and 1; and they arise naturally from the formulation in a manner that makes it possible to tune the controller directly for each performance attribute independently. The result is a controller that can be designed and implemented much more directly and transparently, and one that outperforms the classical PID controller both in set-point tracking and disturbance rejection while using precisely the same process reaction curve information required to tune PID controllers. The design, implementation and performance of the controller are demonstrated via simulation on a nonlinear polymerization process.     

Adaptive hierarchical tuning of fuzzy controllers

Fuzzy controller design includes both linear and non-linear dynamic analysis. The knowledge base parameters associated within the fuzzy rule base influence the non-linear control dynamics while the linear parameters associated within the fuzzy output signal influence the overall control dynamics. For distinct identification of tuning levels, an equivalent linear controller output and a normalized non-linear controller output are defined. A linear proportional-integral-derivative (PID) controller analogy is used for determining the linear tuning parameters. Non-linear tuning is derived from the locally defined control properties in the non-linear fuzzy output. The non-linearity in the fuzzy output is then represented in a graphical form for achieving the necessary non-linear tuning. Three different tuning strategies are evaluated. The first strategy uses a genetic algorithm to simultaneously tune both linear and non-linear parameters. In the second strategy the non-linear parameters are initially selected on the basis of some desired non-linear control characteristics and the linear tuning is then performed using a trial and error approach. In the third method the linear tuning is initially performed off-line using an existing linear PID law and an adaptive non-linear tuning is then performed online in a hierarchical fashion. The control performance of each design is compared against its corresponding linear PID system. The controllers based on the first two design methods show superior performance when they are implemented on the estimated process system. However, in the presence of process uncertainties and external disturbances these controllers fail to perform any better than linear controllers. In the hierarchical control architecture, the non-linear fuzzy control method adapts to process uncertainties and disturbances to produce superior performance.     

Robust optimization-based multi-loop PID controller tuning: A new tool and its industrial application

Modern process plants are highly integrated and as a result, decentralized PID control loops are often strongly interactive. The iterative SISO tuning approach currently used in industry is not only time consuming, but does also not achieve optimal performance of the inherently multivariable control system. This paper describes a method and a software tool that allows control engineers/technicians to calculate optimal PID controller settings for multi-loop process systems. It requires the identification of a full dynamic model of the multivariable system, and uses constrained nonlinear optimization techniques to find the controller parameters. The solution is tailored to the specific control system and PID algorithm to be used. The methodology has been successfully applied in many industrial advanced control projects. The tuning results that have been achieved for interacting PID control loops in the stabilizing section of an industrial Gasoline Treatment Unit as well as a Diesel Desulfurization plant are presented.     

智能阀门设计与控制方法研究

为了满足智能阀门在工业生产中极高的标准,本文进行研究了智能阀门设计与控制方法,设计了智能阀门硬件系统,包括电气转换模块、微处理器、信号处理模块和阀位反馈模块四个部分,以主控制器LPC2290为核心,以及A/D转换器和CAN总线进行通信,实现了智能阀门的控制功能,为了提高智能阀门控制的精确度,本文利用PID控制器和FUZZY-PI复合算法,通过PID控制的基本原理和稳定边界法整定PID控制参数的过程,完成PID控制器的设计,再结合FUZZY-PI复合算法实现智能阀门的偏差判别与条件控制和多路转换开关与信号的转换,实现智能阀门的高精度控制,最后设计了智能阀门定位器系统,实现了智能阀门的精准定位与自我诊断功能,实验表明,本文研究的系统在4s时就能够达到控制稳定,并且控制精确度可高达95%。     

Design of Low-Bandwidth Spatially Distributed Feedback

The paper considers a family of linear time-invariant and spatially invariant (LTSI) systems that are both distributed and localized. The spatial responses of the distributed plant are localized in spatial neighborhoods of each location. The feedback computations are also distributed and the information flow is localized in a spatial neighborhood of each location. The feedback is aimed at controlling spatial distributions of variables in the systems with a relatively low bandwidth in the time direction. Such systems have many important applications including industrial processes, imaging systems, signal and image processing, and others. We describe a new method for designing (tuning) a certain family of low-bandwidth controllers for such plants. We consider LTSI controllers with a fixed structure, which is a PID or a similar low-bandwidth feedback in time and local in spatial coordinates. Two spatial feedback filters, symmetric and with finite spatial response, modify the local PID control signal by mixing in the error and control signals at nearby nodes. These two filters provide loopshaping and regularization of the spatial feedback loop. Like an ordinary PID controller, this controller structure is simple, but provides adequate performance in many practical settings. We cast a variety of specifications on the steady-state spatial response of the controller and its time response as a set of linear inequalities on the design variables, and so can carry out the design of the spatial filters using linear programming. The method handles steady-state limits on actuator signals, error signals, and several constraints related to robustness to plant and controller variation. The method allows handling the effects of boundary conditions and guaranteed closed-loop spatial or time decay. It does appear to work very well for low-bandwidth controllers, and so is applicable in a variety of practical situations.      

模糊逻辑在PID参数整定中的应用

PID控制是最早发展起来的控制策略之一,由于受到参数整定方法烦杂的困扰,常规PID控制器参数往往性能欠佳,对运行工作情况的适应性差。本设计将模糊控制和PID控制结合起来,构建自适应模糊PID控制器,实现PID参数的最佳调整。该模糊自整定PID控制器既具有PID控制器高精度的优点,又具有模糊控制器快速、稳态特性。     

Parametric Design of Optimal in Average Fractional-Order PID Controller in Flight Control Problem

This paper considers a problem of fractional-order PID controller tuning to optimize it in average over a set of initial states of the plant–controller closed system and over a set of typical input signals. The problem is reduced to a multidimensional optimization problem. We suggest an approach to the solution, implementing it algorithmically. The approach is illustrated by a parametric design of an optimal in average fractional-order PID controller for pitch control of an aircraft.     

一阶时滞不稳定过程的复合PID控制

针对一阶时滞不稳定过程讨论了一类复合ＰＩＤ控制及其参数整定公式．该方法基于分步设计的思想，在比例控制器镇定基础上对闭环所构成的广义稳定对象，设计二级ＰＩＤ控制器．在二级控制器设计中，通过引入时滞二阶稳定模型优化ＰＩＤ参数．本文同时讨论了等价的二自由度ＰＩＤ控制器．     

Development of expert control systems: A pattern classification and recognition approach

In this paper, a pattern classification and recognition approach to expert control systems is developed for use in the on-line analysis and design of dynamic systems. The approach used is based on the tuning of a three-term PID controller and, hence, it is not dependent on a specific form of the process model. A real-time experiment of implementing the developed controller using a microcomputer and associated hardware is presented. A sample set of production rules is discussed. The expert system reaches appropriate tuning parameters, using extracted features, such as oscillatory, underdamped, and exponentially monotonic properties.     

Computational intelligence approach to PID controller design using the universal model

Despite the popularity of PID (Proportional-Integral-Derivative) controllers, their tuning aspect continues to present challenges for researches and plant operators. Various control design methodologies have been proposed in the literature, such as auto-tuning, self-tuning, and pattern recognition. The main drawback of these methodologies in the industrial environment is the number of tuning parameters to be selected. In this paper, the design of a PID controller, based on the universal model of the plant, is derived, in which there is only one parameter to be tuned. This is an attractive feature from the viewpoint of plant operators. Fuzzy and neural approaches - bio-inspired methods in the field of computational intelligence - are used to design and assess the efficiency of the PID controller design based on differential evolution optimization in nonlinear plants. The numerical results presented herein indicate that the proposed bio-inspired design is effective for the nonlinear control of nonlinear plants.