File Name: controller tuning and control loop performance .zip
This chapter provides a concise survey, classification and historical perspective of practice-oriented methods for designing proportional-integral-derivative PID controllers and autotuners showing the persistent demand for PID tuning algorithms that integrate performance requirements into the tuning algorithm. The proposed frequency-domain PID controller design method guarantees closed-loop performance in terms of commonly used time-domain specifications. One of its major benefits is universal applicability for both slow and fast-controlled plants with unknown mathematical model.
- Advanced Methods of PID Controller Tuning for Specified Performance
- Controller Tuning and Control Loop Performance
- Controller Tuning and Control loop Performance.pdf
Much has been written about the benefits of robust PID control. Increased productivity, decreased equipment strain, and increased process safety are some of the advantages touted of proper PID tuning. What is often overlooked, though, are the negative consequences of poor PID controller tuning. If a well-tuned system helps equipment run longer and safer, then a poorly tuned system may increased failure frequency and safety incidents.
Advanced Methods of PID Controller Tuning for Specified Performance
This chapter provides a concise survey, classification and historical perspective of practice-oriented methods for designing proportional-integral-derivative PID controllers and autotuners showing the persistent demand for PID tuning algorithms that integrate performance requirements into the tuning algorithm. The proposed frequency-domain PID controller design method guarantees closed-loop performance in terms of commonly used time-domain specifications.
One of its major benefits is universal applicability for both slow and fast-controlled plants with unknown mathematical model. Special charts called B-parabolas were developed as a practical design tool that enables consistent and systematic shaping of the closed-loop step response with regard to specified performance and dynamics of the uncertain controlled plant. How to tune a controller for any control application quickly and appropriately?
This question raised in is still up to date and constantly occupies the automation community worldwide. The answer is very intricate; its intricacy is comparable with the open hitherto unresolved Hilbert problems known from mathematics. Will the PID controllers, historically the oldest but currently still the most used ones, control industrial processes in the near and far future?
Based on the increase of the number of PID tuning methods from to during —, a positive response can be assumed [ 23 ]. The remarkably simple ability of the PID controller to generate a difference equation using the present, past and future values of the control error is often projected into the philosophical understanding [ 1 ] and forecast this controller a long-term perspective.
Beginnings of PID controllers date back to when the Taylor Instruments Companies launched their pneumatic controller with a derivative channel [ 1 ]. Owing to rapid developments in the control theory, it was supposed that the conventional PID controllers would be gradually replaced by advanced ones; however, this did not come to pass mainly due to the simple PID structure and its commercial usability in practice. PID controllers are important parts of distributed control systems, predictive control structures; their coefficients are often adapted by means of fuzzy and neural control and set by genetic algorithms [ 20 , 21 , 35 ].
In multiloop control structures, they are able to stabilize unstable objects and difficult-to-control systems. The 46 existing PID variants and reported diverse tuning methods are a good prerequisite for achieving a satisfactory performance in simple as well as demanding industrial applications [ 23 , 25 ].
PID controllers are widely applied in technological processes of heavy and light industries, for example in control of tension in the roll during paper winding, boiler temperature, chemical reactor pressure, lathe spindle position in metalworking, and so on; they can be found in modern cars controlling combustion control or vehicle dynamics [ 9 ], valve opening and robotic arm position.
In the interconnected power system, they are used to control turbine power and speed in both primary and secondary regulation of active power and network frequency. Some controllers not only do not provide the required performance yet often even stability of the control loop being operated only in open loop and manually switched off by the service staff when approaching the setpoint [ 9 ]. Therefore, a natural requirement for innovative PID tuning methods has come up to ensure the specified performance [ 19 , 22 ] in terms of the maximum overshoot and settling time not only for processes with constant parameters but for their perturbed types.
In this chapter, a novel original robust PID tuning method is presented; hopefully, it will help reverse the above mentioned unfavorable statistics of incorrectly tuned PID controllers.
Despite the fact that there are more than 11, PID controllers in 46 variants operating in industrial processes [ 23 ], mostly three basic forms are used to control industrial processes: the ideal textbook PID controller, the real PID controller with derivative filter, and the ideal PID controller in series with the first-order filter given by the following transfer functions, respectively. The PID controller design objectives are: tracking of setpoint or reference variable w t by y t ,.
Time response of the controlled variable y t is modifiable by parameters K, T i and T d , respectively; the objective is to achieve a zero steady-state control error e t irrespective if caused by changes in the reference w t or the disturbance d t.
This section presents practice-oriented PID controller design methods based on various performance criteria. A controller design is a two-step procedure consisting of controller structure selection P, PI, PD or PID followed by tuning coefficients of the selected controller type.
Consider the unity feedback control loop Figure 1 , where G s is the controlled system. According to the final value theorem, the steady-state error. Industrial process variables e. For pressure and level control in gas tanks, using a P-controller is sufficient [ 3 ]. However, adding the derivative part improves closed-loop stability and steepens the step response rise time.
According to Table 1 [ 46 ], the derivative part is not used in presence of intense noise and a PID controller is not appropriate for plants with large time delays. A: forward compensation suggested, B: forward compensation necessary, C: dead-time compensation suggested, D: dead-time compensation necessary, E: set-point weighting necessary, F: pole-placement.
Performance measures for industrial control loops can be expressed both in the time and the frequency domains. The time-domain performance indicators allow to directly expressing the desired process parameter, whereas the frequency-domain performance indicators can be used as PID tuning parameters. In the time-domain, satisfactory setpoint tracking Figure 2a and disturbance rejection Figure 2b are indicated by small values of maximum overshoot and a decay ratio, respectively, given as.
Figure 2c depicts underdamped plot 1 , overdamped plot 2 and critically damped plot 3 closed-loop step responses. By avoiding the Nyquist plot of L s to enter the circles corresponding to M S or M T , a safe distance from the critical point is kept Figure 3a. With further increasing of M t the closed-loop tends to be oscillatory. Relations between individual stability margins and respective magnitude peaks are given by the following inequalities.
When synthesizing a control loop, if the controller type is already known and the designer has just to select a suitable method to appropriately adjust its coefficients, we speak about PID controller tuning methods. Controller design is a more complex problem which includes determining controller structure and then calculating its parameters.
Controller parameters settings are based on observation of the response to reference or disturbance changes with the assistance of an expert, or the design is driven by empirical rules. The control-loop synthesis is time-consuming and its success is not guaranteed. Analytical methods are used to generate a control law based on the mathematical model of the plant; the plant model is obtained from first principles or via experimental identification.
The success of these methods depends on the accuracy of the mathematical model of the plant, and is not always achievable in practical cases e. Classical tuning methods use only a limited number of characteristic parameters of the plant obtained from the step response or critical system parameters [ 11 , 27 , 31 ]. Their main advantage is a simple and short calculation of controller parameters.
The control objective is to provide a satisfactory response to reference change, or disturbance rejection and often their combination. The main drawback is that the designer cannot influence the performance by means of the adjustable parameters of the algorithm. Also, the resulting closed-loop response may not be satisfactory if the step response of the plant is nonmonotonic, or when the plant has nonminimum-phase dynamics or large time delay.
Most of these methods are implemented in autotuners of industrial PID controllers [ 1 ]. Autotuners are a modern and convenient means for adjusting coefficients of industrial controllers [ 33 , 34 , 49 ].
They implement a two-step algorithm of automatic acquisition of characteristic parameters of the controlled process followed by automatic calculation and adjustment of the controller coefficients. After activating the autotuning function on the industrial controller, the control-loop synthesis is performed automatically in a very short time. In many situations, however, these methods are unreliable because of the imperfection of the plant identification algorithm and the subsequent controller design.
Robust PID controller tuning methods for specified performance represent a modern area of industrial control-loop synthesis. They improve the PID tuning methods by providing stability and required performance also for processes with variable parameters. The major disadvantage of these methods is that the control law is not based on the knowledge of uncertainties of the controlled object and a further research on their possible expansion is needed.
The proposed original method which eliminates this drawback, its theoretical analysis and verification on benchmark examples are the core of the chapter. Tuning methods are commonly used engineering tool for the synthesis of industrial control loops as they do not require a full knowledge of the mathematical description of the controlled plant. This differentiates them from analytical methods which, on the contrary are based on a precise knowledge of the mathematical model of the controlled system.
In the tuning methods, the controlled process is considered as a black-box which is to be revealed only to such extent that the controller synthesis is successful and the control objectives are achieved.
Thus, only those characteristic parameters of the unknown plant have to be acquired via appropriate identification that are inevitable for the PID controller design. In this way, the PID controller coefficients can be obtained in a relatively short time. The implicit knowledge about the controlled system and the ambient influences affect the choice of the PID coefficients calculation method.
According to the way of using the identified data of the controlled plant, the tuning methods are classified into as follows: model-free PID controller tuning methods,. Percentage proportions of commonly used PID controller tuning methods are presented in Figure 4.
There are such PID tuning algorithms, which can be applied without any knowledge about the unknown plant model. Their basic feature is that the identified characteristic parameters of the unknown system appear directly in the PID coefficient tuning rules. They are very popular among practitioners due to a high flexibility and ability to control a wide class of systems. The respective algorithms have been tested on benchmark examples, the control objectives can be expressed by empirical rules.
They are simply algorithmizable for application in industrial autotuners. A seven-step flow diagram of a direct tuning method is depicted in Figure 5. The oldest direct-type engineering method is the well-known Ziegler-Nichols frequency method [ 48 ]. The control objective is a rapid disturbance rejection so that each amplitude of the oscillatory response to disturbance step change is only a quarter of the previous amplitude.
The first characteristic parameter provides basic information on plant dynamics, while the value of the second parameter indicates the degree-of-stability of the plant. When first PID controllers were developed in the period —, no tuning methods were available at the market. Acquired experience, however, was generalized giving rise to empirical trial-and-error tuning method that consist of three main steps: Turning off the integral and derivative parts of the PID controller and increasing the gain until the closed-loop oscillates with constant amplitudes, then adjusting the gain at half of this value.
Decreasing the integral time until oscillations with constant amplitude are obtained, then adjusting the integral time at a treble of this value. Increasing the derivative time until oscillations with constant amplitude are obtained, then adjusting the derivative time at a third of this value.
The set of these rules of thumb is still being used in practice to roughly tune industrial PID controllers and is considered as a predecessor of all engineering tuning methods. In , two direct tuning methods were published and authored by Ziegler and Nichols [ 48 ], employees of the Taylor Instrument Companies producing PID controllers.
The first one is time-domain method ; according to it, the PID coefficients are calculated using the effective time delay and the effective time constant of the step response of the industrial plant.
Due to its simplicity, the Ziegler-Nichols frequency-domain methods are still used in industrial autotuners in the original version, although they have undergone various modifications during the last 70 years of its existence. Due to the technological development after the industrial revolution and major electrification, PID tuning for stability was no more sufficient because a fast setpoint attainment could bring about important savings of time and money and accelerate the entire production process.
More and more demanding requirements on control performance were formulated, and an intense demand for effective tuning methods guaranteeing required performance has arisen. As a rule, application of Ziegler-Nichols methods usually leads to oscillatory closed-loop responses; hence, many scientists have become interested in their possible improvement.
Forty-two modifications of the Ziegler-Nichols frequency method were developed in the period from to An overview of selected model-free methods is given in Table 2. Tuning rules No. Related methods No. All presented methods No. In autotuners of industrial controllers, a relay test [ 29 ] using an ideal relay IR or a hysteresis relay HR is used to quickly determine the plant critical parameters K c and T c.
A typical limit cycle is depicted in Figure 7b. A hysteresis relay is used if y t corrupted by a noise n t [ 47 ]. The advantage of these methods is their applicability for different types of systems, simplicity and the short time needed for the controller design of the—approx.
In these methods, the identified characteristics of the unknown system are used to create its typical model, and the controller design algorithm is derived for this particular model. Formulas for calculation of the controller coefficients include process model parameters that are function of the identified process data. Each method works perfectly for the system whose model has been used in the design algorithm.
Controller Tuning and Control Loop Performance
The early 21 st century has seen a renewed interest in research in the widely-adopted proportional-integral-derivative PID controllers. Each chapter has specialist authorship and ideas clearly characterized from both academic and industrial viewpoints. Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control. PID Control in the Third Millennium is of interest to academics requiring a reference for the current state of PID-related research and a stimulus for further inquiry. Industrial practitioners and manufacturers of control systems with application problems relating to PID will find this to be a practical source of appropriate and advanced solutions.
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A proportional—integral—derivative controller PID controller or three-term controller is a control loop mechanism employing feedback that is widely used in industrial control systems and a variety of other applications requiring continuously modulated control. In practical terms it automatically applies an accurate and responsive correction to a control function. An everyday example is the cruise control on a car, where ascending a hill would lower speed if only constant engine power were applied. The controller's PID algorithm restores the measured speed to the desired speed with minimal delay and overshoot by increasing the power output of the engine. The first theoretical analysis and practical application was in the field of automatic steering systems for ships, developed from the early s onwards. It was then used for automatic process control in the manufacturing industry, where it was widely implemented in pneumatic, and then electronic, controllers. Today the PID concept is used universally in applications requiring accurate and optimized automatic control.
Controller Tuning and Control loop Performance.pdf
A "control loop" is a feedback mechanism that attempts to correct discrepancies between a measured process variable and the desired setpoint. A special-purpose computer known as the "controller" applies the necessary corrective efforts via an actuator that can drive the process variable up or down. A home furnace uses a basic feedback controller to turn the heat up or down if the temperature measured by the thermostat is too low or too high. For industrial applications, a proportional-integral-derivative PID controller tracks the error between the process variable and the setpoint, the integral of recent errors, and the derivative of the error signal. It computes its next corrective effort from a weighted sum of those three terms, then applies the results to the process, and awaits the next measurement.
Control loop performance assessment and improvement of an industrial hydrotreating unit and its economical benefits. Longhi I ; A. Reginato I ; H.
Line Control Company, Inc,. Library of Congress Catalog Card: Nilinber: Tfiird Forth t-. Fourth Printing: June, ,
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