PID控制器及智能控制英文文献

Article ID : 1000 - 8152(2000) 06 - 0861 - 07

Introductions to PID Controllers and Intelligent Control

TAN Yonghong and DANG Xuanju

(School of Computer Sciences , Guilin Institute of Electronic Technology ·Guilin , 541004 , P. R. China)

Achiel R. Van Cauwenberghe

(Department of Control Engineering and Automation , University of Ghent ·Ghent , Belgium)

Mehrdad Saif

(School of Engineering Sciences ,Simon Fraser University ·Burnaby , BC , V5A1S6 , Canada)

Abstract ：Industrial automation level has become a measure of professions modernization level is an important symbol. At the same time, the development of the theory of control also experienced the classical control theory and modern control theory and intelligent control theory of three phase automatic control system can be divided into the open loop control system and closed-loop control system. A control system including the controller, sensors, actuators, and transmitter input and output interface. The output after output interface, actuators, add to the control system, Control system, the accused, the transmitter, through after sensor input interface to controller. Different control system, its sensors and actuators, the transmitter is not the same. At present, the PID control and controller or intelligent instrument has many PID controller in the engineering practice, the products have been widely applied, there are all sorts of PID controller products, companies are developed with PID parameter self-setting function of intelligent regulator (intelligent regulator), including the PID controller is adjusted by intelligent automatic adjustment or calibration, the adaptive algorithm is proposed to realize. Have realized using PID control pressure, temperature, flow, liquid level controller, can realize PID control function of the programmable logic controller (PLC), and can realize PID control PC system, etc.

Key words: intelligent control, PID control

PID controllers can be stand-alone controllers (also called single loop controllers), controllers in PLCs, embedded controllers, or software in Visual Basic or C# computer programs.

PID controllers are process controllers with the following characteristics:

_ Continuous process control

_ Analog input (also known as “measurement” or “Process Variable” or “PV”)

_ Analog output (referred to simply as “output”)

_ Setpoint (SP)

_ Proportional (P) , Integral (I) , and/or Derivative (D) constants

Examples of “continuous process control” are temperature, pressure, flow, and level control.for example, controlling the heating of a tank. For simple control, you have two temperature limit sensors (one low and one high) and then switch the heater on when the low temperature limit sensor turns on and then turn the heater off when the temperature rises to the high temperature limit sensor. This is similar to most home air conditioning & heating thermostats.

In contrast, the PID controller would receive input as the actual temperature and control a valve that regulates the flow of gas to the heater. The PID controller automatically finds thecorrect (constant) flow of gas to the heater that keeps the temperature steady at the setpoint.Instead of the temperature bouncing back and forth between two points, the temperature is held steady. If the setpoint is lowered, then the PID controller automatically reduces the amount of gas flowing to the heater. If the setpoint is raised, then the PID controller automatically increases the amount of gas flowing to the heater. Likewise the PID controller would automatically for hot, sunny days (when it is hotter outside the heater) and for cold, cloudy days.

The analog input (measurement) is called the “process variable” or “PV”. You want the PVto be a highly accurate indication of the process parameter you are trying to control. For example, if you want to maintain a temperature of ＋ or － one degree then we typically strive for at least ten times that or one-tenth of a degree. If the analog input is a 12 bit analog input and the temperature range for the sensor is 0 to 400 degrees then our “theoretical” accuracy is calculated to be 400 degrees divided by 4,096 (12 bits) ＝0.09765625 degrees. [1] We say “theoretical” because it would assume there was no noise and error in our temperature sensor, wiring, and analog converter. There are other assumptions such as linearity, etc.. The point being－with 1/10 of a degree “theoretical” accuracy－even with the usual amount of noise and other problemsone degree of accuracy should easily be attainable.

The analog output is often simply referred to as “output”. Often this is given as 0~100 percent. In this heating example, it would mean the valve is totally closed (0%) or totally open (100%) .

The setpoint (SP) is simply－what process value do you want. In this example－what temperature do you want the process at?

The PID controller’s job is to maintain the output at a level so that there is no difference (error) between the process variable (PV) and the setpoint (SP) .

In Fig. 16.1, the valve could be controlling the gas going to a heater, the chilling of a cooler, the pressure in a pipe, the flow through a pipe, the level in a tank, or any other process control system.

Fig. 16.1 PID controller

What the PID controller is looking at is the difference (or “error”) between the PV and the SP. It looks at the absolute error and the rate of change of error. Absolute error means－is there a big difference in the PV and SP or a little difference? Rate of change of error means－is the difference between the PV or SP getting smaller or larger as time goes on.

When there is a “process upset”, meaning, when the process variable or the setpoint quickly changes－the PID controller has to quickly change the output to get the process variable back equal to the setpoint. [2] If you have a walk-in cooler with a PID controller and someone opens the door and walks in, the temperature (process variable) could rise very quickly. Therefore the PID controller has to increase the cooling (output) to compensate for this rise in temperature.

Once the PID controller has the process variable equal to the setpoint, a good PID controller will not vary the output. You want the output to be very steady (not changing) . If the valve (motor, or other control element) is constantly changing, instead of maintaining a constant value, this could cause more wear on the control element.

So there are these two contradictory goals. Fast response (fast change in output) when there is a “process upset”, but slow response (steady output) when the PV is close to the setpoint.

Note that the output often goes past (over shoots) the steady-state output to get the process back to the setpoint. For example, a cooler may normally have its cooling valve open 34% to maintain zero degrees (after the cooler has been closed up and the temperature settled down) . If someone opens the cooler, walks in, walks around to find something, then walks back out, and then closes the cooler door－the PID controller is freaking out because the temperature may have raised 20 degrees! So it may crank the cooling valve open to 50, 75, or even 100 percent－to hurry up and cool the cooler back down－before slowly closing the cooling valve back down to 34 percent. [3]

Let’s think about how to design a PID controller.

We focus on the difference (error) between the process variable (PV) and the setpoint (SP). There are three ways we can view the error.

This means how big is the difference between the PV and SP. If there is a small difference between the PV and the SP－then let’s make a small change in the output. If there is a large difference in the PV and SP－then let’s make a large change in the output. Absolute error is the “proportional” (P) component of the PID controller. The sum of errors over time

Give us a minute and we will show why simply looking at the absolute error (proportional) only is a problem. The sum of errors over time is important and is called the “integral” (I) component of the PID controller. Every time we run the PID algorithm we add the latest error to

the sum of errors. In other words Sum of Errors＝Error1＋Error2＋Error3 ＋Error4＋…. The dead time

Dead time refers to the delay between making a change in the output and seeing the change reflected in the PV. The classical example is getting your oven at the right temperature. When you first turn on the heat, it takes a while for the oven to “heat up”. This is the dead time. If you set an initial temperature, wait for the oven to reach the initial temperature, and then you determine that you set the wrong temperature－then it will take a while for the oven to reach the new temperature setpoint. This is also referred to as the “derivative” (D) component of the PID controller. This holds some future changes back because the changes in the output have been made but are not reflected in the process variable yet.

Absolute Error/Proportional

One of the first ideas people usually have about designing an automatic process controller is what we call “proportional”. Meaning, if the difference between the PV and SP is small－then let’s make a small correction to the output. If the difference between the PV and SP is largethen let’s make a larger correction to the output. This idea certainly makes sense.

We simulated a proportional only controller in Microsoft Excel. Fig. 16.2 is the chart showing the results of the first simulation (DEADTIME＝0, proportional only) :

Proportional and Integral Controllers

The integral portion of the PID controller accounts for the offset problem in a proportional only controller. We have another Excel spreadsheet that simulates a PID controller with proportional and integral control. Here (Fig. 16.3) is a chart of the first simulation with proportional and integral (DEADTIME＝0, proportional＝0.4) .

As you can tell, the PI controller is much better than just the P controller. However, dead time of zero (as shown in the graph) is not common.

Fig. 16.2 The simulation chart

Derivative Control

Derivative control takes into consideration that if you change the output, then it takes time for

that change to be reflected in the input (PV) . For example, let’

s take heating of the oven.

Fig. 16.3 The simulation chart

If we start turning up the gas flow, it will take time for the heat to be produced, the heat to flow around the oven, and for the temperature sensor to detect the increased heat. Derivative control sort of “holds back” the PID controller because some increase in temperature will occur without needing to increase the output further. Setting the derivative constant correctly allows you to become more aggressive with the P & I constants.

Intelligence and intelligent systems can be characterized in a number of ways and along a number of dimensions. There are certain attributes of intelligent systems, common in many definitions, which are of particular interest to the control community.

In the following, several alternative definitions and certain essential characteristics of ntelligent systems are first discussed. A brief working definition of intelligent systems that aptures their common characteristics is then presented. In more detail, we start with a rather eneral definition of intelligent systems, we discuss levels of intelligence, and we explain the role f control in intelligent systems and outline several alternative definitions. [1] We then discuss daptation and learning, autonomy and the necessity for efficient computational structures in ntelligent systems, to deal with complexity. We conclude with a brief working characterization f intelligent (control) systems.

We start with a general characterization of intelligent systems:

An intelligent system has the ability to act appropriately in an uncertain environment, where n appropriate action is that which increases the probability of success, and success is the chievement of behavioral subgoals that support the system’s ultimate goal. [2]

In order for a man-made intelligent system to act appropriately, it may emulate functions of iving creatures and ultimately human mental faculties. An intelligent system can be haracterized along a number of dimensions. There are degrees or levels of intelligence that can e measured along the various dimensions of intelligence. At a minimum, intelligence requires he ability to sense the environment, to make decisions and to control action. Higher levels of ntelligence may include the ability to recognize objects and events, to represent knowledge in a orld model, and to reason about and plan for the future. In advanced forms, intelligence rovides the capacity to

perceive and understand, to choose wisely, and to act successfully under large variety of circumstances so as to survive and prosper in a complex and often hostile nvironment. [3] Intelligence can be observed to grow and evolve, both through growth in omputational power and through accumulation of knowledge of how to sense, decide and act in complex and changing world.

The above characterization of an intelligent system is rather general. According to this, a reat number of systems can be considered intelligent. In fact, according to this definition, even a thermostat may be considered to be an intelligent system, although of low level of intelligence. It is common, however, to call a system intelligent when in fact it has a rather high level of intelligence.

There exist a number of alternative but related definitions of intelligent systems and in the following we mention several. They provide alternative, but related characterizations of intelligent systems with emphasis on systems with high degrees of intelligence.

The following definition emphasizes the fact that the system in question processes information, and it focuses on man-made systems and intelligent machines:

A. Machine intelligence is the process of analyzing, organizing and converting data into knowledge; where (machine) knowledge is defined to be the structured information acquired and applied to remove ignorance or uncertainty about a specific task pertaining to the intelligent machine. This definition leads to the principle of increasing precision with decreasing intelligence, which claims that: applying machine intelligence to a database generates a flow of knowledge, lending an analytic form to facilitate modeling of the process. Next, an intelligent system is characterized by its ability to dynamically assign subgoals and control actions in an internal or autonomous fashion:

B. Many adaptive or learning control systems can be thought of as designing a control law to meet well-defined control objectives. This activity represents the system’s attempt to organize or order its “knowledge” of its own dynamical behavior, so to meet a control objective. The organization of knowledge can be seen as one important attribute of intelligence. If this organization is done autonomously by the system, then intelligence becomes a property of the system, rather than of the system’s designer. This implies that systems which autonomously (self) -organize controllers with respect to an internally realized organizational principle are intelligent control systems. [5]

A procedural characterization of intelligent systems is given next:

C. Intelligence is a property of the system that emerges when the procedures of focusing attention, combinatorial search, and generalization are applied to the input information in order to produce the output. One can easily deduce that once a string of the above procedures is defined, the other levels of resolution of the structure of intelligence are growing as a result of the recursion. Having only one level structure leads to a rudimentary intelligence that is implicit in the thermostat, or to a variable-structure sliding mode controller.

The concepts of intelligence and control are closely related and the term “Intelligent Control” has a unique and distinguishable meaning. An intelligent system must define and use goals. Control is then required to move the system to these goals and to define such goals. Consequently, any intelligent system will be a control system. Conversely, intelligence is necessary to provide desirable functioning of systems under changing conditions, and it is necessary to achieve a high degree of autonomous behavior in a control system. Since control is an essential part of any

intelligent system, the term “Intelligent Control Systems” is sometimes used in engineering literature instead of “Intelligent Systems” or “Intelligent Machines”. The term “Intelligent Control System” simply stresses the control aspect of the intelligent system.

Below, one more alternative characterization of intelligent (control) systems is included. According to this view, a control system consists of data structures or objects (the plant models and the control goals) and processing units or methods (the control laws) :

D. An intelligent control system is designed so that it can autonomously achieve a high level goal, while its components, control goals, plant models and control laws are not completely defined, either because they were not known at the design time or because they changed unexpectedly.

There are several essential properties present in different degrees in intelligent systems. One can perceive them as intelligent system characteristics or dimensions along which different degrees or levels of intelligence can be measured. [6] Below we discuss three such characteristics that appear to be rather fundamental in intelligent control systems.

Adaptation and Learning. The ability to adapt to changing conditions is necessary in an intelligent system. Although adaptation does not necessarily require the ability to learn, for systems to be able to adapt to a wide variety of unexpected changes learning is essential. So the ability to learn is an important characteristic of (highly) intelligent systems.

Autonomy and Intelligence. Autonomy in setting and achieving goals is an important characteristic of intelligent control systems. When a system has the ability to act appropriately in an uncertain environment for extended periods of time without external intervention, it is considered to be highly autonomous. There are degrees of autonomy; an adaptive control system can be considered as a system of higher autonomy than a control system with fixed controllers, as it can cope with greater uncertainty than a fixed feedback controller. Although for low autonomy no intelligence (or “low” intelligence) is necessary, for high degrees of autonomy, intelligence in the system (or “high” degrees of intelligence) is essential.

Structures and Hierarchies. In order to cope with complexity, an intelligent system must have an appropriate functional architecture or structure for efficient analysis and evaluation of control strategies. This structure should be “sparse” and it should provide a mechanism to build levels of abstraction (resolution, granularity) or at least some form of partial ordering so to reduce complexity. [7] An approach to study intelligent machines involving entropy emphasizes such efficient computational structures. Hierarchies (that may be approximate, localized or combined in heterarchies) that are able to adapt, may serve as primary vehicles for such structures to cope with complexity. The term “hierarchies” refers to functional hierarchies, or hierarchies of range and resolution along spatial or temporal dimensions, and it does not necessarily imply hierarchical hardware. Some of these structures may be hardwired in part. To cope with changing circumstances, the ability to learn is essential, so these structures can adapt to significant, unanticipated changes.

In view of the above, a working characterization of intelligent systems (or of (highly) intelligent (control) systems or machines) that captures the essential characteristics present in any such system is:

An intelligent system must be highly adaptable to significant unanticipated changes, and so learning is essential. It must exhibit high degree of autonomy in dealing with changes. It must be able to deal with significant complexity, and this leads to certain sparse types of functional

architectures such as hierarchies.

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