Industrial Automation - Advanced Process Control

Advanced Process Control

             Introduction

Advanced process control (APC) is a broad term within the control theory. It is composed of different kinds of process control tools, for example, model predictive control (MPC), statistical process control (SPC), Run2Run (R2R), fault detection and classification (FDC), sensor control and feedback systems. APC is often used for solving multivariable control problems or discrete control problems.

             Overview of Advanced Control Methods

             Adaptive Control

An adaptive control system can be defined as a feedback control system intelligent enough to adjust its characteristics in a changing environment so as to operate in an optimal manner according to some specified criteria.

Generally speaking, adaptive control systems have achieved great success in aircraft, missile, and spacecraft control applications. It can be concluded that traditional adaptive control methods are mainly suitable for:

•       Mechanical systems that do not have significant time delays; and

•       Systems that have been designed so that their dynamics are well understood.

In industrial process control applications, however, traditional adaptive control has not been very successful.

             Robust Control

Robust control is a controller design method that focuses on the reliability (robustness) of the control algorithm. Robustness is usually defined as the minimum requirement a control system has to satisfy to be useful in a practical environment. Once the controller is designed, its parameters do not change and control performance is guaranteed.

Robust control methods are well suited to applications where the control system stability and reliability are the top priorities, process dynamics are known, and variation ranges for uncertainties can be estimated. Aircraft and spacecraft controls are some examples of these systems.

             Predictive Control

Predictive control, or model predictive control (MPC), is one of only a few advanced control methods used successfully in industrial control applications. The essence of predictive control is based on three key elements:

•       Predictive model,

•       Optimization in range of a temporal window, and

•       Feedback correction.

These three steps are usually carried on continuously by computer programs online. Predictive control is a control algorithm based on the predictive model of the process. The model is used to predict the future output based on the historical information of the process as well as the future input. It emphasizes the function of the model, not the structure of the model.

Predictive control is an algorithm of optimal control. It calculates future control actions based on a penalty function or performance function. The optimization of predictive control is limited to a moving time interval and is carried on continuously online. The moving time interval is sometimes called a temporal window. This is the key difference compared to traditional optimal control that uses a performance function to judge global optimization

Predictive control is also an algorithm of feedback control. If there is a mismatch between the model and process, or if there is a control performance problem caused by the system uncertainties, the predictive control could compensate for the error or adjust the model parameters based on on-line identification.

             Optimal Control

Optimal control is an important component in modern control theory. Its great success in space, aerospace, and military applications has changed our lives in many ways.

The statement of a typical optimal control problem can be expressed in the following:

”The state equation and its initial condition of a system to be controlled are given. The defined objective set is also provided.”

Find a feasible control such that the system starting from the given initial condition transfers its state to the objective set, and minimizes a performance index. In practice, optimal control is very well suited for space, aerospace, and military applications such as the moon landing of a spacecraft, flight control of a rocket, and the missile blocking of a defense missile.

             Intelligent Control

Intelligent control is another major field in modern control technology. There are different definitions regarding intelligent control, but it is referred to as a control Para diagram that uses various artificial intelligence techniques, which may include the following methods:

•       Learning control,

•       Expert control,

•       Fuzzy control, and

•       Neural network control.

Learning Control: Learning control uses pattern recognition techniques to obtain the current status of the control loop; and then makes control decisions based on the loop status as well as the knowledge or experience stored previously.

Expert Control: Expert control, based on the expert system technology, uses a knowledge base to make control decisions. The knowledge base is built by human expertise, system data acquired on-line, and inference machine designed. Since the knowledge in expert control is represented symbolically and is always in discrete format, it is suitable for solving decision making problems such as production planning, scheduling, and fault diagnosis. It is not well suited for continuous control issues.

Fuzzy Control: Fuzzy control, unlike learning control and expert control, is built on mathematical foundations with fuzzy set theory. It represents knowledge or experience in a mathematical format that process and system dynamic characteristics can be described by fuzzy sets and fuzzy relational functions. Control decisions can be generated based on the fuzzy sets and functions with rules.

Neural Network Control: Neural network control is a control method using artificial neural networks. It has great potential since artificial neural networks are built on a firm mathematical foundation that includes versatile and well understood mathematical tools. Artificial neural networks are also used as one of the key elements in the model-free adaptive controllers.

             Internal Model Control

The Internal Model control (IMC) philosophy relies on the Internal Model principle, which states that “control can be achieved only if the control system encapsulates, either implicitly or explicitly; some representation of the process to be controlled”. In particular, if the control scheme has been developed based on an exact model of the process, then perfect control is theoretically possible. Consider the example shown in the diagram below.

 Open loop control strategy

 A controller, G_c(s), is used to control the process, G_p(s). Suppose G _p (s) is a model of G_p(s). By setting G_c(s) to be the inverse of the model of the process,

G_c(s) = G _p (s)^-1,

And if G_p(s) = G _p (s) ,(the model is an exact representation of the process)

Then it is clear that the output will always be equal to the set point.

             The IMC Strategy

In practice, however, process-model mismatch is common; the process model may not be invertible and the system is often affected by unknown disturbances. Thus the above open loop control arrangement will not be able to maintain output at set point. Nevertheless, it forms the basis for the development of a control strategy that has the potential to achieve perfect control.

              Model Predictive Control(MPC)

Model predictive control, or MPC, is an advanced method of process control. Model predictive controllers rely on dynamic models of the process, most often linear empirical models obtained by system identification. The models are used to predict the behavior of dependent variables (i.e, outputs) of a dynamical system with respect to changes in the process independent variables (i.e., inputs). In chemical processes, independent variables are most often set points of regulatory controllers that govern valve movement (eg., valve positioners with or without flow, temperature or pressure controller cascades), while dependent variables are most often constraints in the process (eg., product purity, equipment safe operating limits). The model predictive controller uses the models and current plant measurements to calculate future moves in the independent variables that will result in an operation that honors all independent and dependent variable constraints. The MPC then sends this set of independent variable moves to the corresponding regulatory controller set points to be implemented in the process.

             Model Representations

MPC is widely adopted in the process industry as an effective means to deal with large multivariable constrained control problems. The main idea of MPC is to choose the control action by repeatedly solving online an optimal control problem. This aims at minimizing a performance criterion over a future horizon, possibly subject to constraints on the manipulated inputs and outputs, where the future behavior is computed according to a model of the plant.

Predictive Constrained Control: PID type controllers do not perform well when applied to systems with significant time-delay. Perhaps the best known technique for controlling systems with large time-delays is the Smith Predictor. It overcomes the debilitating problems of delayed feedback by using predicted future states of the output for control.

Multivariable Control: Most processes require the monitoring of more than one variable. Controller-loop interaction exists such that the action of one controller affects other loops in a multi-loop system. Depending upon the inter-relationship of the process variables, tuning each loop for maximum performance may result in system instability when operating in a closed-loop mode. Loops that have single input single output (SISO) controllers may therefore not be suitable for these types of applications. These types of controllers are not designed to handle the effects of loop interactions.

A multivariable controller, whether it be a Multiple Input Single Output (MISO) or a Multiple Input Multiple Output (MIMO) is used for systems that have these types of interactions.

Model- Based Predictive Control: Model-Based Predictive Control technology utilizes a mathematical model representation of the process. The algorithm evaluates multiple process inputs, predicts the direction of the desired control variable, and manipulates the output to minimize the difference between target and actual variables. Strategies can be implemented in which multiple control variables can be manipulated and the dynamics of the models are changed in real time.

Dynamic Matrix Control: Dynamic Matrix Control (DMC) is also a popular model-based control algorithm. A process model is stored in a matrix of step or impulse response coefficients. This model is used in parallel with the on-line process in order to predict future output values based on the past inputs and current measurements.

Statistical Process Control: Statistical Process Control (SPC) provides the ability to determine if a process is stable over time, or, conversely, if it is likely that the process has been influenced by "special causes" which disrupt the process. Statistical Control Charts are used to provide an operational definition of a "special cause" for a given process, using process data.

SPC has been traditionally achieved by successive plotting and comparing a statistical measure of the variable with some user defined control limits. If the plotted statistic exceeds these limits, the process is considered to be out of statistical control. Corrective action is then applied in the form of identification, elimination or compensation for the assignable causes of variation. "On-line SPC" is the integration of automatic feedback control and SPC techniques. Statistical models are used not only to define control limits, but also to develop control laws that suggest the degree of manipulation to maintain the process under statistical control.