Multiloop Controllers

In this chapter we have considered control problems with multiple inputs (manipulated variables) and multiple outputs (controlled variables), with the main focus on using a set of single-loop controllers (multiloop control). Such MIMO control problems are more difficult than SISO control problems because of the presence of process interactions. Process interactions can produce undesirable control loop interactions for multiloop... 

Strong process interacHons can cause serious problems if a conventional multiloop feedback control scheme (e g., PI or PID controllers) is employed. The process interacHons canproduce undesirable control loop interac tions where the controllers fight each other. Also, it may be difficult to determine the best pairing of controlled and manipulated variables. For example, in the in-hne blending process in Fig. 8-40(<7), should w be controlled with and x with tt>g, or vice versa ... 

Control Strategies for Multivariable Control Problems If a conventional multiloop control strategy performs poorly due to control loop interactions, a number of solutions are available ... 

Pairing of Controlled and Manipulated Variables A key decision in multiloop-control-system design is the pairing of manipu-... 

The MFC strategy is very different from conventional multiloop control strategies and thus initially unfamiliar to plant personnel. 

The component controllers used in the controller subsystem portion of the DCS can be of various types and include multiloop controllers, programmable logic controllers, personal computer controllers, singleloop controllers, and fieldbus controllers. The type of elec tronic con-troUer utihzed depends on the size and func tional characteristic of the process apphcation being controlled. See the earlier section on distributed control systems. 

Multiloop Controllers The multiloop controller is a DCS network device that uses a single 32-bit microprocessor to provide control functions to many process loops. The controller operates independent of the other devices on the DCS network andTcan support From 20 to 500 loops. Data acquisition capability for up to 1000 analog and discrete I/O channels or more can also be provided by this controller. 

The multiloop controller contains a variety of func tion blocks (for example, PID, totalizer, lead/lag compensator, ratio control, alarm, sequencer, and Boolean) that can be soft-wired together to form complex control strategies. The multiloop controller, as part of a DCS, communicates with other controllers and man/machine interface (MMI) devices also on the DCS network. 

There are many advanced strategies in classical control systems. Only a limited selection of examples is presented in this chapter. We start with cascade control, which is a simple introduction to a multiloop, but essentially SISO, system. We continue with feedforward and ratio control. The idea behind ratio control is simple, and it applies quite well to the furnace problem that we use as an illustration. Finally, we address a multiple-input multiple-output system using a simple blending problem as illustration, and use the problem to look into issues of interaction and decoupling. These techniques build on what we have learned in classical control theories. 

Chapter 16 covers the analysis of multivariable processes stability, robustness, performance. Chapter 17 presents a practical procedure for designing conventional multiloop SISO controllers (the diagonal control structure) and briefly discusses some of the full-blown multivariable controller structures that have been developed in recent years. 

This is the most important equation in multivariable control. It applies for any type of controller, diagonal (multiloop SISO) or full multivariable controller. If any of the roots of this equation are in the right half of the s plane, the system is closedloop unstable. 

The first part of this chapter deals with the conventional diagonal structure multiloop SISO controllers. FuU-blown multivariable controllers are briefly discussed at the end of the chapter. 

Most industrial control systems use the multiloop SISO diagonal control structure. It is the most simple and understandable structure. Operators and plant engineers can use it and modify it when necessary. It does not require an expert in apphed mathematics to design and maintain. In addition, the performance of these diagonal controller structures is usually quite adequate for process control apphcations. In fact there has been little quantitative, unbiased data showing that... 

So the multiloop SISO diagonal controller remains an important structure. It is the base case against which the other structures should be compared. The procedure discussed in this chapter was developed to provide a workable, stable, simple SISO system with only a modest amount of engineering effort. The resulting diagonal controller can then serve as a realistic benchmark, against which the more complex multivariable controller structures can be compared. 

Once the controlled variables have been specified, the control structure depends only on the choice of manipulated variables. For a given process, selecting different manipulated variables will produce different control structure alternatives. These control structures are independent of the controller structure, i.e., pairing of variables in a diagonal multiloop SISO structure or one full-blown multivariable... 

One of the major questions in multivariable control is how to tune controllers in a diagonal multiloop SISO system. If PI controllers are used, there are 2N tuning parameters to be selected. The gains and reset times must be specified so that the overall system is stable and gives acceptable load responses. Once a consistent and rational tuning procedure is available, the pairing problem can be attacked. 

Safonov, M. G., and M. Athans. 1997. Gain and phase margin for multiloop LQG regulators. IEEE Transaction on Control. 22. 

Stand-alone systems. Multiloop controllers) or programmable logic controllers (PLC) typically used to control part of a process, and larger supervisory control and data acquisition (SCADA) systems/distributed control systems (DCS) used to control the process or service as a whole (e.g., bulk primary production plant, building management systems). These self-contained systems are a component of an automated manu-... 

Advantages and Disadvantages of MPC Model predictive control offers a number of important advantages in comparison with conventional multiloop PID control ... 

The optimal robust controller designed with one of the new synthesis techniques is generally not of a form that can be readily implemented. The main benefit of the new synthesis procedure is that it allows the designer to establish performance bounds that can be reached under ideal conditions. In practice, a decentralized (multiloop) control structure is preferred for ease of start-up, bumpless automatic to manual transfer, and fault tolerance in the event of actuator or sensor failures. Indeed, a practical design does not start with controller synthesis but with the selection of the variables that are to be manipulated and measured. It is well known that this choice can have more profound effects on the achievable control performance than the design of the controller itself. This was demonstrated in a distillation example [17, 18] in which a switch from reflux to distillate flow as the manipulated variable removes all robustness problems and makes the controller design trivial. 

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