Multiple output process

Empirical Model Identification. In this section we consider linear difference equation models for characterizing both the process dynamics and the stochastic disturbances inherent in the process. We shall discuss how to specify the model structure, how to estimate its parameters, and how to check its adequacy. Although discussion will be limited to single-input, single-output processes, the ideas are directly extendable to multiple-input, multiple-output processes. 

VI.11 The following sets of linear differential equations describe the dynamic behavior of various multiple-input, multiple-output processes. 

Synthesis of Alternative Control Configurations for Multiple-Input, Multiple-Output Processes 461... 

MBC-Tool an environment facilitating the testing of conventional and advanced model based control strategies within Matlab. It includes feed-forward control, cascade control, delay compensation, IMC control as well as decoupling control techniques. It can be used for both single-input single output and multi input multiple output processes and allows the computation of different robustness measures to compare alternative designs. 

MPC-Tool an environment utilized in the designing and testing of Model Predictive Control strategies within Matlab. It allows the implementation and testing of constraint and unconstrained MPC strategies both for single input and multiple output processes. 

MIMO (multiple input, multiple output) process modeling is inherently more complicated than SISO modeling. For linear systems, the Principle of Superposition holds, which allows MIMO models to be developed through a series of single step tests for each input, while holding the other inputs constant. For a process with three inputs (n) and three outputs (y), we can introduce a step change in and record the responses for yi, y2, and 3. The three transfer functions involving u, namely... 

In this section, we have considered only the basic version of the relay auto-tuner. Modifications and extensions are available for nonlinear, open-loop unstable, and multiple-input, multiple-output processes (Yu, 1999 Hang et al., 2002). 

Multiple-input, multiple-output process (2 x 2) Disturbances... 

Multiloop (decentralized) PID control systems are often used to control interacting multiple-input, multiple-output processes because they are easy to understand and require fewer parameters to tune than more general multivariable controllers. Another advantage of multiloop controllers is that loop failure tolerance of the resulting control system can be easily checked. Loop failure tolerance is important in practical apphcations, because some loops may be placed in manual mode, or the manipulated variables of some loops can be saturated at their limits so they cannot be changed to avoid instabihty. 

In this chapter we consider model predictive control (MPC), an important advanced control technique for difficult multivariable control problems. The basic MPC concept can be summarized as follows. Suppose that we wish to control a multiple-input, multiple-output process while satisfying inequality constraints on the input and output variables. If a reasonably accurate dynamic model of the process is available, model and current measurements can be used to predict future values of the outputs. Then the appropriate changes in the input variables can be calculated based on both predictions and measurements. In essence, the changes in the individual input variables are coordinated after considering the input-output relationships represented by the process model. In MPC applications, the output variables are also referred to as controlled variables or CVs, while the input variables are also called manipulated variables or MVs. Measured disturbance variables are... 

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