Model predictive control MPC

Off-line analysis, controller design, and optimization are now performed in the area of dynamics. The largest dynamic simulation has been about 100,000 differential algebraic equations (DAEs) for analysis of control systems. Simulations formulated with process models having over 10,000 DAEs are considered frequently. Also, detailed training simulators have models with over 10,000 DAEs. On-line model predictive control (MPC) and nonlinear MPC using first-principle models are seeing a number of industrial applications, particularly in polymeric reactions and processes. At this point, systems with over 100 DAEs have been implemented for on-line dynamic optimization and control. 

When thermodynamics or physics relates secondary measurements to product quality, it is easy to use secondary measurements to infer the effects of process disturbances upon product quality. When such a relation does not exist, however, one needs a solid knowledge of process operation to infer product quality from secondary measurements. This knowledge can be codified as a process model relating secondary to primary measurements. These strategies are within the domain of model-based control Dynamic Matrix Control (DMC), Model Algorithmic Control (MAC), Internal Model Control (IMC), and Model Predictive Control (MPC—perhaps the broadest of model-based control strategies). 

The model-based control strategy that has been most widely applied in the process industries is model predictive control (MPC). It is a general method that is especially well suited for difficult multi-input, multioutput (MIMO) control problems where there are significant interactions between the manipulated inputs and the controlled outputs. Unlike other model-based control strategies, MPC can easily accommodate inequality constraints on input and output variables such as upper and lower limits and rate-of-cnange limits. 

There are several ways to achieve this variable structure control strategy. The elegant approach is to use model predictive control (MPC). The simple approach is to use override control. The latter technique is demonstrated in this section. 

It should be noted that the optimization problems solved for levels 2 and 3 begin to merge as the plantwide optimization begins to set targets for the unit operations in many process units. This large-scale, frequent optimization of operating conditions is known as real-time optimization (RTO). RTOs are run approximately every 30 minutes to 1 hour, with the resulting optimal setpoints downloaded to model predictive controllers (MPC). 

Closed-loop multivariable boiler control has to be planned and performed carefully because plant operators are not traditionally willing to reduce air-fuel ratios due to concerns about CO and other symptoms associated with Oz-deficient combustion. Model predictive control (MPC) is by far the most widely used technique for conducting multivariable boiler optimization and control. Forms of MPC that are inherently multivariable and that include real-time constrained optimization in the design are best suited for boiler application. 

Model predictive control (MPC) was developed in the 1970s and 1980s to meet control challenges of refineries. The advantages of MPC are most evident when it is used as a multivariable controller integrated with an optimizer. The greatest MPC benefits are realized in applications with dead-time dominance, interactions, constraints, and the need for optimization. As opposed to a traditional control loop, where the controller responds to a difference (error) between the set point and measurement, the predictive controller uses a vector difference between the future trajectory of the set point and the predicted trajectory of the controlled variable as its input (Figure 2.52). 

The classical adaptive control scheme is shown in Figure 2.58. Its goal is to use online identification through artificial intelligence (Al), neural networks, and fuzzy logic to adapt the model to the actual process. Al and model predictive control (MPC) can tolerate inaccuracy and uncertainty in the model, and online training can continuously improve the model. 

Plant-wide control is concerned with designing control systems for a large number of individual process units that may be highly interacting. A typical plant-wide control system will consist of many single-loop controllers as well as multi-variable controllers such as Model Predictive Control (MPC),1 10 and may involve thousands of measurements, hundreds to thousands of manipulated variables and hundreds of disturbance variables. Fortunately, a plant with a large number of processing units can be analysed as smaller clusters of units. 

A simple way to circumvent the problem and improve the control is to use predictive control, often called feed forward. The idea is to use a control signal, CCff, which in some way reflects the expected time-course of CC that is needed to keep CO close to CL In technical systems this control is frequently called model predictive control (MPC [12]), because CCff is typically derived from a mathematical model. In biological systems, the feed forward is mostly delivered by nerve signals, more rarely by hormones. 

Model predictive control (MPC) has become widely known as dynamic matrix control (DMC) and model algorithmic control (MAC). A review of the origins of this class of techniques and their theoretical foundations is provided by Garcia et al. [10]. Many complex applications were reported at the recent IFAC Workshop [11]. 

Some people claim that the plantwide control problem has already been solved by the application of several commercial forms of model predictive control (MPC). MPC rests on the idea that wre have a fair amount of knowledge about the dynamic behavior of the process and that this knowledge can be incorporated into the controller itself. The controller uses past information and current measurements to predict... 

The challenges in SMB control are not only the complex nonlinear process dynamics, but also the long delays of the effect of disturbances. The required control strategy has to be able to handle multivariable dynamics with time-delays and hard constraints. Model Predictive Control (MPC) has been proven to be the most effective control strategy for this type of problems [1,2]. Only recently, a few scientific publications have addressed the automatic... 

A sound approach for operational optimization of supply chains is to conceptualize them as dynamic systems and to apply classic knowledge of control theory to operate them (Perea-Lopez et al, 2001 [3]). Among many approaches. Model Predictive Control (MPC) frameworks have been proposed for supply chain operational management (Bose and Pekny, 2000 [4], Perea Lopez et al., 2003 [5], Mestan et al, 2006 [6]). 

In this work an MILP model which achieves the integration of all three Supply Chain (SC) decision levels is developed. Then, the stochastic version of this integrated model is applied as the predictive model in a Model Predictive Control (MPC) framework in order to incorporate and tackle unforeseen events in the SC planning problem in chemical process industries. Afterwards, the validation of the proposed approach is justified and the resulting potential benefits are highlighted through a case study. The results obtained of this particular case study are analyzed and criticized towards future work. 

During the last two decades, model-predictive control (MPC) (Allgower et ah, 1999, Mayne, 2000 and Rao and Rawlings, 2000) has increasingly been applied to the control of processes with interacting dynamics. The basic idea of MPC is to employ a plant model that predicts the reaction of the plant to the past and future inputs, and to optimize a number of future inputs such that the predicted outputs follow the desired trajectory over a certain period of time (called the prediction horizon). This process is iterated, only the next input is applied to the plant, and new inputs are computed for a prediction horizon that is shifted one step into the future, taking new measurements into account. Thus, the behavior of the real process and disturbances are taken into account. 

At the heart of an model predictive control (MPC) application is the optimization of a variable subject to constraints. A typical MPC cost functional is given as follows ... 

This section covers model predictive control (MPC). It describes what it is, how to design it, how to install it, and how to make it work. This work is both fun and useful, but there is one rule Understand the process. This section contains tips and clues about how to analyze and learn process behavior. 

Model predictive control (MPC), the subject of this section, is sometimes referred to as multivariable control or MVC. 

Industrial model predictive control (MPC) is based on algorithms that were developed many years ago. They share several common traits ... 

Al-Haj Ali et al. [5,6] developed different types of linear time invariant models by system identification, which adequately represent the fluidized-bed drying dynamics. MBC techniques such as IMC and model predictive control (MPC) were used for the designing of the control system. Simulations with multivariable MPC strategy... 

---