Types of optimal control problems

In the case of a car journey, this is the equivalent of the driver keeping his foot flat down on the accelerator for the entire journey, except at the terminal point, when he brakes as hard as possible. 

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Different Types of Optimal Control Problems Based on Equation (6.16), the Lagrangian for this problem is... 

Different Types of Optimal Control Problems and the boundary conditions... 

Unlike parameter optimization, the optimal control problem has degrees of freedom that increase linearly with the number of finite elements. Here, for problems with many finite elements, the decomposition strategy for SQP becomes less efficient. As an alternative, we discussed the application of Newton-type algorithms for unconstrained optimal control problems. Through the application of Riccati-like transformations, as well as parallel solvers for banded matrices, these problems can be solved very efficiently. However, the efficient solution of large optimal control problems with... 

Here, a dynamic optimisation (also known as optimal control) problem formulation and solution proposed by Morison (1984) based on Sargent and Sullivan (1979) is presented. The process model can be described by a system of DAEs (model types III, IV and V presented in Chapter 4) as ... 

Here, the last two equations define the flow rate and the mean residence time, respectively. This formulation is an optimal control problem, where the control profiles are q a), f(a), and r(a). The solution to this problem will give us a lower bound on the objective function for the nonisothermal reactor network along with the optimal temperature and mixing profiles. Similar to the isothermal formulation (P3), we discretize (P6) based on orthogonal collocation (Cuthrell and Biegler, 1987) on finite elements, as the differential equations can no longer be solved offline. This type of discretization leads to a reactor network more... 

We have used sensitivity equation methods (Leis and Kramer, 1985) for gradient evaluation as these are simple and efficient for problems with few parameters and constraints. In general, the balance in efficiency between sensitivity and adjoint methods depends on the type of problem being addressed. Adjoint methods are particularly advantageous for optimal control problems in which the inputs are represented as a large number of piecewise constant input values and few interior point constraints exist. Sensitivity methods are preferable for problems with few parameters and many constraints. 

In Section 3.2.1 (p. 59), we had asserted the Lagrange Multiplier Rule that the optimum of the augmented J is equivalent to the constrained optimum of I. This rule is based on the Lagrange Multiplier Theorem, which provides the necessary conditions for the constrained optimum. We will first prove this theorem and then apply it to optimal control problems subject to different types of constraints. 

However, in chemical engineering only a few problems of this type can be found to be fully described in the literature, although it is well-known that many problems in chemical engineering can be formulated as problems of optimal control. The reason for that is that modeling of chemical processes and providing of appropriate data is surely more difficult than in many other areas. On the other hand, new applications may often provide new challenges even for tried and well-established mathematical methods. 

The upper boimd provided by the solution of the open loop optimal control problem may be viewed as the ultimate performance limit, since the inputs to the plant are manipulated directly. However, there is no guarantee that it is achievable via feedback control. Use of a fixed controller type, on the other hand, does not guarantee similar performance (or indeed feasible operation) with the use of a different controller type. Q—parametrization provides an achievable performance bound, but for linear control. These approaches therefore provide different information the key is for users to be aware of this so that appropriate deductions may be drawn from results they generate. The following sections focus on the use of controller parametrization within an optimization framework, both for analysis and design. 

Timmons et al. (2000) and Latour (1979) have discussed opportunities for the application of on-line optimization or supervisory control in refinery operations. Three general types of optimization problems commonly encountered in industrial process operations are discussed next. 

Determination of the optimal temperature (or supersaturation) trajectory for a seeded batch crystallizer is a well studied problem. This is a dynamic optimization or optimal control problem. The process performance is determined by the crystal size distribution and product yield at the final time. For uniformity of shape and size in the crystals in a seeded batch crystallization process, it is essential to ensure that the nucleation phenomena occurs to the minimum and mostly the seeded crystals grow to the desired size at a certain rate. If nucleation occurs in the initial phase, then there is a possibility that the nucleated crystal will compete with the seeded ones, thus if the phenomena is of late growth, then nucleation in the earlier phase is preferred. Thus, depending upon the process operation, many types of objective functions have been proposed [4]. 

The sensitivity to defects and other control parameters can be improved by optimizing the choice of the probe. It appears, after study of different types of probes (ferritic, wild steel, insulator) with different geometries (dish, conical,. ..), necessary to underline that the success of a feasibility research, largely depends on a suitable definition of measure collectors, so that they are adapted to the considered problem. 

Spreadsheet Applications. The types of appHcations handled with spreadsheets are a microcosm of the types of problems and situations handled with fuU-blown appHcation programs that are mn on microcomputers, minis, and mainframes and include engineering computations, process simulation, equipment design and rating, process optimization, reactor kinetics—design, cost estimation, feedback control, data analysis, and unsteady-state simulation (eg, batch distillation optimization). 

To solve the problems of representation and control, we will employ the framework of the branch-and-bound algorithm, which has been used to solve many types of combinatorial optimization problems, in chemical engineering, other domains of engineering, and a broad range of management problems. Specifically, we will use the framework proposed by Ibaraki (1978), which is characterized by the following features ... 

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