A Novel Optimal Control Algorithm

Abstract

Optimal control theory is a mathematical tool used for addressing and solving dynamic optimization problems. With the development of computers and the current emphasis of optimal design and operation of large-scale systems, optimal control theory has become increasingly important. The mathematical complexity of the optimal control approach does not allow straightforward analytical solutions to optimal control problems. Thus, algorithms implemented on digital computers have to be used. The main concern of this thesis is to advance and improve the existing knowledge of a dynamic optimal control technique known as Dynamic Integrated System Optimization and Parameter Estimation (DISOPE), so as to make it implementable in the process industry and, on the other hand, as a novel linear and nonlinear optimal control algorithm. The main feature of the technique is that it has been designed so as to achieve the correct optimum solution of the process in spite of inaccuracies in the mathematical model employed in the computations. This method, based on Lagrangian and Hamiltonian is used for approximating some required derivative trajectories. For the development of the algorithm, emphasis is placed on making the techniques implementable in digital computer based industrial process control problems. Applications of DISOPE are proposed in the following areas: linear and nonlinear control system based on adaptive state-space linear dynamic models, and batch process optimization. The algorithm proposed for handling control and state dependent. This algorithm has been implemented using Matlab program with relevant example simulations and the result is final optimal control and state responses.