Design and Implementation of a Biologically Inspired Optimal Control Strategy for Chemical Process Control.

In this paper, a biologically inspired optimal control strategy, denoted as BIO-CS, is proposed for nonlinear chemical processes with constraints. This approach combines the ants' rule of pursuit idea with multiagent and optimal control concepts. In this agent-based framework, starting from an initially feasible trajectory for the leader agent, each follower agent improves its path toward the set point by employing optimal control laws. As the number of agents progresses, the trajectories converge to an optimal solution. The developed algorithm employs gradient-based optimal control solvers for the intermediate problems associated with the leader-follower local interactions. The effectiveness of the developed approach is illustrated by addressing the nonlinear dynamic model of a fermentation process to produce ethanol, whose challenges in process dynamics include oscillations and steady-state multiplicity. The proposed method is successfully implemented for this system considering set point tracking, disturbance rejection, and plant-model mismatch scenarios. From the simulation results, the generated solutions for the agents show error reduction, computed as integrated time absolute error, with respect to the set point as the agents' trajectories converge to the optimal solution. The performance of the proposed approach is also compared to that of a classical proportional-integral controller and a single optimal control solver implementation. This comparison indicates the potential of the proposed BIO-CS algorithm for improved performance, computational time reduction, and faster offset-free set point tracking. [ABSTRACT FROM AUTHOR]