Implementation Aspects Regarding Closed-Loop Control Systems Using Evolutionary Algorithms

When an optimal control problem requires an important computational effort, a metaheuristic algorithm (MA) can be a useful approach. An MA is conceived to solve a specific optimal control problem having a characteristic objective function. This algorithm solely yields only an optimal offline solution. The desideratum to have a closed-loop implementation can be fulfilled through a supplementary tool, the Receding Horizon Control (RHC) structure. This paper addresses a particular case and integrates Evolutionary Algorithms into the RHC structure. The main objective is to propose a general harmonization between the Controller of the closed loop and the Evolutionary Algorithm. Some details concerning the implementation of the closed loop and Controller are described. The impact of the RHC's prediction technique upon the control sequences' encoding is also analyzed. Two general structure Controllers are proposed, one of them conceived to cope with restrictive time constraints. Practical ideas have been illustrated through a case study: the well-known optimal control of a fed-batch reactor for ethanol production. This time, our implementation achieves a closed-loop solution. The results from the programs and simulation series validate the Controllers, EAs, and the closed-loop structure. Generally speaking, the association between RHC and EA can be a realistic solution to optimal process control.

Automated summary

This paper explores the use of metaheuristic algorithms for solving optimal control problems and the implementation of closed-loop control through the Receding Horizon Control structure. Through a case study, the effectiveness of the controllers, evolutionary algorithms, and closed-loop structure is validated. The association between RHC and EA provides a realistic solution for optimal process control.