Predictions Based on Evolutionary Algorithms Using Predefined Control Profiles

The general motivation of our work is to meet the main time constraint when implementing a control loop: the Controller's execution time is less than the sampling period. This paper proposes a practical method to diminish the computational complexity of the controllers using predictions based on the Evolutionary Algorithm (EA). It is the case of Model Predictive Control or, more generally, Receding Horizon Control structures. The main drawback of the metaheuristic algorithms (including EAs) working in control structures is their great complexity. Usually, the control variables take values between minimum and maximum technological limits. This work's main idea is to consider the control variables' domain inside a predefined control profile's neighbourhood. The Controller takes into account a smaller domain of the control variables without tracking the predefined control profile or a reference trajectory. The convergence of the EA under consideration is not affected; hence, the same best predictions are found. The predefined control profile is already known or can be determined by solving the optimal control problem without time constraints in open-loop and offline. This work also presents a simulation study applying the proposed technique that involves two benchmark control problems. The results prove that the computational complexity decreases significantly.

Automated summary

This paper proposes a practical method using evolutionary algorithms to reduce the computational complexity of controllers. By constraining the control variables within a predefined control profile's neighborhood, the controller can consider a smaller domain of control variables without tracking the predefined control profile. Simulation results show that the computational complexity decreases significantly.