Multi-objective optimization of a GPI controller for a Solanum tuberosum crop inspection robot using MOPSO and NSGA-II

The intensive production of potatoes (Solanum tuberosum) has negative impacts on human health, the environment, and the economy, mainly due to the excessive and inefficient use of agrochemicals. The implementation of mobile robots for soil inspection represents a promising technological alter-native for optimizing the management of these inputs. However, challenges remain in the modeling, development, and automatic control of this type of agricultural robot. In the field of control, strategies associated with Active Disturbance Rejection, such as Generalized Proportional Integral (GPI) control, offer high robustness and precision in trajectory tracking. However, their performance depends on correct parameter tuning. This paper proposes the multi-objective optimization of a GPI controller implemented in a Solanum tuberosum crop inspection robot. Initially, an optimization problem was formulated to simultaneously minimize tracking error and control effort, using the bio-inspired Multi-Objective Particle Swarm Optimization (MOPSO) algorithm. Subsequently, to contrast the quality of the Pareto front obtained, the evolutionary Non-dominated Genetic Algorithm II (NSGA-II) was implemented, generating an expanded set of 26 non-dominated solutions. The comparative results showed that NSGA-II presents a better distribution of solutions and greater diversity of the Pareto front, while MOPSO offers faster convergence. Together, both approaches allow for more efficient tuning of the GPI controller, improving trajectory tracking by 23 % compared to traditional methods and suggesting the applicability of hybrid optimization strategies in the automation of agricultural robots.