# Benchmarking Controllers for Low-Cost Agricultural SCARA Manipulators

## TL;DR

This paper compares different controllers for a low-cost robotic arm used in agriculture, finding each has unique strengths and weaknesses.

## Contribution

A novel self-tuning PI controller with feedforward element is proposed and benchmarked against SMC and RL controllers.

## Key findings

- The Sliding Mode Controller (SMC) achieved the best response time but caused joint movement jitter.
- The Reinforcement Learning (RL) Controller exhibited sudden breaks and overshooting at setpoints.
- The PIFF controller provided smooth tracking but was sensitive to system dynamics changes.

## Abstract

Agriculture needs to produce more with fewer resources to satisfy the world’s demands. Labor shortages, especially during harvest seasons, emphasize the need for agricultural automation. However, the high cost of commercially available robotic manipulators, ranging from EUR 3000 to EUR 500,000, is a significant barrier. This research addresses the challenges posed by low-cost manipulators, such as inaccuracy, limited sensor feedback, and dynamic uncertainties. Three control strategies for a low-cost agricultural SCARA manipulator were developed and benchmarked: a Sliding Mode Controller (SMC), a Reinforcement Learning (RL) Controller, and a novel Proportional-Integral (PI) controller with a self-tuning feedforward element (PIFF). The results show the best response time was obtained using the SMC, but with joint movement jitter. The RL controller showed sudden breaks and overshot upon reaching the setpoint. Finally, the PIFF controller showed the smoothest reference tracking but was more susceptible to changes in system dynamics.