Linear adaptive controllers for robust high speed and acceleration motion control for delta robots

This paper introduces a two-layer linear control approach to control highly nonlinear robots for high speed and adaptive tracking. The design of the control approach was based on the combination between the most common linear controllers (PID, Linear Quadratic Tracker, and State Feedback) and the principle of online disturbance estimation and rejection using adaptive Kalman filter algorithm. The inertia of the upper arms and the inertia of the motors were considered as the only known parameters, and the effects of all other nonlinear terms in the dynamic model were represented as a lumped disturbance vector. The paper also provides a comparative study between the proposed controllers and the traditional inverse dynamics nonlinear controllers. The results ensure the superiority of the proposed linear controllers in terms of adaptivity, steady-state error, and computational time. The results were validated experimentally using a lab-made delta robot prototype.