Optimized Fuzzy Control with Genetic Algorithms and Differential Evolution for Tracking the Trajectories of an Ankle Prosthesis

This work presents a comparison between three Mandani controllers (trial and error, optimized with Genetic Algorithms (GA), and Differential Evolution (DE)) and a traditional PID controller in the trajectory tracking application in the sagittal/frontal planes of an ankle, considering a disturbance that simulates the existence of an irregularity in the walking surface. The controller rulebase design uses only the error signals and the error derivative. For the implementation of the mentioned controllers, a co-simulation is presented using the MatLAb fuzzy Toolbox, Simulink PID block of Matlab, and Adams View. From the results obtained, a comparison is made to determine the computation time and the position error to choose the best one for the tracking the trajectories of an ankle prosthesis.