PD+ controller tuning with the Cuckoo search algorithm for a leaf cutter robot

Plant tissue culture is the technique of manipulating living plant tissues in a sterile culture medium. This process is performed by taking slices of plant tissue from either leaves or axillary buds as basic cuts. However, there is the possibility of contamination of the samples by human error due to lengthy cuts. Robotic manipulators can help to achieve more efficient cutting of the plant tissue. Nevertheless, the tuning of control algorithms and the parameterization of the system is a difficult task. Thus, a good approach can be the use of a global optimization algorithm to auto-tune the designed controller. In this work, the bio-inspired Cuckoo search algorithm is used for tuning the linear system's dynamic model with three degrees of freedom. It should be noted that using trajectory algorithms, it is necessary to know the fully dynamic model. The Cuckoo's algorithm can estimate indirectly nine parameters of the dynamic model and look for a set of optimal values for them until it finds the function value that satisfies a specified goal. We used the performance index with target function and a saturated PD+ controller where the average of the position errors tends to zero in order to obtain convergence of the model parameters.