Comparative Study of Fuzzy PID Control Algorithms for Enhanced Position Control in Laparoscopic Surgery Robot
Seung Joon Song,
Duck Hee Lee,
Chi Bum Ahn,
This paper proposes intelligent fuzzy proportional–integral–derivative (PID) controllers for the position control of a master–slave configuration laparoscopic surgery robot system. For the slave robot controller, two fuzzy PID control algorithms are implemented: a rule-based fuzzy control algorithm for online PID gain tuning and a learning fuzzy controller to tune the rules in the rule buffer automatically online. The two fuzzy controllers are tested using sinusoidal reference motions with various periods and the test results are compared with those of a conventional PID controller in terms of position control performance. Various performance indices are used for the comparison, including root mean square error, steady-state error, integral of average error, integral squared error, integral time-weighted absolute error, and integral of time multiplied by the squared error. The evaluation shows that the learning fuzzy controller yields the best performance among the three algorithms. Furthermore, the relationship between the scaling gains and the performance can be deduced to construct a comparative tuning algorithm, which enables the scaling gains to be optimally tuned with less trial and error. Further refinement of the algorithm for enhancing control performance is under way.