Abstract: A model-free robust control (MFRC) approach is proposed for position control of robot manipulators in the state space. The control approach is verified analytically to be robust subject to uncertainties including external disturbances, unmodeled dynamics, and parametric uncertainties. There is a high flexibility to work on different systems including actuators by the use of the proposed control approach. The proposed control approach can guarantee the robustness of control system. A PUMA 560 robot driven by geared permanent magnet dc motors is simulated. The simulation results show a satisfactory performance for control system under technical specifications. KeywordsModel-free, robust control, position control, PUMA 560.
Abstract: This paper compares the heuristic Global Search
Techniques; Genetic Algorithm, Particle Swarm Optimization,
Simulated Annealing, Generalized Pattern Search, genetic algorithm
hybridized with Nelder–Mead and Generalized pattern search
technique for tuning of fuzzy PID controller for Puma 560. Since the
actual control is in joint space ,inverse kinematics is used to generate
various joint angles correspoding to desired cartesian space
trajectory. Efficient dynamics and kinematics are modeled on Matlab
which takes very less simulation time. Performances of all the tuning
methods with and without disturbance are compared in terms of ITSE
in joint space and ISE in cartesian space for spiral trajectory tracking.
Genetic Algorithm hybridized with Generalized Pattern Search is
showing best performance.