Model identification for robot manipulators using regressor-free adaptive control

This paper proposes a regressor-free adaptive feedback-linearization control technique that does not require a model approximation or a regressor matrix. Adaptation in the proposed feedback process is acquired through an update law involving adjustment of less control parameters as compared to existing controllers. Under the given constraints, the closed-loop asymptotic stability of the proposed control law is verified using Lyapunov techniques. The proposed controller is compared with existing adaptive controllers on a two degree-of-freedom robot manipulator. Based on the new adaptive technique, the model parameters of the robotic arm are identified using adequate excitation trajectories. The proposed adaptive technique was validated through simulations and experiments.