Adaptive fuzzy design for optimal tracking control of robot manipulators

Optimal output-tracking control of unknown nonlinear motion systems with multivariable structure is designed by solving the linear optimal tracker integrated with an adaptive fuzzy compensator. The adaptive fuzzy compensator performs on-line learning to approximate and compensate the unknown nonlinear dynamics of the system so that minimizing a quadratic performance index can obtain the optimal tracker. The proposed controller and the associated learning algorithm require no preliminary off-line learning for initialization and guarantee the output-tracking error to be uniformly ultimately bounded. Simulation studies on a two-link robot control are demonstrated.