A neurofuzzy controller has been used as a nonlinear compensator for a flexible four-link manipulator. Two classes of neurofuzzy models, the Takagi-Sugeno fuzzy model and the rectangular local linear model network have been applied as a feedforward controller to compensate the nonlinearities. The first model incorporates expert-based fuzzy rules into the controller, whereas the second model structure automatically partitions the input space. An adaptation algorithm is developed to train the controller in order to stabilize the whole system. Two control problems have been considered, namely, joint and tip position control schemes. The output signal redefinition strategy is adapted to stabilize the tip position control scheme. A tradeoff between the tracking accuracy and manipulator link vibration can be achieved. Experimental studies have been carried out on a planar flexible four-link manipulator testbed.

In this paper, the problem of designing robust Hinfinity controllers for linear continuous-time systems subjected to time-varying parameter uncertainty and steady-state variance constraints is considered. The goal of this problem is to design the state feedback controller, such that for all admissible time-varying parameter perturbations, the steady-state variance of each state is not more than the individual prespecified upper bound and the Hinfinity norm of the transfer function from disturbance inputs to system outputs meets the prespecified upper bound constraint, simultaneously. The parameter uncertainties are allowed to be time-varying and norm-bounded. A purely algebraic matrix equation approach is effectively utilized to solve the problem addressed. The existence conditions as well as the explicit expression of desired controllers are presented, and two illustrative examples are used to demonstrate the applicability of the proposed design procedure.