GA-Based Adaptive Fuzzy-Neural Control for a Class of MIMO Systems
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
IEEE Transactions on Neural Networks
Neuro-adaptive force/position control with prescribed performance and guaranteed contact maintenance
IEEE Transactions on Neural Networks
A hierarchical structure of observer-based adaptive fuzzy-neural controller for MIMO systems
Fuzzy Sets and Systems
Robust adaptive neural networks with an online learning technique for robot control
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Robust adaptive motion/force tracking control design for uncertain constrained robot manipulators
Automatica (Journal of IFAC)
Interval type 2 hierarchical FNN with the H-infinity condition for MIMO non-affine systems
Applied Soft Computing
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A new robust learning controller for simultaneous position and force control of uncertain constrained manipulators is presented. Using models of the manipulator dynamics and environmental constraint, a task-space reduced-order position dynamics and an algebraic description for the interacting force between the manipulator and its environment are constructed. Based on this treatment, the robust nonlinear H∞ control approach and direct adaptive neural network (NN) technique are then integrated together. The role of NN devices is to adaptively learn those manipulators' structured/unstructured uncertain dynamics as well as the uncertainties with environmental modelling. Then, the effects on tracking performance attributable to the approximation errors of NN devices are attenuated to a prescribed level by the embedded nonlinear H∞ control. Whenever the adopted NN devices have the potential to effectively approximate those nonlinear mappings which are to be learned, then this new control scheme can be ultimately less conservative than its counterpart H∞ only position/force tracking control scheme. This is shown analytically in the form of theorem. Finally, a simulation study for a constrained two-link planar manipulator is given. Simulation results indicate that the proposed adaptive H∞ NN position/force tracking controller performs better in both force and position tracking tasks than its counterpart H∞ only position/force tracking control scheme