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This paper proposes an adaptive neural network controller for a 3-DOF robotic manipulator that is subject to backlash-like hysteresis and friction. Two neural networks to approximate the dynamics and the hysteresis nonlinearity. A neural network, which utilizes a radial basis function approximates the robot's dynamics. Another neural network, which employs a hyperbolic tangent activation function, is used to approximate the unknown backlash-like hysteresis. We also consider two cases: full state and output feedback control. For output feedback, where system states are unknown, a high gain observer is employed to estimate the states. The proposed controllers ensure the boundedness of the control signals. Simulations are also performed to show the effectiveness of the controllers.

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