Body schemas are a biologically-inspired approach, emulating the plasticity of the animal brains, allowing efficient representation of non-linear mapping between the body configuration space, i.e. its generalized coordinates and the resulting sensory outputs. This paper describes the development of closed-loop control of spherical parallel mechanism based on self-learning body schemas. More specifically, we demonstrate how a complex parallel spherical manipulator in contact with a surface of irregular geometry can be driven to a configuration of balanced contact forces, i.e. aligned with respect to the irregular surface. The approach uses a pseudo-potential functions and a gradient-based maximum seeking algorithm to drive the manipulator to the desired position. It is demonstrated that a neural-gas type neural network, trained through Hebbian-type learning algorithm can learn a mapping between the manipulator's rotary degrees of freedom and the output contact forces. Numerical and experimental results are presented illustrating the performance of the control scheme. A motivating application of the proposed manipulator and its control algorithm is a hand-held eye tonometer based on tactile force measurements. The resulting controller has been shown to achieve 10 mN of force errors which are adequate for tactile tonometers.