A multi-variable modeling approach for improving operator proficiency

Nowadays there is a strong interest for improving the usability, ergonomics and safety in the assortment of human-assisted equipments and mechatronic gadgets that we all depend on in our modern way of life. This is due to the fact that the overall performance, in any human-machine process, in terms of productivity, energy cost, quality and safety depends both on the skills of the human operator and the machine technical conditions. Hence, for control purposes, and in order to enhance operator proficiency, the human complexity must be taken also into account. An effective strategy for developing new intelligent assisted-machines and human adaptive control schemes can be performed by first modeling the human-machine interface, which often takes place in multi-spatial dimensions. This work describes a simplified multi-variable modeling and control strategy for improving human operator performance on 2-D spatial environments, by combining state-space and frequency analysis identification methods with an optimal control approach.